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The Journals of Gerontology Series B: Psychological Sciences and Social Sciences logoLink to The Journals of Gerontology Series B: Psychological Sciences and Social Sciences
. 2019 Apr 30;74(6):e1–e12. doi: 10.1093/geronb/gbz025

Person–Environment Fit Approach to Trajectories of Cognitive Function Among Older Adults Who Live Alone: Intersection of Life-Course SES Disadvantage and Senior Housing

Sojung Park 1,✉, Eunsun Kwon 2, BoRin Kim 3, Yoonsun Han 4
Editor: Deborah Carr
PMCID: PMC6703233  PMID: 31038160

Abstract

Objectives

Drawing from life course and environmental perspectives, we examined the trajectory of cognitive function and how senior housing moderates the effects of life-course socioeconomic status (SES) disadvantage among older people living alone over time.

Method

Six waves of the Health and Retirement Study (HRS) were used with multilevel growth modeling to analyze developmental patterns of cognitive function over time and how various forms of life-course SES disadvantage affect cognitive function depending on senior housing residency status.

Results

At baseline, we found a positive role of senior housing in four subgroups: SES disadvantage in childhood only, unstable mobility pattern (disadvantage in childhood and old age only), downward mobility (no disadvantage in childhood, but in later two life stages), and cumulative disadvantage (all three life stages). Over time, the positive role of senior housing for the unstable and the most vulnerable group persisted.

Discussion

Our findings provide a much-needed practical and theoretical underpinning for environmental policy-making efforts regarding vulnerable elders who live alone.

Keywords: Environmental gerontology, Housing, Later-year cognitive function, Life-course perspective, Living alone

In the United States, more than 12 million people over age 65—30% of the entire older population—currently live alone (Administration on Aging, 2016). An increased understanding of aging-in-place among live-alone adults is timely given the demographic imperatives and associated policy challenges of providing more care and support. Although living alone per se does not necessarily equate with vulnerability, recently a growing body of research has focused on a subset of at-risk older adults who live alone with little-to-no support (Carney, Fujiwara, Emmert Jr., Liberman, & Paris, 2016) who experience greater depression, disability, and poor physical health (Russell & Taylor, 2009), and cognitive decline (Josefsson, Luna, Daniels, & Nyberg, 2016).

We aim to contribute to the literature by examining a subset of older adults who live alone focusing on long-term changes in cognitive function, a fundamental determinant of independent living. A better understanding of cognitive decline among older adults who live alone may have important economic and social policy implications. In addition to the well-established link between socioeconomic disparities and decline in cognitive function (Staff, Chapko, Hogan, & Whalley, 2016), life-course research has suggested that later-year cognitive functional changes may originate from early life adversities, and there may be common risk factors (Lynch & Smith, 2005). Environmental research on aging has emphasized the important role of environmental context (social and physical environment in home and neighborhood, and/or congregate living environment such as senior housing) in achieving healthy aging. To date, no known study has investigated the unique experiences of older people who live alone, especially vulnerable subgroups.

Drawing from life course and environmental aging perspectives, we aimed to contribute to the literature on older adults who live alone with a dual focus on the heterogeneous patterns of socioeconomic disadvantage over the life course and the understudied effects of senior housing. Environmental living contexts are crucial for healthy aging (Lehning, Nicklett, Davitt, & Wiseman, 2017). In particular, senior housing has been advocated as a key component of community-based, long-term care for older adults that links housing with health and social services to support aging-in-place (Stone, Harahan, & Sanders, 2008). The literature offers a range of terms to describe such residential environments, but this study uses the inclusive term senior housing to refer to a non-institutional residential environment that supports residents’ independent living by providing or arranging for a range of services to meet evolving needs (American Association of Homes and Services for the Aging, 2010).

Living Alone, Life-course Socioeconomic Status and Cognitive Function in Old Age

Few studies have demonstrated the associations between living alone and cognitive decline over time. Van Gelder and colleagues (2006) examined cognitive decline over a 10-year period focusing on marital and living alone status among 1,042 men aged 70–89 in Finland, Italy, and the Netherlands and found that men who lost a partner, were unmarried, or lived alone showed over twice as much cognitive decline over the course of the study compared with men who were married or lived with someone.

