Skip to main content
NCBI home page
The Journals of Gerontology Series B: Psychological Sciences and Social Sciences logoLink to The Journals of Gerontology Series B: Psychological Sciences and Social Sciences
. 2024 Aug 24;79(12):gbae147. doi: 10.1093/geronb/gbae147

Does Religion Mitigate the Effect of Neighborhood Disorder on Cognitive Decline?: Evidence From a National Longitudinal Study of U.S. Older Adults

Haena Lee 1,✉, Yeon Jin Choi 2, Jong Hyun Jung 3
Editor: Markus Schafer
PMCID: PMC11601166  PMID: 39180185

Abstract

Objectives

This paper examines whether perceived neighborhood disorder is associated with trajectories of cognitive functioning and whether religion mitigates this association among U.S. older adults.

Methods

Data are drawn from the 2006–2016 Health and Retirement Study (N = 12,669). Religious belief and religious attendance are assessed as potential moderators. Growth curve models are used to estimate trajectories of cognitive functioning over time.

Results

We find that perceived neighborhood disorder is associated with lower cognitive functioning at baseline; however, religious belief mitigates the impact of perceived neighborhood disorder on the level of cognitive functioning. For instance, individuals with high religious belief, despite experiencing high perceived neighborhood disorder, show better cognitive functioning at baseline compared to those with high disorder but low belief. While frequent religious attendance is associated with higher cognitive functioning at baseline, it does not moderate the impact of perceived neighborhood disorder on cognitive functioning.

Discussion

This study underscores the protective role of religious belief against cognitive aging in the face of neighborhood disorder, suggesting that personal faith may provide a cognitive reserve or coping mechanism. Our findings also imply that the absence of religious belief, combined with high perceived neighborhood disorder, may produce a compounded negative impact on cognitive aging.

Keywords: Living environments, Longitudinal design/data analysis, Neighborhood perceptions, Religion/spirituality, Stress

Neighborhood environments play a crucial role in the aging process as they can influence social engagement and encourage healthy behaviors, or create stressful conditions and heighten the risk of illness in older adults (Cornwell & Cagney, 2010). While most studies of neighborhood effects on health in later life have focused on objective measures, recent studies indicate that subjective measures of neighborhood quality may be a more proximal predictor of health because individuals even in the same physical areas may experience environment and perceive neighborhood stressors differently. For example, older adults living in neighborhoods characterized by higher perceived physical disorder and poor aesthetic quality tend to experience worse physical health (Lee et al., 2019), greater functional limitations (Caldwell et al., 2017; Clark et al., 2009), and higher mortality risk (Assari, 2017; Rodriguez-Loureiro et al., 2022). These findings persist and are often more strongly related to health outcomes even when objective measures are accounted for, suggesting that individual perceptions of the neighborhood environment can have a profound impact on health beyond what objective measures may capture.

In recent years, researchers have documented that the effects of subjective neighborhood features extend to cognitive health, with perceived neighborhood characteristics being associated with lower cognitive performance (Estrella et al., 2020; Lee & Waite, 2018; Muñoz et al., 2020; Zaheed et al., 2019). However, prior research has predominantly been cross-sectional in nature, which tends to focus on a snapshot of neighborhood effects without taking into account the temporal order between neighborhood exposures and health outcomes. To date, only two studies have examined the association between perceived neighborhood environments and cognitive decline (Hyun et al., 2024; Sharifian et al., 2020), but they measured cognitive trajectories only for a short period in late adulthood or used populations from single states or specific communities that may not be generalizable. Therefore, the current study addresses these limitations by using the 2006–2016 Health and Retirement Study (HRS) to better understand the impact of prolonged exposure to perceived neighborhood disorder on the trajectories of cognitive functioning. By capturing cognitive trajectories over an extended period in a nationally representative sample, our study aims to clarify the directionality of the neighborhood effects, while providing more robust and generalizable insights into how perceived neighborhood problems influence cognitive decline across the later stages of life.

Perceived neighborhood problems may exert a long-term impact on cognitive decline through several important ways. First, perceptions of neighborhood physical disorder, often characterized by the presence of dilapidated buildings, vandalism, and feelings of unsafety, have been described as a chronic stressor that may trigger a physiological response detrimental to brain structure and function, accelerating cognitive deterioration over time (Lupien et al., 2009). For example, chronic exposure to perceived neighborhood disorder can lead to altered and prolonged cortisol levels (Karb et al., 2012), which have been shown to have neurotoxic effects, damaging the hippocampus over time, a critical region for memory and learning (Conrad, 2008; Sudheimer et al., 2014). Second, the constant perception of threat and anxiety due to perceived neighborhood disorder can disrupt attentional processing necessary for completing cognitive tasks (Derakshan & Eysenck, 2009). The argument is that perceived neighborhood disorder may signify a breakdown in informal social control and shared norms at the community level (Sampson & Raudenbush, 1999), elevating a sense of threat, alarm, and vigilance, which can be cognitively taxing and stressful (McEwen, 2012). Evidence suggests that this continuous state of vigilance that accompanies adverse neighborhood conditions can deplete cognitive resources, potentially leading to difficulties in memory retention over time (Muñoz et al., 2024). Third, neighborhood disorder can lead to social withdrawal and reduced participation in cognitively stimulating activities if people living in these environments are less likely to use outdoor space for activities. Reduced social engagement and mental activities due to perceived disorder, threat, and incivility may therefore accelerate cognitive deterioration, which could otherwise serve as sources for cognitive reserve (Clarke et al., 2012).

Identifying modifiable factors that can prevent cognitive decline in older adults is becoming increasingly important to create effective interventions for cognitive aging. Efforts to identify the modifiable factors have focused primarily on the presence of social support and economic resources as a potential stress-buffering mechanism against neighborhood stressors (Aneshensel et al., 2011; Lee, 2021; Wight et al., 2006). To date, no prior studies have examined the moderating role of religiosity in the effects of perceived disorder on cognitive decline although religious involvement has been found to buffer the health consequences of stressful life circumstances (DeAngelis & Ellison, 2018). For example, research has shown that religious attendance (Bradshaw & Ellison, 2010) and religious beliefs such as afterlife beliefs (Ellison, Burdette, et al., 2009) and a sense of divine control (Upenieks & Schieman, 2023; Upenieks et al., 2022) mitigate the harmful effects of stressors (e.g., financial problems) on individual health and well-being.

Incorporating the stress-buffering perspective (Cohen & Wills, 1985), in this study, we consider religiosity as valuable resources that can mitigate the effects of neighborhood stressors on cognitive decline. Given that religiosity is a multidimensional construct (Glock & Stark, 1965), we distinguish between public (e.g., religious attendance) and private (e.g., religious belief and coping) aspects of religiosity. First, because private religiosity refers to divine beliefs and religious coping practices that help individuals deal with stressful conditions (Henderson et al., 2022), it may mitigate the negative association of perceived neighborhood disorder with cognitive functioning. As most religious traditions portray God as caring and supportive, religious persons believe that they can get help from the divine in time of need (Bradshaw et al., 2010). Hence, God may act as a safe haven for believers that provides a sense of felt security in a disordered neighborhood (Kirkpatrick, 2004). Although living in disordered neighborhoods may lead to religious doubts about the benevolence of God (Hill et al., 2024), individuals who perceive God as a supportive higher power may believe that God will protect them from the threat of neighborhood stressors. Thus, these individuals may more actively engage in cognitively stimulating activities in their neighborhoods, which can buffer the negative influences of neighborhood disorder.

