Aging in Place of Vulnerable Older Adults: Person

Abstract

Based on the premise that the experience of aging in place is different for vulnerable subgroups of older adults compared with less vulnerable subgroups, we focus on low-income older adults as a vulnerable subgroup and senior housing as an alternative to a conventional, private home environment. Using the 2008 and 2010 waves of the Health Retirement Study, regression models determined the impact of person–environment (P-E) fit between poverty status and residence in senior housing on self-rated health. Consistent with the environmental docility hypothesis, findings show that, among low-income individuals, the supportive environment of senior housing plays a pronounced compensating role and may be a key to successful adaptation in aging. As the first research effort to empirically demonstrate the positive health effects of senior housing among socioeconomically vulnerable elders, our findings provide a much-needed theoretical and practical underpinning for policy-making efforts regarding vulnerable elders.

Keywords

aging in place person–environment fit vulnerable elders

Research has consistently demonstrated that older adults prefer to live in their own homes as long as possible (Cutchin, 2003), because the familiar environment provides security and independence. In general, aging in place refers to individuals growing older while remaining in their homes. Home can include a range of potential living environments in residential settings, surrounding neighborhoods, and broader communities (Black, 2008). Aging in place is believed to benefit older adults in general; however, given the heterogeneous nature of older adults, the positive effects may not be the same across subgroups (Iwarsson, Horstmann, & Slaug, 2007). Examining the experience from the environmental perspective (Lawton & Nahemow, 1973) may enable researchers to identify differential effects of aging in place among such subgroups. This perspective suggests that unique combinations of personal needs and resources together with environmental characteristics determine an individual’s adaptation (Wahl, Iwarsson, & Oswald, 2012). Often referred to as the person–environment (P-E) fit perspective, it posits that persons within particular subgroups may be at risk of maladaptation—low well-being and life quality, for example—depending on the level of fit between their needs and environment. Even those who have limited resources and capability can age optimally if environmental characteristics support them in a way that compensates for their limitations or lack of resources.

Although a growing body of research examines older adults’ health and well-being operationalized as aging in place (Greenfield, 2012), there are several limitations. Existing research focusing on various vulnerable subgroups of elderly is very limited. Most existing environmental-perspective research examines health conditions and the role the physically supportive features of home and housing play as older adults increasingly spend more of their time in the home (Wahl, Fänge, Oswald, Gitlin, & Iwarsson, 2009). Although deterioration of health conditions is one of the primary risk factors that accompany aging, it is only one of many sources of vulnerability. Others include low sociodemographic position (e.g., racial or ethnic minority or low socioeconomic status), loss or absence of social support (e.g., death of spouse or childlessness), and living arrangement (e.g., living alone; Grundy, 2006). This study focuses on older adults who risk poor health and well-being because they occupy an economically disadvantaged position and have limited ability to incorporate available support into their living environment (Golant, 2003).

It is important to consider variation in place of residence. In aging in place literature, place is generally synonymous with home—a living environment that ranges from specific residential settings to surrounding neighborhoods and broader communities (Black, 2008). The emergence in recent decades of varied housing options for older adults results from the growing diversity of older adult populations, the increasing frailty of subsidized housing residents, the development of assisted living (AL), and the implementation and expansion of federal and state housing policy (Pynoos, Liebig, Alley, & Nishita, 2004). However, current knowledge and understanding of aging processes in alternative residential environments such as senior housing are still fairly limited (Castle, 2008; Freedman & Spillman, 2014).

Drawing on the P-E fit perspective, the present study addresses gaps in aging in place research by focusing on vulnerable subgroups of older adults and their aging experiences in senior housing environments. In this study, aging in place is conceptualized as a process of aging at a site that involves a varying degree of “fit” between the individual, the social environment, and the physical setting, leading to differences in physical and psychological health. For the environmental context for aging in place, we focus on senior housing that provides various supportive social and health services.

To measure the person dimension, we use income status; to measure the environmental context, we examine senior housing residency as a planned living environment in contrast to a private home. To gauge aging in place, we look at self-rated health. A more comprehensive understanding of the dynamic of aging in place among person, environment, and adaptation will contribute to environmental research in aging that can better identify more vulnerable subgroups and inform the development of useful prognostic tools for intervention programs.

Aging in Place for Vulnerable Elders

The environmental perspective of aging seeks to understand how a person “fits” into his or her environment (P-E fit) and to explicate the role of the environment on older adults’ health and well-being. When environmental demands overwhelm an individual’s competencies (i.e., when the individual experiences functional frailty), he or she is less likely to age in place (Lawton & Nahemow, 1973). Specific contexts of aging in place include physical features of the home (e.g., handle bars in bathrooms, wheelchair ramps), home technology (e.g., universal design), retirement living facilities, or more broadly natural or designed neighborhoods and communities that attend to the increasing and varying health and social support needs of older adults. Although they differ, these various contexts promote independence and autonomy for older adults for as long as possible.