Cognitive decline in old age is influenced by both earlier-life disadvantage and health changes in old age (Chen, Chiao, & Ksobiech, 2014). A growing pool of research has examined how specific aspects of life-course socioeconomic conditions may be related to later health (Otero-Rodríguez et al., 2011). Among various life-course models, the critical period hypothesis stipulates that experiences at a particular stage of life may have lasting effects in older adulthood. A large body of literature on later-year health disparities provides compelling evidence that adverse socioeconomic conditions during childhood and adolescence are a distal cause of cognitive decline and dementia (Zhang, Gu, & Hayward, 2008). Other studies, based on the social mobility hypothesis, have demonstrated that the long-term influence of early life on cognitive aging is mediated by adulthood socioeconomic status (SES). These studies emphasized that poor SES and/or poor health in childhood represent only part of the potential health-related risks over the life course; their effects may be partially or wholly attenuated by upward social mobility (Haas, 2008). On the other hand, cumulative disadvantage theory asserts that dis/advantages tend to accumulate over time, suggesting that disadvantaged experiences as early as childhood up through adulthood can have cumulative detrimental effects on the health and well-being of older adults (Dupre, 2007).

These models are closely interrelated, making it difficult to empirically disentangle the effects of various life-course SES disadvantage on later health (Hallqvist, Lynch, Bartley, Lang, & Blane, 2004). Most studies have focused on childhood and adulthood SES when examining later-year health without including SES in old age. Given the persistent and increasing socioeconomic inequalities in later-year health (Chandola, Ferrie, Sacker, & Marmot, 2007), it is critical to incorporate old-age SES into life-course measures. In this study, we examined eight dynamic patterns of life-course SES disadvantages: SES disadvantages from three critical periods (disadvantaged during childhood only, adulthood only, or old age only), heterogeneous patterns of mobility (upward, unstable, and downward pattern from childhood to old age), and cumulative SES disadvantage incorporating all three (disadvantaged in childhood, adulthood, and old age).

Person–Environment (P–E) Fit Perspective of Cognitive Function among Older Adults who Live Alone

The Ecological Theory of Aging (ETA) posits that old age is a critical phase in the life course profoundly influenced by the living environment. The theory explicitly considers aging a P–E phenomenon (Lawton, 1980) and life-course SES disadvantage as an aspect of personal competence. Wahl, Iwarsson, and Oswald (2012) propose that unique combinations of person resources (e.g., health, SES) and environmental resources (e.g., housing, social connections) determine an individual’s capacity to thrive and age well, positing that individuals can age optimally if environmental characteristics support them in a way that compensates for their limitations.

In ETA’s environmental docility hypothesis, fit suggests individuals with less ability benefit more from the buffering effects of environmental characteristics. In this study, fit is examined through cognitive function, an individual’s life-course SES disadvantage (personal resources), and their senior housing environment (environmental resources). For older adults with life-course SES disadvantage, the importance of senior housing may be more salient. Due in part to insufficient financial resources, lower-SES elders in conventional homes often view aging in their current home as their only choice (Torres-Gil & Hofland, 2012), even if they are unable to modify their homes and access services. Given that increases in the number of individuals in poverty (9.3% of adults aged 65 and over live at or below the federal poverty line [FPL] and 30.4% below 200% of FPL) has outpaced population growth among older adults living alone (Edwards, Bee, & Fox, 2017), this population faces a higher risk of declining health, function, independence, and experiencing premature or avoidable nursing home placement (Salkin, 2009). Older adults living alone often have fewer financial resources than those living with others, especially women and ethnic/racial minorities (Stepler, 2016).

Studies of senior housing programs that link housing and services for low-income elders have found improved psychological well-being (Park, Han, Kim, & Dunkle, 2017; Park, Kim, & Han, 2018); and a greater sense of safety and security (Mollica & Morris, 2005). A few studies have investigated the effects of senior housing on specific healthcare outcomes, among them reduced number of hospitalizations, emergency room visits, and nursing home admissions among moderate- and low-income elders (Spillman, Biess, & MacDonald, 2012). A study by Castle and Resnick (2016) found residents in affordable senior housing with supportive services were more likely to use healthcare services, report health improvements, and receive preventive services. They were less likely to use emergency care, be hospitalized, or move to a nursing home compared to residents in buildings not offering support. One large-scale study (Sanders et al., 2014) used 2008 Medicare and Medicaid data and found that residents living in housing with onsite service coordinators had significantly lower hospitalization rates.

The environmental docility hypothesis suggests that older adults with life-course SES disadvantages who live in senior housing may be less likely to experience cognitive decline than those who live in traditional, private homes. Studies have shown older people who live alone have fewer social contacts and many are lonely (Gow, Corley, Starr, & Deary, 2013). A small social network and loneliness are associated with a higher risk of cognitive decline and dementia (Boss, Kang, & Branson, 2015; Wang, He, & Dong, 2015). Numerous studies have suggested social engagement is associated with lower risk of cognitive decline and dementia in older adults (Bielak, 2010). One common purpose of various housing options is to help older persons age in place, a factor often seen as the bridge between full independent living in a conventional home and long-term institutional care (Field, Walker, & Orrell, 2002), with various levels and compositions of supportive services such as social activities, meal and/or transportation services, and an on-site service coordinator (Howe, Jones, & Tilse, 2013).