In addition, religious coping may promote coherent cognitive schemes to make sense of the world, thereby reducing vulnerability to stressors (Pargament, 1997). By providing a framework for understanding life experiences and events, religious coping helps believers develop a sense of meaning in life. Research demonstrates that a sense of meaning may buffer the pernicious health effects of stressors because it fosters adaptability to life challenges among older adults (Krause, 2007). Given that living in low-quality neighborhoods leads to poor mental health, which in turn interferes with cognitive functioning (Sharifian et al., 2020), the stress-buffering function of a sense of meaning might alleviate stress reactivity and thus mitigate against cognitive decline. Moreover, as religious coping may help believers interpret their stressors in a way that stimulates cognitive faculties, it may protect against the decline of cognitive health over time. Collectively, the theoretical arguments outlined above suggest that private religiosity provides psychological resources that reduce the noxious effects of living in stressful neighborhoods on cognitive health in later life.

Second, religious attendance may also act as a protective factor for older adults who live in neighborhoods perceived as disordered. Religious attendance, often serving as a proxy for organizational involvement, promotes social integration by embedding individuals in a larger social network (Ellison & George, 1994). This in turn may fend off a sense of loneliness—a risk factor for cognitive decline for older adults who live in disordered neighborhoods (Yu et al., 2021). In addition, religious congregations provide valuable contexts for the exchange of social support among attenders, ranging from emotional support to financial aid. This is particularly important because (a) living in adverse neighborhoods tends to compromise social support systems (Krause et al., 2017), and (b) social support predicts a better cognitive functioning in later life by providing greater social stimulation (Arbuckle et al., 1992). Moreover, religious attendance provides opportunities to engage in activities that may stimulate cognitive faculties, including listening to sermons, participating in congregational prayer, having philosophical discussions with fellow church members, and singing hymns. These social activities may help delay the deterioration of cognitive functioning for older adults who live in low-quality neighborhoods.

However, the benefits of religious attendance in disordered neighborhoods may be limited due to the challenges posed by unfavorable neighborhood conditions, and caution should be exercised in presuming this stress-buffering role of religious attendance. The 2017 Baylor Religion Survey indicates that, while many Americans now travel further to their places of worship than in the past, nearly two-thirds still live within 15 min of their place of worship (Dougherty et al., 2020), suggesting that the majority of older worshippers living in disordered neighborhoods may still attend local religious institutions for social interaction and support. However, research by Ulmer and Scheitle (2020) indicates that religious congregations in disadvantaged neighborhoods are more likely to experience property crime, which can disrupt individuals’ routine activities and engagement with the community. This disruption can reduce the ability of these congregations to offer social support and cognitively stimulating interactions, which are crucial for mitigating cognitive decline among older adults. On the other hand, studies have also shown a positive relationship between attending religious organizations and neighborhood satisfaction (Balidemaj, 2020). Higher levels of neighborhood satisfaction among frequent attendees may serve as a protective factor, potentially offsetting some of the negative impacts of neighborhood disorder on cognitive health. Therefore, even if religious congregations in disordered neighborhoods face challenges, the sense of community and satisfaction derived from regular attendance can still provide significant benefits for cognitive health.

Current Study

The main goal of the current analysis is to determine whether perceptions of neighborhood problems, particularly perceived neighborhood disorder, are associated with cognitive trajectories in a nationally representative sample of older adults. Drawing on previous studies suggesting that neighborhood perceptions can influence stress levels, processing efficiency, and engagement in activities, we hypothesized that individuals who live in a neighborhood perceived as disordered will exhibit lower cognitive functioning and faster cognitive decline compared to those who live in more orderly environments (Hypothesis 1).

Moreover, to further identify the potential modifiable factor that can prevent cognitive decline in older adults in the face of perceived neighborhood disorder, we test for interactions between perceived neighborhood disorder and individual religiosity (religious belief and attendance). Anchored in the stress-buffering hypothesis, which suggests that the negative impact of stress can be alleviated by the availability of social support and personal resources, we hypothesized that strong religious belief and regular religious participation will buffer the negative impact of perceived neighborhood disorder on the trajectories of cognitive functioning among older adults (Hypothesis 2).

Method

Sample

Data were drawn from the HRS, a nationally representative, longitudinal survey of approximately 20,000 Americans over the age of 50 and their spouses (Sonnega et al., 2014). The HRS has been biennially collecting a rich array of data on sociodemographic characteristics and health since 1992, with new cohorts added every 6 years. We used the 2006/2008 survey as baseline because it was the first wave of data collection to include respondents’ perception of their neighborhoods and religiosity in the HRS Leave Behind questionnaire (HRS-LB). The HRS-LB data were obtained using a self-administered questionnaire to collect information on participants’ life circumstances, subjective well-being, and lifestyle. A random half-sample of households received the HRS-LB in 2006, and the other half of the sample received it in 2008. The collection of HRS-LB data was repeated every other wave; longitudinal data are available at 4-year intervals. In order to use the pooled sample, we combined the 2006 wave with the 2008 wave as baseline sample (t1) and followed them in 2010/2012 (t2), and 2014/2016 (t3). We linked the HRS with the 2006–2010 American Community Survey (ACS) to get information on tract-level neighborhood socioeconomic status. Restricting the data set to community-dwelling, age-eligible respondents who were included in the HRS-LB, and had at least one cognitive functioning score yielded a final estimation sample of 12,669 (30,455 person-year observations).

Measures

Cognitive function

Cognitive function was assessed based on the three cognitive tests: total word recall, serial 7’s test, and backwards counting. Total word recall was the sum of immediate and delayed 10-noun free word recall measuring memory (0–20 points). A counting backward test was to measure speed of mental processing (0–2 points). A serial sevens subtraction test was to measure working memory (0–5 points), amounting the number of times the respondents correctly subtract seven starting from 100. The final score ranged from 0 (severely impaired) to 27 (highly functioning). The HRS assesses several other cognitive status variables, but they are administered exclusively to participants aged 65 and older (Crimmins et al., 2011). Therefore, in consistent with previous work using the cognitive measures in the HRS (Lee et al., 2021, 2022), we incorporated the three measures that are available to all study participants aged 51 and older. A small percentage of respondents in each wave refused to participate in the cognitive tests and, to reduce sample attrition, we used the imputed cognitive measures released by the HRS in our analysis.

Neighborhood disorder

Perceived neighborhood disorder at baseline was measured using four items in the 2006/2008 HRS-LB assessing how respondents felt about the area within a mile, or 20-min walk of their home (Cagney et al., 2009). Participants used a 7-point scale (1 = least agree to 7 = most strongly agree) to indicate the extent to which they agreed with the following statements: (a) Vandalism and graffiti are a big problem in this area; (b) People would be afraid to walk alone in this area after dark; (c) This area is always full of rubbish and litter; and (d) There are many vacant or deserted houses or storefronts in this area. Responses were averaged using row mean commend (Cronbach’s α = 0.78), with higher values indicating greater perceived neighborhood disorder.