Although aging in place and the P-E fit perspective play a central role in aging policy, practice, and research, the lack of attention to vulnerable subgroups of older adults is a concern. Older adults may universally prefer aging in place, but the choices and services to enable that are much broader for the wealthy (Beggs, Villemez, & Arnold, 1997; Chui, 2008); for example, older adults with middle to low income may lack the resources to modify their homes (Bates & Fasenfest, 2005). In addition, the conventional understanding of the benefits of aging in place may not be applicable to all (Means, 2007). Indeed, for some groups of vulnerable elders, aging in place is not a desirable choice, but they remain in their homes because they lack a better option.

A central proposition of the P-E fit perspective is the environmental docility hypothesis (Lawton, 1989). It suggests that individuals with less ability are affected to a greater extent by the same environmental demands or resources than individuals with more ability. Guided by this proposition, a large body of aging in place research and programs have examined the P-E fit between community-dwelling older adults with various physical and/or cognitive impairment (Stevens-Ratchford & Krause, 2004; Tomey & Sowers, 2009) who live in a conventional home environment (Iwarsson et al., 2007; Oswald et al., 2007), AL (Bicket et al., 2010), and/or in a supportive neighborhood (Yang & Sanford, 2011).

Researchers have also examined individuals with limited resources and those in racial or ethnic minority groups. Lehning, Smith, and Dunkle (2014) focused on African American elders with fewer socioeconomic resources in Detroit, examining the role of physical and social environmental characteristics on their self-rated health. Findings showed that a supportive environment—including access to health care, presence of social support, and community engagement—is associated with better self-rated health, whereas neighborhood problems are associated with worse self-rated health. Another study examined home and neighborhood satisfaction among African American older adults in Detroit (Byrnes, Lichtenberg, & Lysack, 2006), explicitly testing P-E fit by looking at the effect the interaction between environmental characteristics (home and neighborhood hazards) and physical and mental health had on home and neighborhood satisfaction. Findings confirmed the environmental docility hypothesis that older adults with the lowest level of mental and physical health are more likely to face the greatest environmental challenges and have the lowest level of residential satisfaction. They found that, among socioeconomically vulnerable older adults, residing in an impoverished area had an overwhelming effect on the ability to live a good life; however, home environment did not (Byrnes et al., 2006). This finding suggests that considering a private home as the sole signifier of place may be too limited.

These studies make an important contribution to environmental gerontological research, giving specific attention to the broader environment to explore how the experience of aging in place might differ between vulnerable and less vulnerable populations. To further understand differential patterns of aging in place, this study focuses on senior housing as home and perceived neighborhood environment, because home and neighborhood may have different meanings for vulnerable subpopulations of older adults who, most likely, have few residential options.

Senior Housing as an Alternative Living Environment

A diversity of housing options for older adults has emerged as a result of pressures from a multitude of sources, including the growing population diversity among older adults, the increasing frailty of subsidized housing residents, the development of AL, and the implementation and expansion of federal and state policies related to housing for older adults (Pynoos et al., 2004). In recent decades, senior housing has been advocated as a key component of a community-based, long-term care policy for older adults, linking housing with health and social services to support aging in place (Stone, Harahan, & Sanders, 2008). A range of different terms are used in the literature, including supportive housing, congregate living facilities, age-segregated housing, and purpose-built retirement communities. It is estimated that about 10% of the older population resides in senior housing (Schafer, 2000), a proportion that is increasing (Jamieson & Simpson, 2013). Such housing and care arrangements aim to help older persons age in place and are often the bridge between independent living in a conventional home and living in a long-term institutional care facility (Field, Walker, & Orrell, 2002).

The problem is that there is fairly limited empirical knowledge about whether and how much living in senior housing supports aging in place because most of the existing research focuses on older adults in conventional homes. This knowledge base may not be generalizable to the older adult population as a whole (Parmelee & Lawton, 1990); for example, the process of aging in place in a senior residential facility may be different than in a conventional home. Residential care environments both provide and restrict social opportunities (Eckert, Carder, Morgan, Frankowski, & Roth, 2009). The environment of senior housing may be unique in that daily living experiences are shaped by both its physical features and its social-relational characteristics; the senior housing environment may represent an empirical example of an intertwined physical and social environment (Wahl & Lang, 2004) or the “socio-physical” environment as suggested by Canter and Craik (1981).