Increasingly, studies have shown the importance of community resources and services on cognitive function. For example, a study using a population-based sample of older adults aged 65 and over in Baltimore, Maryland (Lee et al., 2011) showed lower processing speed and executive function for persons residing in areas with higher hazard scores (including social disorganization, public safety, physical disorder, and economic deprivation). Using representative data of adults 50 years and older in Chicago, Clarke et al. (2012, 2015) found that living in a higher socioeconomic area characterized with better access to recreational centers, transportation, and/or sufficient public spaces was related to better cognitive functioning. The association between a resource-filled residential environment and cognition may reflect increased exposure to cognitively stimulating activities (Wilson et al., 2003; cf. Reijnders, van Heugten, & van Boxtel, 2013). Senior housing may encourage cognitive development and maintenance through social norms in support of beneficial social and health behaviors (Murray & Stafford, 2014) and provide opportunities for social interaction and social support (Cattell, Dines, Gesler, & Curtis, 2008). More socially complex environments in senior housing may provide more cognitive resources through diverse stimuli and the encouragement of activities such as volunteering (Park, Kim, & Cho, 2017), delaying or preventing decline in cognitive function; the protective effects may be pronounced for those with accumulated life-course SES disadvantages.

Existing research suggests the importance of housing and neighborhood environment in cognitive health; however, researchers have focused little attention on senior housing. More importantly, little work has examined subgroups of individuals with differential patterns of life-course SES disadvantage, and no known study has examined the relationship between senior housing residence and change in cognitive function.

Research Questions

Drawing on the life-course and P–E fit perspectives, changes in cognitive function are examined. To measure the person dimension, we focus on life-course disadvantage (childhood only, adulthood only, or old age only); three SES mobility trajectories (upward, unstable, downward); and cumulative SES disadvantage. To measure environmental context, we examine senior housing residency, broadly defined as non-institutional settings that support independent living in contrast to conventional, private-home dwellings.

Given that proximal experiences may be more influential than past experiences, we expect SES disadvantage in old age only is more critical in determining lower cognitive function and subsequent decline over time. Vulnerability experiences such as downward mobility and cumulative disadvantage may contribute to lower cognitive functioning at baseline and over time. We do not posit a specific hypothesis regarding senior housing; however, given the supportive nature of senior housing, we expect senior housing residents will have better cognitive function compared to their peers in conventional homes, although as individuals with greater health issues may select into senior housing, the opposite may also be true.

To empirically examine the P–E fit, we assess the extent to which senior housing residence moderates the effects of life-course SES disadvantage on cognitive function over time. We expect that individuals with childhood SES disadvantage only, downward mobility, and SES disadvantage in all three life stages are likely to have better outcomes when residing in a senior-living environment.

Study Data and Method

Six waves (2002–2012) of the Health and Retirement Study (HRS) merged with RAND Center for the Study of Aging (RAND HRS) data were used. The HRS is a national longitudinal study that surveys more than 22,000 adults aged 50 and older and their spouses every 2 years. Details of the multistage sample design, selection criteria, implementation, and response rates are available elsewhere (Sonnega et al., 2014). Study baseline is the 2002 wave. Since 2002, RAND HRS has included a poverty measure based on U.S. Census Bureau poverty threshold levels and family composition.

We used four criteria to draw our sample: First, those who lived with others were excluded. Second, respondents who were institutionalized or unable to independently answer survey questions were excluded. Third, we selected adults aged 65 and older at baseline. Decline in cognitive function could start in an early life stage; however, cognitive function tends to be stable in middle-age and deteriorate after the mid-60s (Anstey, 2014; Plassman et al., 2008). Finally, we excluded those who changed their main residence during the study period. This resulted in a sample of 3,821 individuals and 11,733 observations. Since data with multiple imputations is not technically feasible for testing nested models in Stata, we dropped observations missing covariates (n = 47 cases). The HRS survey is conducted every 2 years; up to six repeated observations were obtained over the 10-year period. During the study period, 2,233 (19.10%) respondents from the total sample died; another 1986 (16.99%) dropped out for other reasons.

Measures

Life-Course Socioeconomic (SES) Disadvantage

SES disadvantage in childhood was measured by family financial status and parents’ education and principal occupation. Financial status was assessed by whether the respondent indicated their family was poor (yes = 1; no = 0) when they were born and up to age 16. Parental education was measured by the highest level of education completed by either parent; those who did not finish high school were coded 1, whereas those who attained a high school education or higher were coded 0. Parents’ occupation was binary (1 = unskilled manual; 0 = skilled manual and above), classified based on the often-used Erikson-Goldthorpe class scheme (1992), which distinguishes professionals and managers, other white-collar employees, farmers, skilled blue-collar employees, and unskilled/low-wage service workers. To create the childhood SES index, we summed these three measures and created two categories based on a median split: The group at or above the median was coded 1 (disadvantaged) and the group below the median was coded 0 (non-disadvantaged).

