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Correspondence: Address correspondence to Adaeze Akamigbo, Department of Health Management and Policy, College of Public Health, University of Iowa, 5231 Westlawn Building, Iowa City, IA 52241. E-mail: adaeze-akamigbo{at}uiowa.edu
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Key Words: African American elders • Health services research • Logistic modeling • Nursing homes • Long-Term care
A recent and interesting development in the nursing home literature is the consideration of individual expectations, that is, a reflection of one's attitudes and beliefs, and how these relate to actual nursing home placement. The analyses of expectations have been greatly facilitated by the sponsorship by the National Institute on Aging of the Health and Retirement Study (HRS) and the survey on Assets and Health Dynamics Among the Oldest Old (AHEAD). Both the HRS and the AHEAD survey assess expectations regarding future wealth transfers, retirement, and health services use. To date, three investigative teams have examined the expectations assessments in these studies (Holden, McBride, & Perozek, 1997; Lindrooth, Hoerger, & Norton, 2000; Taylor, Osterman, Acuff, & Østbye, 2005). They have shown that the main antecedents of expectations are the standard risk factors for placement, such as limitations in activities of daily living (ADLs), absence of social supports, and advancing age, leading these investigators to conclude that expectations for placement are rationally based (Greene & Ondrich, 1990; Holden et al., 1997; Lindrooth et al., 2000; Salive, Collins, Foley, & George, 1993). However, some evidence for an independent association of expectations on actual nursing home placement has also been reported (Taylor et al., 2005).
These initial studies of expectations regarding placement have made important contributions to the literature. They are not, however, without limitations. These include the absence of age as a selection criterion, the treatment of expectations as a continuous measure, the absence of several potential confounders, and the decision to consider race and gender as having only additive effects. Our purpose in this article is to contribute to the literature by addressing these four limitations. First, our analysis is limited to older adults who are 70 years of age or older at baseline. This is the most appropriate population from which to draw conclusions about the role that expectations may play in eventual placement for older adults, because placement is on their immediate time horizon. The 51- to 61- year-old HRS individuals that Holden and colleagues studied were sufficiently younger that their perceptions of the potential risk for being placed in a nursing home might have been inherently different (i.e., more hypothetical) than the perceptions of those 10 to 20 years their senior. Simply put, the risk of nursing home placement is not yet on the radar of individuals in their fifth decade, and their reports may not generalize to those in their seventh or eighth decade. Although Lindrooth and colleagues (2000) and Taylor and colleagues (2005) focused on older adults, both claim to have included non-age-eligible spouses (i.e., participants younger than 70 years of age) and proxy respondents. Because proxy respondents were not asked the risk perception questions, it is not clear how they could have been included in those prior analyses.
The second limitation that we address is the treatment of expectations as a continuous measure. HRS and AHEAD participants were asked at baseline to report the chance, in percentages, that they believed they had of being placed in a nursing home over the next 5 years. The resulting distribution was nonnormal. Indeed, in the AHEAD sample there is a clustering (or heaping) of responses at several specific (but not uniformly cyclical) points throughout the full range. Moreover, 14% of the AHEAD participants chose not to report their expectations. Of those who did respond to the expectation question, 51% estimated no chance of nursing home placement within 5 years, 6% indicated a 10% chance, and 10% indicated a 50% chance. Modeling these expectations as a continuous variable is ill advised given this pronounced clustering. Especially problematic is the selection bias that results from the exclusion of the substantial number of participants who did not report their expectations at all. To overcome this limitation, we adopt a categorical approach in which the cumulative distribution of expectations serves as a guide for the categorization and incorporates the nonresponders as a separate group.
The third limitation we address involves the set of covariates employed to address potential confounding. The AHEAD survey includes many measures that were previously unavailable in prior data sets used to estimate the risk for placement among older adults. Despite the availability of these potential confounders, few have been included in previous analyses of expectations for nursing home use. The less comprehensive nature of the covariate set may have resulted in the findings reported by Taylor and associates (2005) regarding a marginal independent effect of expectations on subsequent placement.