Growing research on various types of senior housing including assisted living facilities (ALFs) and Continuing Care Retirement Communities (CCRC) has examined broader quality of life issues: physical and mental health (McLaren, Turner, Gomez, McLachlan, & Gibbs, 2013; Parsons, Mezuk, Ratliff, & Lapane, 2011), social-relational environment (Kemp, Ball, Hollingsworth, & Perkins, 2012), and health behavior (Gaines, Poey, Marx, Parrish, & Resnick, 2011).

This study uses the inclusive term senior housing to refer to a noninstitutional residential environment that supports residents’ independent living by ensuring that a range of services are provided or arranged for to meet residents’ evolving needs (American Association of Homes and Services for the Aging [AAHSA], 2010). The variety of senior housing options has been fueled in large part by seniors with the financial resources to pay for a range of higher end housing options; those who lack the ability to pay have been mostly ignored. Ironically, for low-income elders, the implications of senior housing are particularly important.

Three factors place low-income elders at higher risk of challenges to successful aging in place: relatively worse health conditions (chronic conditions, physical and cognitive impairment), poorer housing conditions (physical deficiencies, outdated equipment), and insufficient financial resources to modify housing features or access supportive services (Bates & Fasenfest, 2005; Gibler, 2003).

Almost two million low-income older adults have excessive housing costs (paying more than half their income for rent) or live in moderately or severely inadequate housing (Stone et al., 2008). Many vulnerable elders in conventional homes view aging in place as their only choice (Torres-Gil & Hofland, 2012) because they cannot afford anything else. Given the high poverty rates among older adults (9.7% of adults aged 65 and above live at or below the federally defined poverty threshold and 15% live at near poverty), an increasing number of low-income elders face a higher risk of declining health, functioning, independence, and even premature or avoidable nursing-home placement due to their inadequate residential environments (Salkin, 2009; Spillman, Biess, & MacDonald, 2012). It is necessary to address at the policy level the large and rapidly expanding pool of low- and moderate-income elders who have the challenges of finding and maintaining affordable, stable housing that they can adapt to their changing needs (Stone, 2013). Senior housing for low-income elders has long been recognized as an underutilized resource to address the looming health care issues affecting vulnerable subgroups of older adults (Handy, 2012). To make matters worse, the development of policies and programs supporting low-income senior housing have stagnated in the past several years (Salkin, 2009).

Concern about the lack of senior housing for low- and moderate-income elders is confounded by the still-limited empirical knowledge on aging in place in senior housing in general and the extremely limited research examining whether and to what extent senior housing supports aging in place for vulnerable subgroups of elders, particularly the low income. The few studies on subsidized housing programs found reduced anxiety and depression, feelings of personal control, improved psychological well-being (Ficke & Berkowitz, 2000; Monk & Kaye, 1992), and a greater sense of safety and security (Mollica & Morris, 2005) among senior housing residents. Empirical studies comparing aging in place among low-income elders in subsidized senior living environments and conventional homes are rare. One study compared change in functional-health status among comparable groups of low-income older adults, one group living in conventional, private homes and the other in an affordable ALF (Fonda, Clipp, & Maddox, 2002). Findings showed residents in assisted living reported a similar pattern of improvement or decline in functional health compared with their community-dwelling peers but were more likely to maintain high-functioning status (Fonda et al., 2002).

Existing research seems to share several important limitations: First, little work has examined health outcomes to determine whether and to what extent senior housing supports aging in place (see Golant, Parsons, & Boling, 2010). Second, all extant studies have been based on pilot programs and/or small samples within specific geographic areas (Golant et al., 2010). Third, to the best of our knowledge, no known study has used a longitudinal, nationally representative sample to look at the effects of living in senior housing.

The Present Study

Guided by the P-E fit perspective, this study explored three dimensions of aging in place: person, environment, and adaptation. For the person dimension, we focused on income status because many social policies and programs are developed and designed with the federal poverty level (FPL) as one criterion for eligibility. To gauge the environmental context, we examined senior housing residence. To measure adaptation, we examined self-rated health as the outcome of the P-E fit model. Validity of self-rated health as a parsimonious measure of health and well-being in old age has long been demonstrated; it captures not only one’s physical health status but also one’s social, emotional, and psychological well-being (Benyamini, Leventhal, & Leventhal, 2000; Diener, Suh, Lucas, & Smith, 1999; Dowd & Zajcova, 2007). Some attention has been given to environmental contexts such as neighborhood on self-rated health (Lehning et al., 2014); however, no known study has examined the extent to which the self-rated health of vulnerable elders differs by type of senior housing residence.