SES disadvantage in adulthood was measured using respondents’ education and occupation with the same categories as parents. These two measures were summed to form an adulthood SES index then categorized into binary based on a median split: disadvantaged (coded as 1) and non-disadvantaged (0).

SES disadvantage in old age was measured using the Federal Poverty Level (FPL). Following previous literature (Spillman, Biess, & MacDonald, 2012), respondents with incomes below 185% of FPL were coded as low-income (1); those above as non-low income (0). The FPL corresponds roughly to the national level of 80% of Area Median Income, the low-income threshold used by the U.S. Department of Housing and Urban Development.

Life-course SES disadvantage was measured by cross-classifying SES disadvantage over childhood, adulthood, and old age to produce eight distinct patterns of SES disadvantage over the life course, reflecting different disadvantage exposure categories and social mobility (upward and downward). The eight patterns of life-course SES disadvantage were: no disadvantage at all (reference group); disadvantaged during childhood only, adulthood only, or old age only (Critical Period hypothesis); disadvantaged during childhood and adulthood (upward); during childhood and old age (unstable); during adulthood and old age (downward); and disadvantaged throughout the life course (Cumulative Disadvantage and Social Mobility hypotheses).

Senior Housing

Senior housing residency was measured with one question, “Is your (house or apartment) part of a retirement community, senior citizens’ housing, or other type of housing that offers services for older adults or someone with a disability?” A binary indicator was used to measure senior housing residency (0/1).

Outcome Measure

Cognitive function

Cognitive function was based on the Telephone Interview for Cognitive Status (TICS), a validated cognitive screening instrument patterned on the Mini-Mental State Examination (Rodgers, Ofstedal, & Herzog, 2003). TICS (0–35) includes immediate and delayed word recall, serial 7 backwards count, object identification, date naming, and president and vice president naming. Higher scores indicate higher cognitive functioning.

Covariates

Ascribed characteristics

We examined three factors: Age coded 1 (65–74), 2 (75–84), or 3 (85 years and above); White coded 1 and non-White 0; and gender 0 (men); 1 (women).

Marital status was coded 0 (never married), 1 (widowed), or 2 (divorced/separated). Based on life-course theories about social relationships such as the Social Convoy Model (Antonucci, Birditt, & Arjouch, 2011) and Socioemotional Selectivity Theory (Charles & Carstensen, 2010) that highlight the importance of close family and friends for maintaining health and well-being, a social support variable using a continuous indicator of presence of children, friends, or relatives living within 10 miles (yes = 1, no = 0) was included.

Functional health was measured by difficulties in activities of daily living (ADL) including bathing, eating, dressing, walking across a room, and getting in or out of bed, ranging from 0 to 6. Chronic conditions were measured by the number of chronic diseases respondents had ever been diagnosed with (high blood pressure, diabetes, cancer, lung disease, heart disease, stroke, psychiatric problems, and arthritis), ranging from 0 to 8. Depressive symptoms were measured by eight items in the short version of the Center for Epidemiologic Studies Depression Scale, ranging from 0 to 8 (Steffick, 2000).

Housing tenure was measured by the number of waves a respondent lived in their current housing. Residential area was included (urban areas >250,000 = 0, rural areas <250,000 = 1) to control for the unequal availability of senior housing facilities.

Analytical Strategy

We used multilevel growth modeling to analyze developmental patterns of cognitive function over time and how life-course SES disadvantages affect cognitive function depending on residency. Multilevel growth models are specifically designed for analysis of trajectories in repeated measures of longitudinal or panel data (Bollen, Christ, & Hipp, 2004); they estimate individual trajectories based on person-specific initial values of outcomes (intercepts) and rates of change (slope) that describe intra-individual change patterns in outcome as a function of time. The time indicator was determined by the respondent’s wave participation represented as a continuous variable centered on the grand mean; the intercept represented outcome level at mean time of follow-up (6.20 years). We did not apply weights to the regression models. Proper application of survey weights to survey data remains unclear as survey weights depend on the actual data and the design of the survey. In a complicated multilevel regression model, it becomes difficult to interpret the results (Gerlman, 2007).

We conducted preliminary analyses to determine the specification of fixed and random effects for change over time (results not shown). We estimated total constant correlation across occasions and assessed the relative magnitude of each variation via an intra-class correlation. Results show 62% of differences are due to constant mean differences between individuals, whereas 38% is due to remaining variation. Comparison of model fit between models of increasing complexity indicated a random linear model provided the best fit for describing time-related outcome change.