Religiosity

Religiosity included religious belief and religious attendance. Religious belief at baseline was assessed using four items. Respondents used a 6-point scale (1 = strongly disagree to 6 = strongly agree) to indicate the degree to which they agree to the following statements: (a) I believe in a God who watches over me; (b) The events in my life unfold according to a divine or greater plan; (c) I try hard to carry my religious beliefs over into all my other dealings in life; and (d) I find strength and comfort in my religion. Following the literature (Bierman et al., 2018), we created an index averaging answers to the four items (Cronbach’s alpha = 0.92), with higher values indicating higher levels of religious belief. Although the items above span a diverse set of religious constructs, such as sense of divine control, religious coping, and intrinsic religious motivation, they assess one’s commitment to their religious beliefs (Chan et al., 2019). A supplementary analysis also shows that these items load on only one factor with loadings of 0.77 or higher, which confirms that the resulting index has excellent face validity and psychometric properties (see Supplementary Table 1). Religious attendance at baseline was assessed using a question of “About how often have you attended religious services during the past year?” with possible responses ranging from 1 (not at all) to 5 (more than once a week).

Covariates

Baseline sociodemographic covariates included age, gender (male; female), race/ethnicity (non-Hispanic White; non-Hispanic Black, Hispanic), educational attainment (less than high school; high school diploma; college or more), total household wealth (decile; range 0–10), smoking status (not smoking; currently smoking), self-rated health (range: 1 = poor to 5 = excellent), social support (range: 1–4 greater support), social engagement (range: 1 = almost never or yearly to 5 = daily), residential tenure (less than 10 years; 10 years or more), mover status (did not move; moved during the study period), and religious affiliation (none; protestant; catholic; Jewish; other). Baseline neighborhood covariates included neighborhood disadvantage (range: 0–1 greater disadvantage) and urban/rural status (rural; urban). More detailed information on covariates can be found in Supplementary Material.

Analytic Plan

The data analysis was carried out in several steps. First, we estimated sample characteristics in Table 1. Second, we fit a series of growth curve models in Models 1–3 (Table 2). In Model 1, we examined whether perceived neighborhood disorder and religiosity are associated with lower cognitive functioning and faster cognitive decline. We, then, added interaction terms between perceived neighborhood disorder and religious belief in Model 2 and perceived neighborhood disorder and religious attendance in Model 3 to examine whether religiosity moderates the effect of neighborhood perceptions on cognitive trajectories. All models controlled for individual- and neighborhood-level covariates.

Table 1.

Sample Characteristics at Baseline (N = 12,669)

Variables M (SD) %
Cognitive function (raw; range: 0–27) 15.24 (4.32)
Perceived neighborhood disorder (range: 1–7) 2.48 (1.36)
Religious belief (range: 1–6) 5.02 (1.39)
Religious attendance (range: 0 = not at all to 4 = more than once a week) 1.92 (1.46)
Age (in years; range: 52–100) 69.25 (9.55)
Gender
 Male 41.4
 Female 58.6
Race/ethnicity
 Non-Hispanic White 79.8
 Non-Hispanic Black 12.6
 Hispanic 7.6
Education
 Less than high school 20.7
 High school 34.9
 More than high school 44.4
Household wealth (decile; range: 1–10) 6.00 (2.91)
Smoking status
 Not smoking 87.6
 Smoking 12.4
Self-rated health (range: 1 = poor to 5 = excellent) 3.18 (1.09)
Social support (range: 1–4) 2.15 (0.53)
Social engagement (range: 1 = almost never to 5 = daily) 2.90 (1.50)
Residential tenure
 Less than 10 years 49.1
 10 years or more 50.9
Mover status
 Not moved during the study period 75.7
 Moved during the study period 24.3
Type of neighborhood
 Rural 22.9
 Urban 77.1
Neighborhood disadvantage (range: 0–1 greater disadvantage) 0.10 (0.06)
Religious affiliation
 None 6.5
 Protestant 64.5
 Catholic 26.2
 Jewish 2.1
 Other 0.7
Open in a new tab

Notes: M = mean; SD = standard deviation.

Table 2.

Regression Coefficients From Linear Growth Curve Models of Trajectories of Cognitive Function in Later Life (N = 12,669)

Variables Model 1 Model 2 Model 3
Coef. Coef. Coef.
Intercept
Age (centered) −0.168*** −0.135*** −0.165***
Perceived neighborhood disorder −0.118*** −0.285** −0.087*
Religious belief −0.075** −0.151*** −0.077**
Religious attendance 0.057** 0.059** 0.099*
Perceived disorder × Religious belief 0.032*
Perceived disorder × Religious attendance −0.016
Gender (ref: male)
 Female 0.779*** 0.779*** 0.780***
Race/ethnicity (ref: non-Hispanic White)
 Non-Hispanic Black −2.151*** −2.164*** −2.142***
 Hispanic −1.224*** −1.230*** −1.217***
Education (ref: less than high school)
 High school 1.751*** 1.758*** 1.753***
 More than high school 3.008*** 3.005*** 3.010***
Household wealth 0.114*** 0.113*** 0.115***
Smoking status (ref: not smoking)
 Smoking −0.334*** −0.331*** −0.335***
Self-rated health 0.442*** 0.441*** 0.442***
Social support 0.004 0.001 0.003
Social engagement −0.015 −0.015 −0.015
Residential tenure (ref: less than 10 years)
 10 years or more 0.147* 0.148* 0.147*
Mover status (ref: not moved)
 Moved −0.187** −0.187** −0.186**
Type of neighborhood (ref: rural)
 Urban 0.292*** 0.291*** 0.292***
Neighborhood disadvantage −2.584*** −2.595*** −2.594***
Religious affiliation (ref: none)
 Protestant −0.180 −0.168 −0.181
 Catholic −0.072 −0.057 −0.075
 Jewish −0.070 −0.067 −0.066
 Other −0.201 −0.178 −0.200
Slope
Age (centered) −0.005*** −0.005*** −0.005***
Perceived neighborhood disorder −0.000 −0.014 −0.002
Religious belief −0.002 −0.008* −0.002
Religious attendance 0.000 0.000 −0.002
Perceived disorder × Religious belief 0.003
Perceived disorder × Religious attendance 0.001
Gender (ref: male)
 Female −0.001 −0.001 −0.001
Race/Ethnicity (ref: non-Hispanic White)
 Non-Hispanic Black −0.004 −0.005 −0.004
 Hispanic 0.002 0.001 0.001
Education (ref: less than high school)
 High school 0.007 0.007 0.007
 More than high school −0.007 −0.007 −0.007
Household wealth 0.002 0.002 0.002
Smoking status (ref: not smoking)
 Smoking 0.003 0.003 0.003
Self-rated health −0.001 −0.001 −0.001
Social support −0.009 −0.010 −0.010
Social engagement 0.002 0.002 0.002
Residential tenure (ref: less than 10 years)
 10 years or more −0.002 −0.002 −0.002
Mover status (ref: not moved)
 Moved −0.008 −0.008 −0.008
Type of neighborhood (ref: rural)
 Urban 0.008 0.007 0.008
Neighborhood disadvantage 0.080 0.079 0.082
Religious affiliation (ref: none)
 Protestant −0.006 −0.005 −0.006
 Catholic 0.003 0.005 0.003
 Jewish 0.034 0.035 0.034
 Other −0.030 −0.028 −0.030
Open in a new tab

Notes: Coef. = coefficient; ref = reference.