This study has two main objectives. First, we examine income status and senior housing as related to sociodemographics, health conditions, and environmental contexts. Based on the previous socioeconomic inequality literature, we expect that lower income status is associated with lower self-rated health (Feinstein, 1993; House et al., 1994) and worse environmental conditions (Braubach & Fairburn, 2010). Regarding senior housing, based on previous research on residential relocation in later life (Glaser & Grundy, 1998; Oswald & Rowles, 2006), we generally expect that elders in senior housing will be more likely to be older and in worse health. Second, as a way to empirically examine P-E fit, we first look at the extent to which income status and senior housing residence are associated with self-rated health independently and in combination after controlling for all relevant covariates. Based on the previous literature on self-rated health in old age (Chandola, Ferrie, Sacker, & Marmot, 2007), we expect lower income status to predict lower levels of self-rated health. We did not posit a specific hypothesis, because no known study has empirically considered self-rated health directly comparing older adults in senior housing and in conventional homes. Still, given the supportive nature of senior housing, we generally expect that residents in senior housing will be more likely to have better self-rated health compared with their peers in private homes. Next, we examine to what extent living in senior housing moderates the effects of poverty on self-rated health. Guided by the environmental docility hypothesis, we hypothesize that lower income individuals who reside in senior living are likely to have better self-rated health.

Method

Data and Sample

Participants were drawn from the 2008 and 2010 waves of the Health and Retirement Study (HRS). The HRS is a national longitudinal study that surveys more than 22,000 older adults aged 50 and older and their spouses every 2 years. Details of the data are available elsewhere (Servais, 2010). . In our analysis, all independent variables (IVs) including senior housing experience were taken from Wave 1, and the dependent variable (DV; self-rated health) was taken from Wave 2 to make a clear time causality between IVs and DV (Kim et al., 2015; Liang et al., 2005). In addition, self-rated health in Wave 1 was controlled for in our analyses to show more clearly the effects of senior housing on self-rated health.

Our sample was based on four criteria: First, to minimize inclusion of relatively younger older adults who have relocated to senior housing for amenity-seeking reasons rather than to adapt to losses and limitations in old age (Lovegreen, Kahana, & Kahana, 2010), we selected older adults aged 75 years and older to focus on the old-old or very-old group among older adults (Ferraro, 1980; Neugarten, 1996; Nygren et al., 2007). Second, respondents who were institutionalized or unable to independently answer survey questions across two waves were excluded. Third, the sample was restricted to respondents who provided housing-related information. Finally, because we used lagged variables for self-rated health to control for potential confounding effects, the sample was further limited to those who participated in both waves of the study.

Using these criteria, our original sample consisted of 3,160 individuals. In the HRS data, if a respondent is age eligible for the survey, other members of the household including a spouse are also surveyed. Because self-rated health among individuals in the same household may be interdependent, we included only one respondent from each household. This is particularly necessary in that interdependence of self-rated health may be stronger among those in senior housing because that may be a narrower experience than in a traditional private home, which permits wider social engagement within the community. After randomly selecting one respondent from each household, the sample was reduced to 2,684. We removed 18 cases (0.6 % of the sample) with missing information on one or more of the covariates, resulting in a final sample of 2,666.¹

In this study, we built a series of hierarchical models to examine the role of theoretical constructs in the P-E fit perspective. For age cohort comparisons, we formed two groups: the old-old, age 75 to 84 years (79% of the sample), and the oldest, age 85 and above (21%); 60% of the sample were women; 87% were White; 10% currently resided in senior housing; 25% had a moderate income; and 31% had a low income.

Measures

Low-income status

To focus on vulnerable subgroups, we used the definition of low income from previous literature (Spillman et al., 2012) to categorize the sample into three groups: Income higher than 300% of FPL was coded as non-low income (0), between 185% and 300% of FPL as moderate income (1), and below 185% of FPL as low income (2). The FPL group corresponds roughly to the national level of 80% of Area Median Income (AMI), the low-income threshold used by the U.S. Department of Housing and Urban Development (HUD).

Senior housing residency

Senior housing residency was measured with one question “Is your (house/apartment) part of a retirement community, senior citizens’ housing, or some other type of housing that offers services for older adults or someone with a disability?” Data on senior housing were aggregated across earlier waves of the study and updated with the information on new move-ins in 2008. Values for senior housing were hierarchically assigned for those entering the study prior to 2010. A binary indicator was used to measure residency in senior housing (0 or 1).

Self-rated health

Self-rated health was measured with a 5-item scale (1 = poor, 2 = fair, 3 = good, 4 = very good, 5 = excellent). Studies often measure self-rated health as an ordered categorical indicator, which we attempted to do in the preliminary analysis. However, diagnostic tests indicated that the assumptions of an ordered logit model were violated (i.e., homoscedasticity and proportional odds assumption). As an alternative, we treated it as a continuous measure, because preliminary diagnostics suggest no violations of normality assumption.