Variables were entered sequentially in a hierarchical model-building process. The first model added an attrition and death indicator to the unconditional model. The second added age, gender, and race indicators. The third model examined the effects of life-course SES disadvantage status and senior housing living on outcomes. The fourth model added old-age covariates to the third model. The fifth model included interaction terms between income status and senior housing to evaluate moderating effects of senior housing.

Results

Descriptive and Bivariate Analyses

As indicated in Table 1, the proportion of the oldest old (aged 85 and older) was largest among those who had SES disadvantage in both adult and old age (27.8%). Women comprised the largest proportion (84.85%) of those with SES disadvantage in old age only. White respondents were less likely to experience SES disadvantage at all stages (57.14%).

Table 1.

Characteristics of Older Adults Living Alone at Baseline (Individual baseline) (N = 3,811)

Critical period Mobility patterns
Entire No SES disadvantage (n = 1,192, 31.28%) Childhood only (n = 381, 10.00%) Adulthood only (n = 264, 6.93%) Old age only (n = 628, 16.48%) Childhood + adulthood (n = 190, 4.99%) Childhood + old age (n = 262, 6.87%) Adulthood + old age (n = 497, 13.04%) All three stages (n = 397, 10.42%) Statistics
Ascribed
 Age group 65–74 37.57 35.41 54.69 39.89 28.67 50.96 46.16 29.41 53.00 x 2(14) = 140.60***
 75–85 43.27 43.96 37.55 38.83 46.39 30.47 42.60 42.78 32.71
 ≥85 19.16 20.63 7.76 21.28 24.94 18.57 11.24 27.81 14.29
 Woman (%) 78.09 73.50 68.57 71.28 84.85 57.03 81.07 81.82 78.95 x 2(7) = 67.03***
White (%) 79.30 91.21 87.76 80.32 84.62 73.44 80.47 61.23 57.14 x 2(7) = 249.16***
Old age covariates
 Separated (%) 18.95 16.02 26.12 16.49 18.65 26.56 18.93 17.11 23.31 x 2(7) = 22.07**
 Widowed (%) 75.33 76.56 64.90 77.13 75.76 68.75 72.78 76.47 68.42 x 2(7) = 22.037**
Social network (M, SD) 1.60 (0.084) 1.50 (0.80) 1.52 (0.82) 1.60 (0.89) 1.49 (0.83) 1.57 (0.91) 1.73 (0.81) 1.69 (0.87) 1.78 (0.85) F(7,3803) = 5.86***
 Housing tenure (M, SD) 3.25 (1.480) 2.88 (0.83) 3.12 (0.84) 2.87 (0.88) 2.90 (0.70) 3.00 (0.90) 2.97 (0.82) 2.89 (0.71) 3.07 (0.79) F(7,3803) = 3.96**
 Rural (%) 28.05 20.15 24.90 25 24.94 38.28 33.14 31.02 42.86 x 2(7) = 69.23***
Senior housing 16.92 16.48 13.06 16.49 19.35 14.84 17.16 23.26 15.79
Health
 Functional limitation (M, SD) 0.42 (0.97) 0.34 (0.87) 0.25 (0.77) 0.43 (1.03) 0.45 (0.90) 0.39 (0.93) 0.37 (0.88) 0.64 (1.08) 0.62 (1.14) x 2(7) = 96.93***
 Chronic condition (M, SD) 2.24 (1.42) 2.02 (1.31) 1.97 (1.29) 2.15 (1.41) 2.07 (1.40) 2.02 (1.51) 2.37 (1.42) 2.33 (1.30) 2.47 (1.50) F(7,3803) = 5.46***
 Depressive symptoms (M, SD) 2.048 (2.11) 1.60 (1.85) 1.59 (1.17) 1.85 (1.92) 2.11 (2.11) 2.23 (2.27) 2.15 (2.15) 2.25 (2.07) 2.75 (2.33) F(7,3803) = 12.72***
 Cognitive function (M, SD) 20.88 (5.49) 22.76 (4.84) 23.06 (4.57) 19.14 (6.11) 21.30 (5.21) 20.00 (5.67) 21.10 (4.56) 17.62 (5.49) 18.13 (5.17) F(7,3803) = 58.10***
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Note. *p < .05. **p < .01.***p < .001.

Trajectory of cognitive function

Table 2 shows the hierarchical models in this study. Model 1 includes the fixed linear effects of time and examines whether, on average, there is change in cognitive function and a random linear effect of time. Cognitive function at the mean time of follow-up was 20.09 (p < .001), decreasing over time (a linear slope of −1.12, p < .001 in Model 1). Random effects are significant in between- and within-person variations in cognitive function, but are reduced or partially explained by each additional set of variables. See Table A in the Supplementary Appendix, which includes all the covariates in Model 2.