*p < .05. **p < .01. ***p < .001.

We examined trajectories of cognitive function over the 8-year study period using growth curve models. This analytical framework accounts for partially missing (or unbalanced) data using maximum likelihood and performs equally well or better than multiple imputation methods (Curran et al., 2010). The mean number of observations per respondent was 2.4 (maximum = 3). Given the well-established association between age and changes in cognitive trajectories, we used age as the indicator of time (Sliwinski & Mogle, 2008), centered at the age of 71 for analyses. Preliminary analyses showed a nonlinear relationship between age and the outcome, so we added age-squared to all models. All independent variables and covariates were interacted with age to examine variations in the rate of change in cognitive function. Analyses were conducted using mixed in Stata 18. More information on the final model equation can be found in Supplementary Material.

Results

Table 1 describes sample characteristics of key variables at baseline. The mean cognitive function score at baseline was 15.24 (standard deviation [SD] = 4.32) on a 27-point scale. The mean score for perceived neighborhood disorder was 2.48 (SD = 1.36; range: 1–7). The mean religious belief was 5.02 (SD = 1.39; range: 1–6) and the mean religious attendance was 1.92 (SD = 1.46; range: 0–4), which means that, on average, respondents reported that they attended religious services two or three times a month.

Table 2 presents coefficients from growth curve models. The coefficients listed under the intercept are derived from the main effects of key variables indicating initial status (baseline differences), while the coefficients under the slope come from their interactions with time, which denote rate of change. Consistent with previous studies, the negative linear (b = −0.168, p < .001) and curvilinear (b = −0.005, p < .001) effects of age found in Model 1 indicate that cognitive function declines with advancing age and this decline accelerates as individuals grow older. In terms of perceived neighborhood disorder, Model 1 showed that greater perceived neighborhood disorder was associated with lower cognitive function at baseline (b = −0.118, p < .001). Higher religious belief was associated with negatively associated with cognitive function at baseline (b = −0.075, p < .01), while frequent religious attendance was associated with better cognitive function at baseline (b = 0.057, p < .01). However, we did not find perceived disorder, religious belief, and religious attendance to be significantly associated with rate of decline.

In Model 2, when the interaction term between perceived neighborhood disorder and religious belief was introduced, we found that religious belief mitigated the negative impact of neighborhood disorder on baseline cognition (b = 0.032, p < .05). That is, high levels of religious belief are associated with better cognitive function at baseline, particularly in the context of high neighborhood disorder. However, we did not find the interaction term between disorder, belief, and time to be statistically significant, suggesting that the combined effect of disorder and belief does not significantly affect the rate of cognitive decline. In contrast, in Model 3, no protective effect was observed in the level of cognitive functioning and rate of decline among those who frequently attend religious services, as their interaction with neighborhood disorder was not statistically significant.

In Figure 1, we illustrated the predicted trajectories of cognitive function by neighborhood disorder and religious belief based on the results from Model 2. To ease the interpretation, we grouped neighborhood disorder and religious belief into four categories: low disorder and low belief, low disorder and high belief, high disorder and low belief, and high disorder and high belief. These categorizations were defined using the means plus or minus 1.5 SDs for each variable. As presented in Figure 1, individuals with high neighborhood disorder who also have a high level of religious belief (solid black line) exhibit better cognitive function at baseline compared to those with high disorder and low belief, but they did not necessarily have a slower rate of cognitive decline over time. In contrast, those who experience low neighborhood disorder and possess high religious belief start off with a higher cognitive function, showing the benefits of a supportive environment and personal beliefs.

Figure 1.

Alt Text: This graph is based on growth curve models, illustrating the buffering role of religious belief in the association between neighborhood disorder and cognitive functioning in later life.

Open in a new tab

Predicted probability of cognitive functioning by neighborhood disorder and religious belief, Health and Retirement Study, 2006/2008–2014/2016.

Sensitivity Analysis

We conducted a series of sensitivity analyses to test robustness of our results. First, although we focused on baseline measurements, to further address concerns of residential movements and religious transitions over time, we conducted additional analyses that adjusted for changes in neighborhood environment and religiosity. Accounting for whether respondents had ever moved to worse neighborhoods in the past 5 years (see Supplementary Table 2) did not change our findings reported in this study, although the significance levels of a few effects declined due to the reduced number of respondents. In another set of additional analyses, we included religious transition variables in our model to address concerns about shifts in religiosity over time (see Supplementary Table 3). Results suggested that respondents who maintained high levels of religious belief experienced slower cognitive decline, while those who consistently attended religious services frequently had higher cognitive function at baseline than those who maintained low belief and infrequent attendance. However, none of the interaction terms between neighborhood disorder and religious transitions were statistically significant. This may be because of the reduced number of respondents (about one third of our data have one time point only). Therefore, we interpret these findings as preliminary results that need further confirmation from other data sets with longer records of religious transitions. Moreover, we conducted additional analyses to investigate whether the protective effect of religious belief differs by residential tenure (see Supplementary Table 4); and the three-way interaction term between disorder, belief, and residential tenure showed no differences in the buffering effect of belief on baseline cognition between people who lived in their neighborhood for more than 10 years and those who did not. Lastly, to address concerns about whether subjective measures matter more than objective measures, we included objective neighborhood disorder at baseline in our model (see Supplementary Table 5). Our results suggest that both objective and subjective disorder independently predict baseline cognition. However, when introducing both types in the same model, we found that the magnitude of the coefficient and p value of objective disorder reduced, while those of subjective disorder remained largely consistent. While these results suggest that subjective disorder might be a more proximal predictor than objective measures, it is important to approach this conclusion with caution as both disorder measures remain statistically significant. Distinguishing the differential impacts of objective and subjective neighborhood measures may go beyond the scope of the present paper, however, future research may benefit from further exploring this topic as objective neighborhood context and neighborhood perceptions are linked yet distinct constructs that may exert unique influences on cognitive health.

Discussion

The purpose of this study was to explore the association between perceived neighborhood disorder and cognitive trajectories, specifically examining the potential moderating effects of religious belief and religious attendance. Our findings highlight three key points. First, individuals residing in neighborhoods perceived as disordered had lower cognitive functioning at baseline. Second, religious belief appears to provide some resilience, mitigating the impact of perceived neighborhood disorder on baseline cognition. For example, individuals with high religious belief, despite experiencing high perceived disorder, demonstrated better cognitive functioning at baseline compared to those with high disorder but low belief. Third, while religious attendance was associated with higher cognitive functioning at baseline, it did not moderate the effects of perceived neighborhood disorder on cognitive functioning.

We demonstrated that religious belief may provide greater protection against poor cognition at baseline for those living in disordered neighborhoods. These observations align with previous studies, suggesting that religiosity is an important personal resource that may cushion the deleterious effects of neighborhood stressors on cognitive function in later life (Krause, 2011). It is possible that religious belief, such as faith and spirituality, may offer a sense of comfort, hope, and coping mechanism in the face of adversity (Jung & Lee, 2022; Pargament, 1997). It may also provide a source of solace, meaning, and resilience that can help individuals navigate stressful situations, including concerns about neighborhood safety and disorder (Krause et al., 2017). Given that previous scholarship tends to focus on the direct, positive effect of religiosity on cognitive function in later life (Henderson et al., 2022), we move this scholarship forward by underscoring the moderating role of religiosity in the association between neighborhood and cognition.