Covariates

We included a number of sociodemographic covariates: age cohort groups (aged 75-84 = 0, 85 years and above = 1), current marital status (not married = 0, married = 1), gender (women = 1, men = 0), education (less than high school = 0, high school = 1, some college = 2, college graduate = 3), and race (White = 1, non-White = 0).

Guided by the P-E fit perspective, we included additional covariates in the person and environment dimensions that have been identified as influential factors in older adults’ aging in place. For the person dimension, we included a range of health status indicators. Functional status evaluated activity of daily living (ADL) skills measured by difficulty in bathing, eating, dressing, walking across a room, and entering or leaving bed (no ADL limitations = 0, 1 or more limitations = 1). Physical health was measured on a scale of 0 to 8, counting the presence of chronic health conditions prevalent in later life. The environmental context was guided by previous environmental research emphasizing the importance of the experiential dimension of aging in place (Rowles & Chaudhury, 2005). For supportive in-home features, we used five features available in the data: ramp, railing, wheelchair access, bathroom fixtures including grab bar, and emergency call button, all coded 1 (present) or 0 (absent). A summary score of the five variables provides the level of supportive physical features in home. To measure the global housing environment, respondents were asked to rate the physical condition of their housing (1 = poor condition, 2 = fair, 3 = good, 4 = very good, 5 = excellent). The neighborhood environment was measured with one question “Would you say the safety of [your/that] neighborhood is excellent, very good, good, fair, or poor?” (1 = poor, 2 = fair, 3 = good, 4 = very good, 5 = excellent). To control for the unequal availability of senior housing facilities, residential region was included (urban areas of more than 250,000 population = 0, rural areas of fewer than 250,000 population = 1).

Analysis

First, bivariate analyses were done using chi-square and one-way ANOVA tests to determine differences in sociodemographic covariates, health conditions, and environmental contexts. Next, using linear regression analysis in three hierarchical models, we examined the main and interaction effects of income status and senior housing residency on self-rated health. In the first model, sociodemographic controls, income status, and senior housing residency were entered. In the second model, health conditions and environmental covariates were added. The final model included the interaction terms between income status and senior housing residency. All predicting variables, including covariates as well as wave 1 self-rated health, were entered as 2008 measures. The DV, self-rated health, was measured in 2010. Analyses were conducted with STATA 13.

Results

Table 1 presents characteristics of the sample and their differences across income status and senior housing environment. Clear patterns of association indicate that low-income elders were the most vulnerable, followed by moderate-income elders. Relative to their better-off peers, low-income elders were significantly more likely to be the oldest old (26%), women (75%), and have lower levels of education. A lower proportion of the low-income group was White (78%) or married (25%). The income groups also varied by health and environmental characteristics. A higher proportion of low-income elders had ADL (19%) problems and more chronic conditions (M = 2.69). The same pattern emerged on environmental context: Low-income elders were more likely to have lower levels of perceived quality of housing (M = 3.53) and neighborhood safety (M = 3.68) and a higher proportion lived in rural areas (35%).

Table 1.

Background Characteristics and Components of P-E Fit Among Older Adults.

	Person (income status)					Environment (senior housing)
	Entire (N = 2,666)	Low income (n = 815, 31%)	Modest income (n = 662, 25%)	Non-low income (n = 1,189, 45%)	Statistics	Senior housing (n = 277, 10%)	Conventional home (n = 2,389, 90%)	Statistics
	% or M (SD)	% or M (SD)	% or M (SD)	% or M (SD)	Statistics	% or M (SD)	% or M (SD)	Statistics
Sociodemographics
Age group (Reference: ≥75 and <85)
Oldest (≥85)	21	26	18	19	χ²(2) = 17.15**	35	19	χ²(1) = 39.50**
Women	60	75	58	51	χ²(2) = 119.10**	70	59	χ²(1) = 1.75**
Education (Reference: College graduate)					χ²(6) = 327.63**			χ²(3) = 3.95
Less than high school	28	44	25	17		26	28
High school	32	33	41	28		30	32
Some college	21	16	21	23		25	20
Race (Reference: Non-White)					χ²(2) = 82.97**			χ²(1) = 3.15^†
White	87	78	91	91		91	87
Married	46	25	48	58	χ²(2) = 219.85**	31	47	χ²(1) = 27.62**
Health
Disability (Reference: ADL = 0)
ADL (1-2)	19	30	27	17	χ²(2) = 30.39**	18	19	χ²(1) = 0.04
Chronic conditions	2.50 (1.33)	2.69 (1.37)	2.54 (1.33)	2.39 (1.28)	F(2, 2663) = 7.10**	2.70 (1.28)	2.48 (1.33)	F(1, 2664) = 5.86*
Environmental context
In-home supportive features	0.81 (1.20)	0.87 (1.30)	0.78 (1.17)	0.80 (0.14)	F(2, 2663) = 1.26	2.16 (1.74)	0.66 (1.01)	F(1, 2664) = 448.95**
Satisfaction with housing	3.88 (0.96)	3.53 (1.02)	3.87 (0.95)	4.12 (0.85)	F(2, 2663) = 95.62**	4.23 (0.85)	3.84 (0.97)	F(1, 2664) = 40.60**
Satisfaction with neighborhood safety	3.97 (0.99)	3.68 (1.04)	3.94 (0.98)	4.19 (0.89)	F(2, 2663) = 67.38 **	4.20 (0.92)	3.94 (0.99)	F(1, 2664) = 16.76**
Rural	31	35	32	26	χ²(2) = 18.77**	15	33	χ²(1) = 33.85**