Table 2.

Life-Course SES Disadvantage, Senior Housing, and Cognitive Function Among Older Adults Who Live Alone (2002–2012; N = 11,686 observations)

Model 1 Model 2 Model 3 Model 4 Model 5
Null Ascribed + Life-course SES disadvantage + Senior housing old age covariates + Life-course SES disadvantage * senior housing
For initial level
 Intercept 20.09 (0.09)*** 17.61 (0.21)*** 19.64 (0.23)*** 18.73 (0.36)*** 19.10 (0.36)***
Senior housing (SH) −0.35 (0.15)* −1.07 (0.22)***
SES disadvantage
 Childhood only (critical) 0.16 (0.21) 0.07 (0.20) −0.13 (0.22)
 Adult only (critical) −3.10 (0.22)*** −2.87 (0.21)*** −3.00 (0.23)***
 Old only (critical) −0.48 (0.12)*** −0.52 (0.12)*** −0.65 (0.13)***
 Child + adult only (upward) −2.35 (0.26)*** −2.05 (0.25)*** −2.08 (0.28)***
 Child + old only (unstable) −0.48 (0.22) −0.56 (0.22)** −0.90 (0.23)***
 Adult + old only (downward) −3.41 (0.19)*** −3.21 (0.19)*** −3.43 (0.20)***
 All three −3.09 (0.22)*** −2.95 (0.21)*** −3.36 (0.23)***
SES disadvantage * SH
 Childhood only * senior housing 1.01 (0.42)*
 Adult only * senior housing 0.56 (0.48)
 Old only * senior housing 0.54 (0.30)
 Child + adult only * senior housing 0.00 (0.55)
 Child + old only * senior housing 1.63 (0.46)***
 Adult + old only * senior housing 1.10 (0.39)*
 All three * senior housing 2.22 (0.44)***
For linear change
 Time −1.12 (0.07)*** −0.85 (0.03)*** −0.84 (0.05)*** −0.91 (0.06)*** −0.85 (0.06)***
 Childhood only 0.12 (0.09) 0.07 (0.09) 0.00 (0.10)
 Adult only −0.15 (0.11) −0.11 (0.11) −0.16 (0.12)
 Old only −0.03 (0.08) −0.10 (0.07) −0.12 (0.08)
 Child + adult 0.04 (0.13) 0.08 (0.13) 0.05 (0.14)
 Child + old 0.08 (0.10) 0.05 (0.10) −0.12 (0.11)
 Adult + old −0.19 (0.09) −0.05 (0.08) −0.07 (0.10)
 All three 0.09 (0.09) 0.02 (0.09) −0.13 (0.10)
SH −0.17 (0.15) −1.07 (0.22)***
SES disadvantage * SH
 Childhood only * senior housing 0.14 (0.26)
 Adult only * senior housing 0.11 (0.34)
 Child + adult * senior housing 0.31 (0.36)
 Old only * senior housing 0.00 (0.21)
 Child + old * senior housing 0.63 (0.27)*
 Adult + old * senior housing 0.04 (0.22)
 All three * senior housing 0.70 (0.26)**
Random effect
 Linear change 0.53 (0.05)*** 0.50 (0.05)*** 0.55 (0.05)*** 0.48 (0.05)*** 0.47 (0.05)***
 Between-person variance 21.12 (0.58)*** 16.24 (0.48)*** 13.78 (0.43)*** 12.22 (0.39)*** 12.13 (0.39)***
 Covariance 1.89 (0.15)*** 1.62 (0.14)*** 1.64 (0.13)*** 1.38 (0.12)*** 1.34 (0.12)***
 Within-person variance 8.28 (0.15)*** 8.44 (0.14)*** 8.46 (0.15)*** 8.42 (0.15)*** 8.42 (0.12)***
 Deviance −32781.10 (12) −32557.12 (26) −32338.35 (36) −323,128 (50)
 ∆χ2 (df) 705.58 (4)*** 447.96 (14)*** 437.54 (10)*** 104.79 (15)***
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Note. SES = socioeconomic status; SH = senior housing. No SES disadvantages at all states served as the reference group. Models 2 through 5 were adjusted for ascribed factors (age, gender, and race) and old age covariates were added to Models 4 and 5.