To the best of our knowledge, this study is the first that observed the buffering role of religious belief in mitigating the negative effect of living in disordered neighborhoods on cognitive trajectories among older adults. Our findings suggest that high levels of religious belief are associated with better cognitive functioning at baseline in the context of high neighborhood disorder. There are several lines of explanations for these protective effects of religious belief. Individuals with high levels of religious belief may be inclined to integrate religious teachings into their own lives (Schieman, 2011). In these processes, people attempt to grasp religious doctrines and figure out how to carry their religious convictions over into their dealings in life. This in turn may strengthen cognitive abilities, which may mitigate potential deleterious influences of neighborhood problems on cognitive aging. Moreover, individuals who have high levels of religious belief tend to cultivate a personal relationship with God. This intimate relationship with God may enhance feelings of significance (Jung, 2015), an important psychological resource to cope with stressful conditions such as perceived neighborhood disorder. In addition, as they believe that God is actively involved in their lives, they may attribute good and bad outcomes in life to God (Wilt et al., 2024). Attributions to God, especially for negative events such as living in disordered neighborhoods, may help people better explain those events, and thus regain a sense of coherence (Lewis Hall & Hill, 2019). Although neighborhood disorder may hamper cognitive functioning, making sense of the world in ways that evoke supernatural interaction may stimulate cognitive faculties, which can protect against the decline of cognitive health over time.

However, our analyses show that religious attendance does not act as a buffer vis-à-vis neighborhood disorder. One potential explanation for this pattern is that repeated religious activities in exclusive religious communities may discourage reflective processing while promoting automatic processing. Older adults often attend the same church as they tend to live in the same geographical area for a long time. In fact, a recent study has found that older adults who attended religious services for an extended period of time exhibited poor cognitive health due to their segregated and ritualized religious lifestyles (Hill et al., 2020). As such, religious attendance may not be effective in stimulating cognitive function in later life if it becomes a routine activity without stimulations. In addition, not all individuals experience their relationships with other fellow members in a satisfying and positive way in religious congregations. Religious congregations can be fraught with disagreements over various issues including theological differences, debates on political issues such as attitudes toward homosexuality, and administrative matters like the use of facilities (Ellison & Lee, 2010). Moreover, when believers do not follow the norm of religious institutions, they may be the target of criticism and gossip. This negative side of religion has been found to be associated with higher psychological distress (Ellison et al., 2009) and poor health (Nguyen, 2020). Broadly, our findings align with prior evidence suggesting that religious attendance may not always be favorable for health including cognitive functioning (Ellison et al., 2023; Hill et al., 2020; Upenieks & Zhu, 2024).

Viewed in this way, our study may have important implications for research on the religious–health connection. That is, a religious variable may have mixed consequences for health in later life. Specifically, our study reveals that religious belief modifies the noxious effects of neighborhood stressors on cognitive health, while religious attendance had a null effect in moderating the neighborhood effects on cognitive decline. A handful of previous studies echo these findings. For example, an analysis of 921 adults in the United States showed that religious belief offsets the harmful effects of financial hardship on anxiety, while religious attendance does not (Ellison, Burdette, et al., 2009). Similarly, a study of 2,376 Canadian workers revealed that divine beliefs buffer the noxious mental health consequences of perceived underpayment among men, but religious attendance fails to serve as a buffer (Upenieks & Schieman, 2023). Our study adds to this literature, showing that the stress-moderating effects of religiosity vis-à-vis perceived neighborhood disorder may also depend on the religious variables included in the analysis. In this regard, the results in the study highlight the multidimensional nature of religiosity (Glock & Stark, 1965), emphasizing the importance of taking various aspects of religiosity into account in research on religion and health.

Although our study contributes to a better understanding of the relationship between neighborhoods, religion, and cognition in older adults, it also has several limitations. First, although religiosity is a multidimensional construct (Glock & Stark, 1965), this study evaluates only two dimensions of religiosity due to available data limitations. Other indicators of religiosity including prayer, religious/spiritual struggles, and images of God may modify the link between neighborhood problems and cognitive function among older adults. In particular, attachment to God—a set of divine beliefs that perceives God to be the “ultimate attachment figure” who is caring, loving, and supportive (Kirkpatrick, 2004)—is a promising candidate because it is found to be a buffer against the detrimental effects of stressful conditions on health (Ellison et al., 2012). As older adults experience a loss of social connections, they tend to turn to God as a substitute attachment figure (Cicirelli, 2004). Hence, having a secure attachment to God may enable older adults to build a close, intimate relationship with God, which in turn may serve as a cognitively stimulating resource. Therefore, it is plausible that attachment to God may protect against the decline of cognitive function among older adults who live in stressful, low-quality neighborhoods. Future research could explore whether attachment to God and potentially other dimensions of religious involvement might counteract the adverse effects of neighborhood stressors on cognitive function in later life. Second, future research may benefit from extending the current study by examining subgroup variations in the moderating effects of religious involvement. Theory and research suggest that religion is a particularly valuable resource for those from disadvantaged backgrounds regarding socioeconomic status, gender, and race/ethnicity (Glock & Stark, 1965; Henderson et al., 2022). While our supplementary analyses show that the buffering effect of religiosity does not differ by education (see Supplementary Table 6), future investigations may expand upon these findings by assessing whether the stress-buffering patterns observed in the study differ across other social statuses such as gender and race (e.g., Bierman, 2006; Upenieks & Schieman, 2023). This effort might enhance our sociological knowledge of the ways that the effectiveness of religion as a coping resource is conditional upon one’s place in the stratification system. Lastly, we acknowledge that our analysis utilized baseline measurements for neighborhood disorder and did not explicitly test for the effects of residential mobility, although we controlled for mover status. Future research incorporating longitudinal data tracking neighborhood changes will be crucial in distinguishing the dynamic nature of neighborhood influences and their impacts on cognitive aging processes.

Conclusion

Research has consistently shown that older adults, who are more likely to stay in the same residential areas as they age, are particularly vulnerable to environmental stressors because of their extended exposure to hazardous environmental elements compared to younger adults, and because of the changes in biological and psychological functioning associated with normal aging, which may diminish the ability of older adult to effectively deal with stress (Glass & Balfour, 2003). Our findings contribute to the sparse literature by identifying religious belief as an important yet overlooked source of stress buffer for later life cognitive function in the face of perceived neighborhood disorder. We posit that religious belief, such as personal faith, spirituality, and internalized values, can serve as a source of resilience that helps mitigate the negative impact of perceived neighborhood disorder on cognitive functioning. By further investigating the underlying processes through which religious belief provides greater protection against cognitive functioning, future research can provide a better understanding of how we might use religiosity to promote cognitive aging among socially vulnerable groups living in disordered neighborhoods.