Note. P-E = person–environment; ADL = activity of daily living.

†

p < .1.*p < .05. **p < .01.

Living in senior housing also varied on many of the correlates. Individuals living in senior housing were more likely to be the oldest old (35%), women (70%), and have a higher number of chronic diseases (M = 2.70), whereas a lower proportion of this group tended to be married (31%). Regarding environmental contexts, residents in senior housing tended to have a higher quality of housing (M = 4.23) and neighborhood safety (M = 4.20).

Table 2 presents the results of regression analysis. Compared with those in the reference group (non-low income), the direction of the association, although not significant, showed that those in the low-income group may be more likely to have worse self-rated health (B = −0.07, p < .1 in Model 1) after controlling for sociodemographic covariates. The negative effects of low income on self-rated health, however, disappear in Model 2 when health and environmental covariates are added. In Model 3, the interaction terms between income group and senior housing residency were included to investigate the extent to which senior housing moderates the main effect of a low income on self-rated health. Older adults in the low-income group were more likely to have better self-rated health when living in senior housing (B = 0.26, p < .05). Although not significant, individuals in the moderate-income group who lived in senior housing showed higher levels of self-rated health (B = 0.24, p < .1). Across all models, no main effect of senior housing residency was found.

Table 2.

P-E Fit of Aging in Place: Self-Rated Health (N = 2,666).

Self-rated health	Model 1		Model 2		Model 3
Self-rated health	B	SE	B	SE	B	SE
Self-rated health in 2008 (M)	0.59**	0.01	0.51**	0.01	0.51**	0.02
Low income (Reference: Non-low income)
Moderate income	0.01	0.06	0.04	0.06	0.01	0.07
Low income	−0.07^†	0.04	−0.03	0.05	−0.06	0.06
Senior housing residency	0.03	0.05	−0.03	0.05	−0.15^†	0.05
Sociodemographics
Age group (Reference: ≥75 and <84)
Oldest (≥85)	−0.05	0.03	−0.03	0.04	−0.03	0.04
Women	−0.03	0.03	−0.03	0.03	−0.03	0.03
Education (Reference: College graduate)
Less than high school	−0.24**	0.04	−0.22**	0.04	−0.22**	0.04
High school	−0.08^†	0.05	−0.08^†	0.05	−0.08^†	0.04
Some college	−0.09^†	5	−0.09^†	0.05	−0.08^†	0.05
Married	−0.01	0.04	−0.03	0.03	−0.03	0.03
Race (Reference: Non-White)
White	0.09	0.05	0.04	0.05	0.03	0.05
Covariates in person and environment
Health condition
Disability (Reference: ADL = 0)
ADL (≥1)			−0.25**	0.04	−0.25**	0.04
Chronic conditions			−0.08**	0.01	−0.08**	0.01
Environmental context
In-home supportive features			0.00	0.01	0.00	0.01
Satisfaction with housing			0.04*	0.02	0.04*	0.01
Satisfaction with neighborhood safety			0.06**	0.02	0.06**	0.02
Rural			0.03	0.03	0.03	0.03
P-E fit
Moderate Income × Senior Housing					0.24^†	0.13
Low Income × Senior Housing					0.26*	0.11
Constant	1.44**	0.13	1.51**	0.13	1.53*	0.15
	Adj-R² = .38		Adj-R² = .40		Adj-R² = .41
	F(11, 2654) = 149.31**		F(17, 2648) = 105.79**		F(19, 2646) = 95.10*
Δχ²	—		97.56 (6)**		5.89 (2)*

Note. P-E = person–environment; ADL = activity of daily living.

†

p < .1.*p < .05. **p < .01.

Discussion

Based on the premise that aging in place would be different for vulnerable subgroups of older adults, we focused on low-income older adults and the differences in self-rated health based on living in senior housing opposed to a conventional, private home. To the best of our knowledge, this is the first study that conceptualizes senior housing environments when empirically testing the P-E fit model.