The fourth limitation of the previous studies of expectations for placement involves the treatment of race and gender. A large body of literature has identified race and gender, along with ADLs and incontinence, as prime risk factors for nursing home placement (Kemper & Murtaugh, 1991; Liu, Coughlin, & McBride, 1991; Liu, McBride, & Coughlin, 1994; Murtaugh, Kemper, & Spillman, 1990; Salive et al., 1993; Wolinsky, Callahan, Fitzgerald, & Johnson, 1992, 1993). Women generally outlive men and thus are more likely to survive until very old age, when the risk for placement increases. For married men, the risk is reduced because their spouses outlive them, and women generally serve as caretakers; this makes it possible for men to remain in the community for a longer period (Freedman, Berkman, Rapp, & Ostfeld, 1994). The mechanism that explains observed racial differences in placement is less clear. Many studies have found that African Americans are at a reduced risk for placement even though older Blacks have poorer health status than older Whites (Kemper & Murtaugh, 1991; Wallace, Levy-Storms, Kington, & Andersen, 1998; Wolinsky et al., 1992). Despite what is known about the individual effects of race and gender, to our knowledge the interaction between these two variables and how this affects perceptions or eventual nursing home use has never been studied. In this article, we carefully consider the Race x Gender interaction, and its observed effects on expectations for placement, as well as nursing home placement itself.
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The AHEAD survey includes a total of 7,447 participants who were 70 years of age and older in 1993 during baseline interviews. This study is restricted to nonproxy respondents and individuals identified as being Caucasian or African American. Any nursing home admission for these individuals that occurred after their baseline interview was captured during each wave of data collection or during exit interviews with collaterals previously identified by the respondent. The final analysis includes 6,242 individuals, 5,363 of whom are White and 879 of whom are African American.
Expectations
At baseline, the AHEAD survey addressed a variety of expectations, including the chance of moving to a nursing home within 5 years. This is the question we focus on for this analysis: Of course nobody wants to go to a nursing home, but sometimes it becomes necessary. What do you think are the chances that you will move to a nursing home in the next 5 years?
Response options ranged from 0% to 100%. A response of 0 indicates absolute certainty that one will not be placed in a nursing home, whereas a response of 100 indicates absolute certainty of placement. A response of 50 suggests maximal uncertainty about future placement. Individuals also had the option of not indicating their chances, with response options for "don't know," "refused," and "inappropriate". In these analyses, we examined expectations as a categorical variable with a set of four dummy variables (1%–10%, 11%–50%, 51%–100%, and no response, vs the reference category of 0%). We selected these categories on the basis of question response clustering (see the paragraphs that follow).
Variables in the Model
The predisposing characteristics reflect an individual's propensity to seek and use health services (Andersen & Newman, 1973; Greene & Ondrich, 1990), and they include demographic and social factors, as well as attitudes and beliefs. The demographic factors we included were age (dummy variables for 75–84 years of age and 85 and older vs the reference category of 69–74 years of age), race (0 = Whites, 1 = Blacks), gender (0 = women, 1 = men), and marital status (0 = married, 1 = unmarried, divorced, single, or separated). Our coding of these variables reflected tipping points in their distributions that maximize contrast in relation to the expectations. Some of these tipping points reflect the nonlinear relationships of these variables with either expectations or nursing home placement.
The social characteristics we included were education, social supports, geographic location, and population density. We measured education with a binary variable indicating whether the participant was a high school graduate (0 = no, 1 = yes). We added a number of social support variables, including binary markers for having children (0 = one child or none, 1 = two or more children), living with others in the household (0 = none, 1 = one or more people), and having access to informal or formal helpers (0 = no helpers, 1 = yes). Some researchers have found social supports to increase the risk of placement, whereas others have found that supports moderate the impact of stress on risk for placement (Aneshensel, Pearlin, & Schuler, 1993; Newman, Struyk, Wright, & Rice, 1990; Pearlman & Crown, 1992). We included three dummy variables for region of the country where a participant lived (South, North central, and East versus West as the reference category), and we used a binary indicator for population density (living in a metropolitan statistical area, or MSA).