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

Baseline effects of life-course SES and senior housing

The main effects of life-course SES disadvantage were examined starting at Model 3. At baseline, most groups with disadvantage were significantly more likely to have lower levels of cognitive function across all models, except those who had SES disadvantage during childhood only and childhood and old age only. The group with SES disadvantage in adulthood only (B = −3.10, p < .001), those with disadvantage in adulthood and old age (B = −3.41, p < .001), and those who accumulated SES disadvantage at all three life stages were more likely to have lower levels of cognitive function (B = −3.09, p < .001). In Model 4, living in senior housing was associated with lower levels of cognitive function at baseline.

P-E fit: Interplay among life-course SES, senior housing, and time

In Model 5, interaction terms between SES disadvantage and senior housing were included to investigate the extent to which senior housing moderates the main effects of SES. Compared to older adults living in conventional homes, at baseline older adults living in senior housing with SES disadvantage in childhood only (B = 1.01, p < .05), in both childhood and old age (B = 1.63, p < .001), in adulthood and old age (B = 1.10, p < .05), and in all three stages (B = 2.22, p < .001) were likely to have higher levels of cognitive function. Over time, senior housing played a positive moderating role for those who had SES disadvantage in childhood and old age (B = 0.63, p < .05), as well as those who had SES disadvantage in all three stages (B = 0.70, p < .01). Supplementary Figure 1 shows the protective role of senior housing for the two groups.

Discussion

Focusing on life-course SES in childhood, adulthood and old age, and on senior housing as an environmental context, we examined the trajectory of cognitive function and whether senior housing moderates the effects of life-course SES disadvantage among older people living alone over time.

Life-course SES Disadvantage and Changes in Cognitive Function

Our first research question regards the extent to which life-course SES and senior housing environments are independently associated with cognitive function at baseline and over time. Partially consistent with the hypothesis, the negative influence of life-course SES in the most vulnerable group (SES disadvantage in all three life stages) and downward mobility (no SES disadvantage in childhood, but disadvantage later) stood out. This was unsurprising, since descriptive and bivariate analyses showed individuals in these groups were the most vulnerable in social stratification factors (age, gender, race) and health conditions. Notably, close to 32% of those who live alone have no SES disadvantages, appearing to belong to a “resourceful” profile (Portacolone, 2015), having accumulated financial and human (education and occupation) capitals that manifest in their relatively healthy status across all conditions (Table 1). This group is mostly white (91.21%) and widowed (76.56%), which suggests these individuals are a resourceful and/or resilient subgroup who may live alone by personal choice or changes in life circumstances. Strikingly, 47% of the sample belongs to the disadvantaged in old age group and 10% have accumulated disadvantages over the life course.

We did not find a long-term, independent effect of SES disadvantage in childhood. Instead, the strongest critical period effect was SES disadvantage in adulthood only, perhaps because life-course SES disadvantage factors were examined extending into old age. The very small association between old-age SES disadvantage only or both childhood and old age (unstable mobility) suggests that for older adults who live alone, deleterious effects on cognitive function may start in adulthood, partially supporting previous findings (Lyu & Burr, 2016).

The strongly negative effect of life-course SES disadvantage on cognitive function at baseline did not significantly change over time, consistent with a prior trajectory study for depression (Kim & Durden, 2007). Our findings suggest intra-cohort gaps in health are stable across the life course (Ferraro & Farmer, 1996). Nonetheless, the persistent inequality hypothesis predicts health and SES disadvantages will persist over time in the absence of moderating “fit” variables as suggested by the person–environmental fit perspective.

P–E Fit and Changes in Cognitive Function

Consistent with the environmental docility hypothesis, senior housing positively moderated some of the main effects of life-course SES disadvantage. At baseline, we found the positive role of senior housing in four subgroups: SES disadvantage in childhood only, unstable mobility pattern (SES disadvantage in childhood and old age only), downward mobility (no SES disadvantage in childhood, but later in life), and cumulative disadvantage (SES disadvantage in all three stages). The positive influence of senior housing was strongest for the group that experienced cumulative disadvantage. Over time, the positive role of senior housing for the unstable and the most vulnerable groups persisted, suggesting senior housing may counter risks emerging from life-course disadvantage (Ferraro, Shippee, & Schafer, 2009). These findings elucidate the increasing protective effect of a senior housing environment on cognitive functioning over time, especially for those with current SES disadvantages in old age combined with the socioeconomic hardships carried over since childhood, implying that among all life-course SES disadvantage groups, members of the unstable and cumulative disadvantaged groups may be most vulnerable, yet reap the greatest benefit from senior housing residency over time.