Vulnerable Older Adults and Senior Housing as P-E Relation

For our first research question, we explored subgroups of vulnerable elders by income status and senior living environment. As expected, individuals in lower income groups (moderate- and low-income groups) were generally older and female, had a lower level of education, and worse physical housing conditions than the non-low-income group. These findings support the hypothesis of differential aging in place among vulnerable elders, who frequently experience such poor housing conditions as leaky roofs, broken equipment (e.g., toilets and heaters), and structural issues (e.g., slippery floor) without the financial resources to make repairs, adaptations, or modifications that make living in their private homes safer and more pleasant; this subgroup may be at risk of low health and well-being if aging in place.

As hypothesized, senior housing residents tended to be older, female, nonmarried, and have a higher number of chronic conditions. Compared with their peers in private homes, senior housing residents were more satisfied with the physical condition of their housing. Given that our sample was restricted to individuals 75 and older, this may reflect the physical supportive features of senior housing such as wheelchair access, handrails, and grab bars in the restroom.

P-E Fit and Self-Rated Health

For our second research question, we asked to what extent low-income status and senior housing environments were independently associated with self-rated health. Consistent with prior research findings (Chandola et al., 2007; Malmström, Sundquist, & Johansson, 1999), individuals with low-income status were more likely to have lower self-rated health (Model 1). Combined with the findings in the earlier bivariate analysis, our findings clearly showed that individuals with low-income status were most vulnerable in all social stratification factors (i.e., age, gender, race, and education) examined.

Considering the supportive environment of senior housing where staff either directly provide or arrange for various health and social services, we expected living in senior housing to be associated with better self-rated health, but we did not find a significant association. We speculate this may be partially due to the heterogeneity of senior housing environments and their residents. The limitations of secondary data precluded analysis distinguishing possibly diverse types of senior housing. Nevertheless, ours is the first empirical attempt at examining the effect of senior housing residency using nationally representative data.

Although it would be premature to make a conclusive statement regarding the effect of senior housing residency on the process of aging in place, there is clearly a need for more careful consideration. The P-E fit perspective suggests that aging individuals—even those with limited resources and capability—can experience optimal outcomes if environmental characteristics support them in a way that compensates for their limitations or lack of resources. To empirically examine the P-E fit, we measured the extent to which senior housing might moderate the effects of low-income status on self-rated health. Based on the environmental docility hypothesis, we expected that more vulnerable elders would be affected by environmental resources and needs. Consistent with the hypothesis, our findings suggest that among individuals with low-income status, the supportive environment of senior housing played a pronounced compensating role. For these groups, successful adaptation may lie in senior housing residence.

The significant effects of senior housing for the most vulnerable group have several important implications for theoretical and empirical research, as well as for policies and programmatic interventions. Individuals with low-income status in this study comprise the most vulnerable subgroup of older adults. Their low-income status may be a manifestation of inequality accumulated through developmental processes during their life course (Willson, Shuey, & Elder, 2007), and it may be that living in senior housing serves as a compensating mechanism partially countering risks, such as lower self-rated health, emerging from their life course disadvantage (Ferraro, Shippee, & Schafer, 2009). The lack of a significant role for senior housing environments among the moderate-income group seems to support this possibility. Future longitudinal research examining the trajectory of income status by different residential environments should examine P-E dynamics.

From a policy perspective, the demonstrated positive effect of senior housing for vulnerable elders is important. Across the country, approximately two million low-income seniors reside in subsidized or affordable residential facilities (Institute for the Future of Aging Services [IFAS], 2009). Affordable low-income senior housing has received an increasing amount of attention as one component of long-term care policies and programs for low-income older adults. However, to date the literature has tended to focus on either anecdotal information or on largely descriptive empirical studies pointing out the benefits of senior housing (IFAS, 2009; Pynoos et al., 2004; Wilden & Redfoot, 2002). Research findings that explore the benefits of living in such environments are far from conclusive, and the impact of supportive housing remains largely untested (AAHSA, 2010). Our findings add much-needed initial empirical evidence regarding the health benefits of senior housing for low-income elders. Research should explore the intertwined physical and social environment (Wahl & Lang, 2004), specifically, how daily living experiences are differentially shaped by the physical features of the environment and social-relational characteristics within the housing community, and how these differ from the daily living experiences in conventional homes. Although a growing body of research on senior housing has examined physical and mental health (Liu & Lapane, 2009; McLaren et al., 2013; Parsons et al., 2011) and the social-relational environment among the residents and their social networks (Kemp et al., 2012; Street & Burge, 2012), no research has yet comprehensively examined both physical features of the home and the social environment (i.e., social relations with peer residents, staff members, and friends and family).