The remaining measures of the predisposing characteristics reflect attitudes and health beliefs. We included markers for the propensity to institutionalize, and expectations for nursing home placement. The two markers for the propensity to institutionalize reflected nursing home use or hospitalization in the past 12 months (0 = no, 1 = yes). As already indicated, we coded expectations for nursing home placement as a set of four dummy variables (1%–10%, 11%–50%, 51%–100%, and no response, vs the reference category of 0%).
Enabling factors reflect the fact that although an individual may be predisposed to seeking and accessing health care services, the individual must have some means to facilitate their service utilization. These factors are usually measured by conditions that reflect access to resources that may be instrumental in accessing health care services. We used five binary variables (each coded as 0 = no, 1 = yes) to reflect having Medicaid, Medicare Part A, Medicare Part B, low household income, and low household assets. Although income and assets have been largely insignificant in explaining variations in nursing home placement, we adjusted for these variables to account for the large disparities in income and wealth in the AHEAD sample that may impact expectations and eventual placement. We included Medicaid because at least 60% of nursing home expenditures are covered by states' Medicaid programs, and coverage by Medicaid can facilitate the use of long-term care (Intrator & Mor, 2004).
Given the presence of predisposing and enabling conditions, the level of illness perceived by an individual is the principal determinant of health services use. Generally referred to as the need component, these factors indicate the level of vulnerability a person may experience as a result of declining health status for which a nursing home may provide the best rehabilitative or restorative environment. We used a variety of self-rated disease history and functional status measures to adjust for need. We tapped self-rated health with three dummy variables (very good, good, and fair/poor vs excellent as the reference category). In the literature, most significant results find that worse self-rated health as indicated by "fair" or "poor" strongly predicts eventual nursing home use among older adults (Miller & Weissert, 2000). Our disease history markers (each coded as 0 = no, 1 = yes) included any prior heart condition, any history of cancer, incontinence experienced in the past 12 months, and any hip fractures. We included having any limitations with ADLs or instrumental ADLs (IADLs), traditionally strong predictors of nursing home placement (Wallace et al., 1998), as binary variables (0 = none, 1 = one or more limitations). We measured cognitive function by using a self-assessment of memory, immediate and delayed recall of 10 words, and other questions from the Telephone Interview for Cognitive Status (Brandt, Spencer, & Folstein, 1988). Scores ranged from 1 to 35, with 35 being the highest. We collapsed these scores into a set of two dummy variables reflecting the point where a maximum contrast was observed, with the inclusion of missing scores as a separate category (15–35 and no response vs 1–14 as the reference category).
Analytic Models
First we estimated a linear model to examine the impact of standard placement covariates on expectations, where the probability estimates are treated as continuous. Then we used multinomial logistic regression to further explore expectations, standard covariates, and the Race x Gender interaction. Finally, we estimated a binary logistic regression model for placement with expectations added as a covariate in addition to other standard explanatory variables. We obtained crude and adjusted effects to fully examine the relationships between all of the covariates and nursing home placement. We performed analyses by using standard statistical software packages.
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When the no-response category is compared with the 0% expectations group, similar differences appear. Predisposing characteristics show that age is associated with no response compared with 0%, with AORs of 1.67 (p <.01) and 1.89 (p <.01) for 75–84 years and 85 and older age groups, respectively. Men are less likely to not respond than to report 0% (AOR = 0.65, p <.01), and Black men as indicated by the interaction term are more likely to not respond than to report 0% (AOR = 1.71, p =.01) than the additive effects for race and gender would have suggested. Race did not have a significant additive effect across response levels. Residence in the South (AOR = 1.57; p <.01) or Central (AOR =1.56; p <.01) regions and nursing home use in the past year (AOR = 2.48; p =.01) were all associated with higher odds of nonresponse compared with 0% expectations. Being a high school graduate (AOR = 0.77; p <.01) and having children (AOR = 0.75; p <.01) and helpers (AOR = 0.79; p =.01) are associated with lower odds of nonresponse compared with 0% for predisposing factors. The need characteristics indicate that individuals who report their health as good or fair–poor are more likely to not respond compared with reporting 0% expectations, with AORs of 1.82 (p <.01) and 1.99 (p <.01), respectively. Limitations in IADLs (AOR = 1.37; p =.01) are associated with higher odds of nonresponse. Individuals with higher cognitive function are more likely to respond (AOR = 0.62; p <.01), whereas those who refused to answer the cognitive function questions were also more likely not to indicate their expectations (AOR = 2.53; p <.01).