Supplementary Material

gbae147_suppl_Supplementary_Materials
gbae147_suppl_supplementary_materials.docx (54.8KB, docx)

Acknowledgments

The Health and Retirement Study is sponsored by National Institute on Aging (U01AG009740) and conducted by the University of Michigan, USA. An earlier version of this paper was presented at the 2022 Gerontological Society of America.

Contributor Information

Haena Lee, Department of Sociology, Sungkyunkwan University, Seoul, South Korea.

Yeon Jin Choi, College of Social Work, University of Kentucky, Lexington, Kentucky, USA.

Jong Hyun Jung, Department of Sociology, Sungkyunkwan University, Seoul, South Korea.

Funding

This work was supported by pilot funding from the National Institute on Aging (NIA; 5R24AG045061-08). H. Lee was supported by an NIA K99 Pathway to Independence Award (K99AG071834).

Conflict of interest

None.

Data Availability

The analytic data sets for this study incorporate multiple restricted data sources from the Health and Retirement Study (HRS). Due to privacy and confidentiality requirements, access to these data sets is regulated and requires a formal application process. Researchers interested in utilizing these data sets for their research must apply through the HRS website at https://hrs.isr.umich.edu.

Ethics Statement

The study was approved by the Institutional Review Board at Sungkyunkwan University, South Korea (SKKU 2023-10-035-001).