The buffering effect is particularly noteworthy when considering that older adults self-select into senior housing due to poor health, as indicated by the negative coefficient of senior housing in the analytic models. Aging-in-place is known to be a strong preference among older adults (Haslbeck, McCorkle, & Schaeffer, 2012), even those without the resources to modify their home environments (Torres-Gil & Hofland, 2012). Analytic results also highlight possible heterogeneity among senior housing residents. Some individuals (16.48% of the sample) may belong to a “gated elite” group (Portacolne, 2015) who relocate to a private-pay residential environment such as continuing care retirement communities (CCRC). Individuals who experience SES disadvantage in old age regardless of disadvantage in adulthood and/or childhood are likely to move into less expensive, rent-subsidized senior housing. The most vulnerable older adults benefit from supportive living environments that provide more access to health resources compared to their aging-in-place peers with similar experiences of SES disadvantage. The buffering effect of senior housing may be critical for those with cumulative SES disadvantages who lack the funds to maintain their homes in old age.

From a policy perspective, the demonstrated positive effects of senior housing for vulnerable elders is important. At-risk, living-alone older adults (more likely to live in low SES neighborhoods with limited environmental resources) are increasingly prevalent (Schüle & Bolte, 2015). Growing research highlights the importance of identifying this subgroup and screening and monitoring for health risks. Across the United States, approximately 2 million low-income seniors currently reside in subsidized independent housing (Institute for the Future of Aging Services, 2009). It is becoming more common for residents to age-in-place or move into subsidized housing at an advanced age, which has led to the need for health and supportive services for older adults as their deteriorating health conditions place them at greater risk of hospitalization and premature nursing home admission (Torres-Gil & Hofland, 2012). An estimated 1.9 million older adult are low-income renters faced with a severe housing cost burden (pay more than half of their income on rent) and/or living in severely inadequate housing in 2015 (Joint Center for Housing Studies, 2018); only one in three of those in need receive housing assistance. The waiting lists for subsidized units average at least 1 year (Lam, Locke, & Vandawalker, 2012). Our findings add much-needed initial empirical evidence for efforts to expand the supply of housing affordable to low income older adults and ensure these homes have access to services that allow people to age-in-place. Study findings provide meaningful implications for both theoretical and empirical life-course research on cognitive health. At a micro level (i.e., within the senior housing environment), supportive-residence seniors may experience improved cognitive health as a result of increased opportunities for social engagement (Bielak, 2010).

The theory of activity setting (O’Donnell & Tharp, 2012) suggests residents develop common experiences through shared activities within their housing setting, which contributes to shared behavioral development. Because of data limitations, we could not examine how social engagement among residents in senior housing environments differs from those aging-in-place. Future research should explore the intertwined physical and social environment (Wahl & Lang, 2004). An important future inquiry would be to empirically examine the extent to which senior housing’s physical, social, and service environments contribute to an increase in social engagement, which in turn may lead to health benefits for older adults. At the neighborhood level, studies have shown socioeconomic characteristics and service environment are associated with cognitive function in later years (Clark et al., 2015). Future research on senior housing should incorporate broader dimensions of the living environment to contribute to efforts to make subsidized senior housing, in partnerships with various community agencies, serve as an efficient locus of social and health service provision.

Future research is needed that focuses on ethnic/racial minor elders in senior housing. Minority populations have increased from 6.9 million in 2006 (19% of the older adult population) to 11.1 million in 2016 (23% of older adults) (Administration on Aging, 2016). Ethnic/racial minority elders may face greater vulnerability to health problems due to a confluence of lower socioeconomic status, lack of awareness and/or access to community resources, and substantial cultural barriers including limited language competency.

A primary limitation of this study is its indicator of senior housing. A more refined categorization of senior housing that follows industrial definitions would be ideal (Coe & Boyle, 2012). Future empirical study should differentiate among different housing types and how each may be associated with various outcomes. In auxiliary analyses (not shown), the size of the link between SES disadvantage variables and cognitive functioning was relatively small, despite being statistically significance, suggesting there may be other important factors that explain the variation in cognitive abilities among older adults. Identifying such factors is an area that merits further investigation. The final limitation is the measure of cognitive function: the phone-interview-based MMSE used in this study may have increased the risk of findings being negatively impacted by hearing impairments. Despite these limitations, our study is among the first to use the life-course perspective to empirically demonstrate the positive effects of senior housing among socioeconomically vulnerable elders, providing a much-needed practical and theoretical underpinning for policy-making efforts regarding vulnerable elders who live alone.

Funding

The Health and Retirement Study is conducted by the Institute for Social Research at the University of Michigan, with grants from the National Institute on Aging (U01AG009740) and the Social Security Administration. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders.

Author Contributions

S. Park conceived the study, conducted the data analyses, and took primary responsibility for writing the article. E. Kwon and B. Kim managed data and conducted the preliminary analysis. Y. Han provided critical feedback on all versions of the manuscript and contributed to all paper revisions.

Conflict of Interest

None reported.

Supplementary Material

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Acknowledgments

We thank the reviewers for their feedback and comments on earlier versions of this article.

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