Modeling the Risk of Nursing Home Placement
Of the 633 persons placed in a nursing home from 1993 to 1998, 83 were African Americans and 550 were White. Women accounted for about 66% of placements in both groups. Of the 633 placements, 272 were temporary, with individuals subsequently reinterviewed as community-dwelling residents at follow-up.
To determine the crude effects of expectations and the other covariates on placement within 5 years, we first estimated a series of univariable binary logistic regression models. We then used a multivariable model to estimate the adjusted effects. As shown in Table 4, twelve variables had statistically significant crude odds ratios that remained statistically significant after adjustment. Individuals who reported expectations of 11%–50% or refused to answer had a higher risk for placement compared with those who said 0%. Older age, prior hospitalization or nursing home use, lower self-rated health, and difficulties with ADLs or IADLs were also significant and strong risks for placement. Being married, having children, being in a particular geographic region, and having good cognitive function were all associated with lower placement risk. In addition, although race did not have a significant crude relationship with placement, the AOR from the final model shows that African Americans were at a significantly lower risk than Whites.
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Interestingly, there were no additive differences in expectations between Whites and Blacks, although many prior studies have found that placement differs by race (Cagney & Agree, 1999; Falcone & Broyles, 1994; Liu et al., 1994). These results and the reported recent trend in the increasing prevalence of African Americans in nursing homes calls for further inquiry into racial differences in nursing home use (National Center for Health Statistics, 2004; Ness, Ahmed, & Aronow, 2004). Furthermore, we did not find significant differences by gender even though most prior studies have found that women—who are overrepresented among the oldest old, widowed, and frail—are at a greater risk for nursing home placement (Coughlin, McBride, & Liu, 1990; Greene & Ondrich, 1990; Miller & Weissert, 2000).
Although it has been largely ignored in the literature, the Race x Gender interaction was significant when we contrasted the nonresponse versus 0% response categories in the multinomial multivariable logistic regression model of expectations (see Table 3). However, we observed no significant results when we added three-way Race x Gender x Expectations interactions (not shown), confirming that the level of expectations has the same effect on nursing home placement regardless of race or gender.
Nursing home use prior to baseline did not affect risk perception, except among those who chose not to provide their expectations. This result was unexpected, as prior use was assumed to result in higher expectation levels. Although the reasons for this are unclear, these findings cast doubt on the assumption that the definition of nursing home placement is homogenous across individuals. An important distinction about placement in a nursing home prior to baseline is that these were all temporary placements; that is, individuals had to be community dwelling at baseline in order to be enrolled in the AHEAD study. This suggests that when they are asked about their expectations for placement, respondents assumed that subsequent placement would be permanent. Accordingly, further inquiry is warranted into the typology of nursing home placement in light of the different types and lengths of stay individuals may experience.
The significant and consistent relationship between expectations and actual placement suggests that the former may be an efficient and effective summary screening measure for use in identifying elders with elevated risks of nursing home placement. Specifically, individuals who chose not to answer the risk perception question had the highest odds of subsequent placement. Refusing to answer the expectations question can thus be taken as an indicator that further evaluation of the subject's medical risk profile is warranted, much like a "fair" or "poor" response to the traditional self-reported health question is indicative of elevated mortality risk.
The addition of expectations to the list of risk factors for nursing home placement has implications for care planning and policy directives. Policy makers at the state level are interested in refined instruments for determining older adults at risk for placement in order to better plan for resource allocation (Miller & Weissert, 2000). Care planning must take into account cost-effective options in order to accommodate the growing population of older adults who will use long-term care services (Spillman & Lubitz, 2000). In the face of rising nursing home costs for state Medicaid programs, early identification of elders at elevated risk for placement is important for policy formation.
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1 Department of Health Management and Policy, The University of Iowa, Iowa City.
2 Center for Research on the Implementation of Innovative Strategies in Practice, Iowa City Veterans Affairs Medical Center, Iowa City, Iowa.
Decision Editor: Linda S. Noelker, PhD
Received for publication February 22, 2006. Accepted for publication April 5, 2006.
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