References

  1. Aneshensel, C. S., Ko, M. J., Chodosh, J., & Wight, R. G. (2011). The urban neighborhood and cognitive functioning in late middle age. Journal of Health and Social Behavior, 52(2), 163–179. https://doi.org/ 10.1177/0022146510393974 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Arbuckle, T. Y., Gold, D. P., Andres, D., Schwartzman, A., & Chaikelson, J. (1992). The role of psychosocial context, age, and intelligence in memory performance of older men. Psychology and Aging, 7(1), 25–36. https://doi.org/ 10.1037//0882-7974.7.1.25 [DOI] [PubMed] [Google Scholar]
  3. Assari, S. (2017). Social determinants of depression: The intersections of race, gender, and socioeconomic status. Brain Sciences, 7(12), 156. https://doi.org/ 10.3390/brainsci7120156 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Balidemaj, A. (2020). Attendance of religious organizations as a predictor of neighborhood satisfaction. Journal of Social Service Research, 46(3), 416–426. https://doi.org/ 10.1080/01488376.2019.1582450 [DOI] [Google Scholar]
  5. Bierman, A. (2006). Does religion buffer the effects of discrimination on mental health? Differing effects by race. Journal for the Scientific Study of Religion, 45(4), 551–565. https://doi.org/ 10.1111/j.1468-5906.2006.00327.x [DOI] [Google Scholar]
  6. Bierman, A., Lee, Y., & Schieman, S. (2018). Chronic discrimination and sleep problems in late life: Religious involvement as buffer. Research on Aging, 40(10), 933–955. https://doi.org/ 10.1177/0164027518766422 [DOI] [PubMed] [Google Scholar]
  7. Bradshaw, M., & Ellison, C. G. (2010). Financial hardship and psychological distress: Exploring the buffering effects of religion. Social Science & Medicine, 71(1), 196–204. https://doi.org/ 10.1016/j.socscimed.2010.03.015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bradshaw, M., Ellison, C. G., & Marcum, J. P. (2010). Attachment to God, images of God, and psychological distress in a nationwide sample of Presbyterians. The International Journal for the Psychology of Religion, 20(2), 130–147. https://doi.org/ 10.1080/10508611003608049 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Cagney, K. A., Glass, T. A., Skarupski, K. A., Barnes, L. L., Schwartz, B. S., & Mendes de Leon, C. F. (2009). Neighborhood-level cohesion and disorder: Measurement and validation in two older adult urban populations. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 64(3), 415–424. https://doi.org/ 10.1093/geronb/gbn041 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Caldwell, J. T., Lee, H., & Cagney, K. A. (2017). Disablement in context: Neighborhood characteristics and their association with frailty onset among older adults. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 74(7), e40–e49. https://doi.org/ 10.1093/geronb/gbx123 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Chan, T., Michalak, N. M., & Ybarra, O. (2019). When God is your only friend: Religious beliefs compensate for purpose in life in the socially disconnected. Journal of Personality, 87(3), 455–471. https://doi.org/ 10.1111/jopy.12401 [DOI] [PubMed] [Google Scholar]
  12. Cicirelli, V. (2004). God as the ultimate attachment figure for older adults. Attachment & Human Development, 6(4), 371–388. https://doi.org/ 10.1080/1461673042000303091 [DOI] [PubMed] [Google Scholar]
  13. Clark, C. R., Kawachi, I., Ryan, L., Ertel, K., Fay, M. E., & Berkman, L. F. (2009). Perceived neighborhood safety and incident mobility disability among elders: The hazards of poverty. BMC Public Health, 9(1), 162. https://doi.org/ 10.1186/1471-2458-9-162 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Clarke, P. J., Ailshire, J. A., House, J. S., Morenoff, J. D., King, K., Melendez, R., & Langa, K. M. (2012). Cognitive function in the community setting: The neighbourhood as a source of ‘cognitive reserve’? Journal of Epidemiology and Community Health, 66(8), 730–736. https://doi.org/ 10.1136/jech.2010.128116 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Cohen, S., & Wills, T. A. (1985). Stress, social support, and the buffering hypothesis. Psychological Bulletin, 98(2), 310–357. https://doi.org/ 10.1037/0033-2909.98.2.310 [DOI] [PubMed] [Google Scholar]
  16. Conrad, C. D. (2008). Chronic stress-induced hippocampal vulnerability: The glucocorticoid vulnerability hypothesis. Reviews in the Neurosciences, 19(6), 395–411. https://doi.org/ 10.1515/revneuro.2008.19.6.395. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Cornwell, E. Y., & Cagney, K. A. (2010). Neighborhoods and health in later life: The intersection of biology and community. Annual Review of Gerontology and Geriatrics, 30(1), 323–348. https://doi.org/ 10.1891/0198-8794.30.323 [DOI] [Google Scholar]
  18. Crimmins, E. M., Kim, J. K., Langa, K. M., & Weir, D. R. (2011). Assessment of cognition using surveys and neuropsychological assessment: The Health and Retirement Study and the aging, demographics, and memory study. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 66(Suppl._1), i162–i171. https://doi.org/ 10.1093/geronb/gbr048 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Curran, P. J., Obeidat, K., & Losardo, D. (2010). Twelve frequently asked questions about growth curve modeling. Journal of Cognition and Development, 11(2), 121–136. https://doi.org/ 10.1080/15248371003699969 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. DeAngelis, R., & Ellison, C. (2018). Aspiration strain and mental health: The education-contingent role of religion. Journal for the Scientific Study of Religion, 57(2), 341– 364. https://doi.org/ 10.1111/jssr.12520 [DOI] [Google Scholar]
  21. Derakshan, N., & Eysenck, M. W. (2009). Anxiety, processing efficiency, and cognitive performance: New developments from attentional control theory. European Psychologist, 14(2), 168–176. https://doi.org/ 10.1027/1016-9040.14.2.168 [DOI] [Google Scholar]
  22. Dougherty, G. B., Golden, S. H., Gross, A. L., Colantuoni, E., & Dean, L. T. (2020). Measuring structural racism and its association with BMI. American Journal of Preventive Medicine, 59(4), 530–537. https://doi.org/ 10.1016/j.amepre.2020.05.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Ellison, C. G., Bradshaw, M., Kuyel, N., & Marcum, J. P. (2012). Attachment to God, stressful life events, and changes in psychological distress. Review of Religious Research, 53(4), 493–511. https://doi.org/ 10.1007/s13644-011-0023-4 [DOI] [Google Scholar]
  24. Ellison, C. G., Burdette, A. M., & Hill, T. D. (2009). Blessed assurance: Religion, anxiety, and tranquility among US adults. Social Science Research, 38(3), 656–667. https://doi.org/ 10.1016/j.ssresearch.2009.02.002 [DOI] [PubMed] [Google Scholar]
  25. Ellison, C. G., & George, L. K. (1994). Religious involvement, social ties, and social support in a southeastern community. Journal for the Scientific Study of Religion, 33(1), 46–61. https://doi.org/ 10.2307/1386636 [DOI] [Google Scholar]
  26. Ellison, C. G., Guven, M., DeAngelis, R., & Hill, T. (2023). Perceived neighborhood disorder, self-esteem, and the moderating role of religion. Review of Religious Research, 65(3), 317– 343. https://doi.org/ 10.1177/0034673X231208098 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Ellison, C. G., & Lee, J. (2010). Spiritual struggles and psychological distress: Is there a dark side of religion? Social Indicators Research, 98(3), 501–517. https://doi.org/ 10.1007/s11205-009-9553-3 [DOI] [Google Scholar]
  28. Ellison, C. G., Zhang, W., Krause, N., & Marcum, J. P. (2009). Does negative interaction in the church increase psychological distress? Longitudinal findings from the Presbyterian Panel Survey*. Sociology of Religion, 70(4), 409–431. https://doi.org/ 10.1093/socrel/srp062 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Estrella, M. L., Durazo-Arvizu, R. A., Gallo, L. C., Isasi, C. R., Perreira, K. M., Vu, T.-H. T., Vasquez, E., Sachdeva, S., Zeng, D., Llabre, M. M., Tarraf, W., González, H. M., Daviglus, M. L., & Lamar, M. (2020). Associations between perceived neighborhood environment and cognitive function among middle-aged and older women and men: Hispanic Community Health Study/Study of Latinos Sociocultural Ancillary Study. Social Psychiatry and Psychiatric Epidemiology, 55(6), 685–696. https://doi.org/ 10.1007/s00127-019-01829-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Glass, T. A., & Balfour, J. L. (2003). Neighborhoods, aging, and functional limitations. In Kawachi I. & Berkman L. F. (Eds.), Neighborhoods and health (pp. 303–334). Oxford University Press. [Google Scholar]
  31. Glock, C. Y., & Stark, R. (1965). Religion and Society in Tension. Rand McNally. [Google Scholar]
  32. Henderson, A. K., Walsemann, K. M., & Ailshire, J. A. (2022). Religious involvement and cognitive functioning at the intersection of race–ethnicity and gender among midlife and older adults. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 77(1), 237–248. https://doi.org/ 10.1093/geronb/gbab034 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Hill, T. D., Carr, D. C., Burdette, A. M., & Dowd-Arrow, B. (2020). Life-course religious attendance and cognitive functioning in later life. Research on Aging, 42(7–8), 217–225. https://doi.org/ 10.1177/0164027520917059 [DOI] [PubMed] [Google Scholar]
  34. Hill, T. D., Upenieks, L., Wolf, J. K., Cossman, L., & Ellison, C. G. (2024). Do religious struggles mediate the association between neighborhood disorder and health in the United States? Journal of Religion and Health, 63(1), 202–223. https://doi.org/ 10.1007/s10943-023-01780-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Hyun, J., Lovasi, G. S., Katz, M. J., Derby, C. A., Lipton, R. B., & Sliwinski, M. J. (2024). Perceived but not objective measures of neighborhood safety and food environments are associated with longitudinal changes in processing speed among urban older adults. BMC Geriatrics, 24(1), 551. https://doi.org/ 10.1186/s12877-024-05068-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Jung, J. H. (2015). Sense of divine involvement and sense of meaning in life: Religious tradition as a contingency. Journal for the Scientific Study of Religion, 54(1), 119–133. https://doi.org/ 10.1111/jssr.12170 [DOI] [Google Scholar]