Future research should identify the effects of the physical and social environmental characteristics on the health and well-being of older adults in senior housing compared with those living in private homes. In our study, we aimed to make an initial step toward building an empirical knowledge on the aging in place experience of low-income elders in senior housing. We believe our findings on the positive role of senior housing for low-income elders’ self-rated health is much-needed initial evidence. Future investigation of the effects of senior housing for low-income elders on specific health care outcomes such as reduced number of emergency units and nursing-home placement is important as current evidence related to these outcomes is rare (Spillman et al., 2012).

Ultimately, if future data permit a clear distinction among various types of senior housing, it will be important to comprehensively examine the role of physical and social environmental characteristics on various outcomes of aging in place, and how the association may differ across housing types. For example, Robison et al. (2010) compared community-based senior housing that provides long-term care services and a nursing home to examine various outcomes, including quality of care, quality of life, emotional well-being, and social interaction.

Limitations

Certain limitations of this current analysis should be acknowledged. Underlying the P-E fit perspective is a dynamic association between personal competence and the environment. Generalizations about vulnerable elders and senior housing environments and their influence on health would be substantially improved if it were possible to examine changes in vulnerability and residential environment over time to shed light on whether changes in these dimensions lead to, or result from, changes in health.

Another limitation concerns the bivariate indicator of a senior housing environment used. A more refined categorization of senior housing—such as independent-living facility, ALF, or continuing care residential complex—that follows senior housing industry definitions would be ideal (Coe & Boyle, 2012); the data used in this study did not permit this categorization. Many affordable senior housing options, although designated as independent-living facilities, offer service coordination programs (Perl, 2010) that link community health and social services. This makes it difficult to differentiate between private home, independent living, and AL contexts. The limited existing research is confined to comparisons between different AL residents (Fonda et al., 2002) or between residents in the continuing care environment and their peers in a conventional private setting (Gaines, Poey, Marx, Parrish, & Resnick, 2011; Robison et al., 2010). Future empirical study should continue to try to differentiate among different housing types and how they may be associated with various outcomes of aging in place.

Also, older adults in private-conventional homes, when eligible, may receive same community-based services coordinated into senior housing, which may dilute the positive effect of living in senior housing we found in this study. Despite such limitation, existing studies have indicated the relatively low rate of use of community-based services due to barriers (Cox, 2004), including lack of unawareness of available services, lack of match between available services and the deeds of older individuals, and/or lack of coordination system to facilitate the fragmented services. Our finding on positive role of senior housing for low-income elders provides important initial evidence on the effects of the senior housing environment with varying services which may be either directly provided in the building or may be coordinated by an in-house service employee. Future empirical study should continue to pursue to what extent different living in housing type is associated with various outcomes of aging in place.

Another potential limitation is sample size. Because only about 10% of the sample lived in senior housing, power may have been an issue, especially in the lack of statistically significant findings regarding the relationship between self-rated health and type of living environment. Future inquiry with data on a larger sample of senior housing residents is needed.

Despite these limitations, our study is one of the first research efforts to empirically demonstrate the positive health effects of senior housing among socioeconomically vulnerable elders. Our findings provide a much-needed theoretical and practical underpinning for policy-making efforts regarding vulnerable elders.

Footnotes

Declaration of Conflicting Interests

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

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biographies

Sojung Park, PhD, is an assistant professor in the George Warren Brown School of Social Work at Washington University in Saint Louis. She received her PhD degree from the Joint Program in Social Work and Psychology at the University of Michigan, Ann Arbor. Her research interest includes aging in place, affordable senior housing, and vulnerable elders.

Yoonsun Han received her PhD degree from the Joint Program in Social Work and Sociology at the University of Michigan and is currently an assistant professor at Sungkyunkwan University in South Korea. Her area of research concerns social relationships and contexts, as well as advanced research methods.

BoRin Kim, PhD, is an assistant professor in the College of Health and Human Services at the University of New Hampshire. Her current research focuses on social support and physical/mental health among older adults in diverse social and cultural contexts.

Ruth E. Dunkle’s research, teaching, and clinical practice focus on gerontology. Recent research projects include families with a serious mental illness, a historical perspective of social service delivery in Detroit in the 20th Century and issues related to lower income older adults aging in place. She has written several books, the most recent on the oldest old.

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Aging in Place of Vulnerable Older Adults: Person–Environment Fit Perspective

Abstract

Keywords

Aging in Place for Vulnerable Elders

Senior Housing as an Alternative Living Environment

The Present Study

Method

Data and Sample

Measures

Low-income status

Senior housing residency

Self-rated health

Covariates

Analysis

Results

Discussion

Vulnerable Older Adults and Senior Housing as P-E Relation

P-E Fit and Self-Rated Health

Limitations

Footnotes

Declaration of Conflicting Interests

Funding

Notes

Author Biographies

References