  37. Jung, J. H., & Lee, H. J. (2022). Death of a child, religion, and mental health in later life. Aging & Mental Health, 26(3), 623–631. https://doi.org/ 10.1080/13607863.2021.1889968 [DOI] [PubMed] [Google Scholar]
  38. Karb, R. A., Elliott, M. R., Dowd, J. B., & Morenoff, J. D. (2012). Neighborhood-level stressors, social support, and diurnal patterns of cortisol: The Chicago Community Adult Health Study. Social Science & Medicine, 75(6), 1038–1047. https://doi.org/ 10.1016/j.socscimed.2012.03.031 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Kirkpatrick, L. A. (2004). Attachment, evolution, and the psychology of religion. Guilford Press. [Google Scholar]
  40. Krause, N. (2007). Evaluating the stress-buffering function of meaning in life among older people. Journal of Aging and Health, 19(5), 792–812. https://doi.org/ 10.1177/0898264307304390 [DOI] [PubMed] [Google Scholar]
  41. Krause, N. (2011). The perceived prayers of others, stress, and change in depressive symptoms over time. Review of Religious Research, 53(3), 341–356. https://doi.org/ 10.1007/s13644-011-0016-3 [DOI] [Google Scholar]
  42. Krause, N., Ironson, G., Pargament, K., & Hill, P. (2017). Neighborhood conditions, religious coping, and uncontrolled hypertension. Social Science Research, 62, 161–174. https://doi.org/ 10.1016/j.ssresearch.2016.08.004 [DOI] [PubMed] [Google Scholar]
  43. Lee, H. (2021). Disorder, networks, and cognition: Do social networks buffer the influence of neighborhood and household disorder on cognitive functioning? Aging & Mental Health, 26(5), 1010–1018. https://doi.org/ 10.1080/13607863.2021.1922600 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Lee, H., Cagney, K. A., & Hawkley, L. (2019). Crime, perceived danger, and adiposity: The role of gender. Journal of Aging and Health, 31(9), 1715–1736. https://doi.org/ 10.1177/0898264318787370 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Lee, H., Lee, M. W., Warren, J. R., & Ferrie, J. (2022). Childhood lead exposure is associated with lower cognitive functioning at older ages. Science Advances, 8(45), eabn5164. https://doi.org/ 10.1126/sciadv.abn5164 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Lee, H., Ryan, L. H., Ofstedal, M. B., & Smith, J. (2021). Multigenerational households during childhood and trajectories of cognitive functioning among U.S. older adults. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 76(6), 1161–1172. https://doi.org/ 10.1093/geronb/gbaa165 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Lee, H., & Waite, L. J. (2018). Cognition in context: The role of objective and subjective measures of neighborhood and household in cognitive functioning in later life. Gerontologist, 58(1), 159–169. https://doi.org/ 10.1093/geront/gnx050 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Lewis Hall, M. E., & Hill, P. (2019). Meaning-making, suffering, and religion: A worldview conception. Mental Health, Religion & Culture, 22(5), 467–479. https://doi.org/ 10.1080/13674676.2019.1625037 [DOI] [Google Scholar]
  49. Lupien, S. J., McEwen, B. S., Gunnar, M. R., & Heim, C. (2009). Effects of stress throughout the lifespan on the brain, behaviour and cognition. Nature Reviews Neuroscience, 10(6), 434–445. https://doi.org/ 10.1038/nrn2639 [DOI] [PubMed] [Google Scholar]
  50. McEwen, B. S. (2012). Brain on stress: How the social environment gets under the skin. Proceedings of the National Academy of Sciences of the United States of America, 109(Supplement_2), 17180–17185. https://doi.org/ 10.1073/pnas.1121254109 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Muñoz, E., Hyun, J., Diaz, J. A., Scott, S. B., & Sliwinski, M. J. (2024). Exposure to neighborhood violence, and laboratory-based and ambulatory cognitive task performance in adulthood. Social Science & Medicine, 348, 116807. https://doi.org/ 10.1016/j.socscimed.2024.116807 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Muñoz, E., Scott, S. B., Corley, R., Wadsworth, S. J., Sliwinski, M. J., & Reynolds, C. A. (2020). The role of neighborhood stressors on cognitive function: A coordinated analysis. Health & Place, 66, 102442. https://doi.org/ 10.1016/j.healthplace.2020.102442 [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Nguyen, A. W. (2020). Religion and mental health in racial and ethnic minority populations: A review of the literature. Innovation in Aging, 4(5), igaa035. https://doi.org/ 10.1093/geroni/igaa035 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Pargament, K. I. (1997). The psychology of religion and coping: Theory, research, practice. Guilford Press. [Google Scholar]
  55. Rodriguez-Loureiro, L., Casas, L., Bauwelinck, M., Lefebvre, W., Vanpoucke, C., & Gadeyne, S. (2022). Long-term exposure to objective and perceived residential greenness and diabetes mortality: A census-based cohort study. Science of the Total Environment, 821, 153445. https://doi.org/ 10.1016/j.scitotenv.2022.153445 [DOI] [PubMed] [Google Scholar]
  56. Sampson, R. J., & Raudenbush, S. W. (1999). Systematic social observation of public spaces: A new look at disorder in urban neighborhoods. American Journal of Sociology, 105(3), 603–651. https://doi.org/ 10.1086/210356 [DOI] [Google Scholar]
  57. Schieman, S. (2011). Education and the importance of religion in decision making: Do other dimensions of religiousness matter? Journal for the Scientific Study of Religion, 50(3), 570–587. https://doi.org/ 10.1111/j.1468-5906.2011.01583.x [DOI] [Google Scholar]
  58. Sharifian, N., Spivey, B. N., Zaheed, A. B., & Zahodne, L. B. (2020). Psychological distress links perceived neighborhood characteristics to longitudinal trajectories of cognitive health in older adulthood. Social Science & Medicine, 258, 113125. https://doi.org/ 10.1016/j.socscimed.2020.113125 [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Sliwinski, M., & Mogle, J. (2008). Time-based and process-based approaches to analysis of longitudinal data. Handbook of Cognitive Aging: Interdisciplinary Perspectives, SAGE Publications, Inc., 477–491. https://doi.org/ 10.4135/9781412976589.n28. [DOI] [Google Scholar]
  60. Sonnega, A., Faul, J. D., Ofstedal, M. B., Langa, K. M., Phillips, J. W., & Weir, D. R. (2014). Cohort profile: The Health and Retirement Study (HRS). International Journal of Epidemiology, 43(2), 576–585. https://doi.org/ 10.1093/ije/dyu067 [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Sudheimer, K. D., O’Hara, R., Spiegel, D., Powers, B., Kraemer, H. C., Neri, E., Weiner, M., Hardan, A., Hallmayer, J., & Dhabhar, F. S. (2014). Cortisol, cytokines, and hippocampal volume interactions in the elderly. Frontiers in Aging Neuroscience, 6, 153. https://doi.org/ 10.3389/fnagi.2014.00153 [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Ulmer, J. T., & Scheitle, C. P. (2020). Religious congregations and crime incidents: Opportunity and bias. Journal of Crime and Justice, 43(2), 193–211. https://doi.org/ 10.1080/0735648x.2019.1628801 [DOI] [Google Scholar]
  63. Upenieks, L., & Schieman, S. (2023). Divine compensation? Gender, religiosity, and the link between feeling underpaid and psychological distress. Review of Religious Research, 65(4), 445–475. https://doi.org/ 10.1177/0034673x231213950 [DOI] [Google Scholar]
  64. Upenieks, L., Schieman, S., & Bierman, A. (2022). Jitters on the eve of the great recession: Is the belief in divine control a protective resource? Sociology of Religion, 83(2), 194–221. https://doi.org/ 10.1093/socrel/srab018 [DOI] [Google Scholar]
  65. Upenieks, L., & Zhu, X. (2024). Life course religious attendance and cognitive health at midlife: Exploring gendered contingencies. Research on Aging, 46(2), 95–112. https://doi.org/ 10.1177/01640275231188998 [DOI] [PubMed] [Google Scholar]
  66. Wight, R. G., Aneshensel, C. S., Miller-Martinez, D., Botticello, A. L., Cummings, J. R., Karlamangla, A. S., & Seeman, T. E. (2006). Urban neighborhood context, educational attainment, and cognitive function among older adults. American Journal of Epidemiology, 163(12), 1071–1078. https://doi.org/ 10.1093/aje/kwj176 [DOI] [PubMed] [Google Scholar]
  67. Wilt, J. A., Van Tongeren, D. R., & Exline, J. J. (2024). Are daily supernatural attributions to God and the devil/demons linked with meaning in life? The Journal of Positive Psychology, 19(2), 369–378. https://doi.org/ 10.1080/17439760.2023.2169630 [DOI] [Google Scholar]
  68. Yu, X., Yang, J., Yin, Z., Jiang, W., & Zhang, D. (2021). Loneliness mediates the relationships between perceived neighborhood characteristics and cognition in middle-aged and older adults. International Journal of Geriatric Psychiatry, 36(12), 1858–1866. https://doi.org/ 10.1002/gps.5595 [DOI] [PubMed] [Google Scholar]
  69. Zaheed, A. B., Sharifian, N., Kraal, A. Z., Sol, K., Hence, A., & Zahodne, L. B. (2019). Unique effects of perceived neighborhood physical disorder and social cohesion on episodic memory and semantic fluency. Archives of Clinical Neuropsychology, 34(8), 1346–1355. https://doi.org/ 10.1093/arclin/acy098 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

gbae147_suppl_Supplementary_Materials
gbae147_suppl_supplementary_materials.docx (54.8KB, docx)

Data Availability Statement

The analytic data sets for this study incorporate multiple restricted data sources from the Health and Retirement Study (HRS). Due to privacy and confidentiality requirements, access to these data sets is regulated and requires a formal application process. Researchers interested in utilizing these data sets for their research must apply through the HRS website at https://hrs.isr.umich.edu.

Articles from The Journals of Gerontology Series B: Psychological Sciences and Social Sciences are provided here courtesy of Oxford University Press

RESOURCES