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a Department of Sociology, Arizona State University, Tempe
Correspondence: Kathleen M. Mathieson, MA, Arizona State University, Department of Sociology, P.O. Box 872101, Tempe, AZ 85287-2101. E-mail: kmathieson{at}asu.edu.
Decision Editor: Laurence G. Branch, PhD
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Abstract |
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Key Words: Elderly adults • Disability • Functional adaptations
The significance of the aging of the U.S. population has been stated repeatedly. It is estimated that by the year 2030, the number of older Americans will nearly double, and older adults will make up about 20% of the entire U.S. population (Abeles, Gift, and Ory 1994
, p. 1). Such a demographic trend brings with it many issues and challenges for health and health care; although people are living longer, they are also living with more chronic health conditions and disabilities (Zimmer and Chappell 1994). These developments have implications for both the formal health care system, such as increased ambulatory health care and short-term hospitalization (Weiss and Lonnquist 2000), and informal health care, such as increased provision of elderly care by adult children and greater use of home health care services.
Although overall chronic disability among older age groups has decreased in the last few decades (Manton and Stallard 1995, Manton and Stallard 1996), management of disability and maintenance of functional independence among elderly adults remain salient issues because of the growing proportion of older age groups. Increasingly, research has emphasized the importance of health promotion (Nicholas 1993) and maintenance of functional independence (Zimmer and Chappell 1994) in older adults. Informal health care practices have increasingly been cited as a way of maintaining health and functioning among elders while simultaneously reducing medical expenditures (Edwardson, Dean, and Brauer 1995; Jirovec and Kasno 1993; Kart and Engler 1995).
In this study, we examined a specific type of informal care based on one of the three domains conceptualized by the Self-Care Assessment of Community-Based Elderly Study (DeFriese, Konrad, Woomert, Kincade Norburn, and Bernard 1994, pp. 102–103): functional adaptations that promote functional capability, including home modifications and equipment use for mobility or activities of daily living (ADLs).
We focus on the importance of health status and financial resources (i.e., need and enabling characteristics) in determining the practice of home modifications and equipment use for mobility or ADLs. Building on the health behavior model (HBM), we hypothesized that poor health and physical limitations have positive effects on the practice of functional adaptations because they reflect a necessity for such adaptations. More importantly, we hypothesized that income and health insurance have positive effects on the practice of functional adaptations, as these financial resources can enable elders to make adaptations.
Elderly persons often experience decreases in functional ability and greater difficulty in performing everyday tasks (Verbrugge, Rennert, and Madans 1997). The importance of functional adaptations in moderating this increasing difficulty has been stated frequently. For instance, Manton, Corder, and Stallard 1993(p. 176) found that from 1982 to 1989, dependence on equipment, both by itself and in combination with personal assistance, increased, whereas institutionalization rates were stable, and mortality and chronic disability prevalence rates declined. They concluded that incentives for equipment use among elderly people can increase their social autonomy. Similarly, use of equipment assistance has been shown to be effective for reducing and resolving limitations, and, compared with personal assistance from others, allows elders to maintain a greater sense of self-sufficiency (Verbrugge et al. 1997). The benefits of functional adaptations are not only realized at the individual level, but have also been shown to affect medical expenditures. In a recent study, Stearns and colleagues 2000 found that certain lifestyle practices and functional adaptations among elderly adults were associated with reductions in Medicare expenditures.
In sum, functional adaptations are related to many positive outcomes for seniors, as well as concomitant reductions in health care expenditures and less need for institutionalization. Thus, research has begun to focus on social factors associated with the likelihood of using adaptations such as assistive devices (Gitlin, Schemm, Landsberg, and Burgh 1996; Mann, Hurren, Tomita, and Charvat 1995; Verbrugge et al. 1997; Zimmer and Chappell 1994), home modifications, and personal assistance from others (Kincade Norburn et al. 1995; Stearns et al. 2000). Although these studies are informative, more research is needed to increase our understanding of patterns of functional-adaptation use. Knowledge about these patterns can help inform interventions and education aimed at increasing functional ability and independence among elderly people.
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Need and Enabling Characteristics |
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TOPAbstractNeed and Enabling...MethodsResultsDiscussionReferences |
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HBM, developed to help explain trends in health services use, has been applied to several facets of informal care (e.g., Kart and Engler 1994, Kart and Engler 1995; Mathieson 2000). Initial attempts have shown that the behavioral model is useful for studying the use of assistive devices among elderly adults (Gitlin et al. 1996; Zimmer and Chappell 1994).
Under the behavioral model, predisposing characteristics are factors existing prior to the onset of illness that may contribute to an individual's propensity to use health services, enabling characteristics refer to individual resources (income, health insurance status) and community resources that allow individuals to access services, and need characteristics refer to both perceived and evaluated health status (Andersen and Newman 1973).
This study builds on growing research using the behavioral model to examine trends in informal health practices. We focused specifically on the importance of health status and financial resources (i.e., need and enabling characteristics) in predicting the use of functional adaptations. We chose to concentrate on need and enabling characteristics for two reasons. First, past research has found support for the additive hypothesis which states that increased need translates into increased use of adaptations (Gitlin et al. 1996; Hartke, Prohaska, and Furner 1998; Zimmer and Chappell 1994). Second, certain adaptations or devices may be costly and may not be covered by Medicare. Whereas other studies focusing on functional adaptations as an outcome have used income level as a predictor (Hartke et al. 1998; Kincade Norburn et al. 1995; Zimmer and Chappell 1994), it has typically been included as a demographic control measure. We sought to explore the enabling role of financial factors in more depth by including not only an objective income measure, but also several subjective measures relating to respondents' perceptions of income sufficiency. Additionally, we examined the potential enabling role of supplemental health insurance for elders who want or need to implement functional adaptations. This expanded examination of enabling characteristics builds on intriguing new research showing that extraindividual factors such as access to care and health insurance are associated with greater odds of survival and reduced likelihood of transitioning from independence to disability among Medicare beneficiaries (Porell and Miltiades 2001). We are building on this new research studying the effects of extraindividual enabling factors and making a significant innovation in that our outcome of interest is use of functional adaptations instead of functional ability.
As stated, our hypotheses were that (a) poor health and physical limitations have positive effects on the practice of functional adaptations because they reflect a necessity for such adaptations and (b) income and health insurance have positive effects on the practice of functional adaptations because they can enable elders to make adaptations.
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Methods |
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). A stratified random sample of noninstitutionalized Medicare beneficiaries 65 years of age and older, drawn from 50 primary sampling units (38 urban and 12 rural) was obtained. The sample was stratified by gender and age in the final stages, with oversampling of the oldest old (85 years old and older) to facilitate subgroup analysis, and was selected proportional to the urban/rural distribution of the elderly population. This sample is unique in that it is representative of the total noninstitutionalized elderly population of Medicare beneficiaries in the contiguous United States (Kincade Norburn et al. 1995). Questions pertained to the type and extent of self-care behaviors for ADLs, management of chronic conditions (through self-care activities, equipment use, and environmental modifications), medical self-care for acute conditions, and health promotion/disease prevention. Social support, health services use, and sociodemographic/economic variables were also included.
Dependent Variable Measures
Home Modifications.
Respondents were asked about changes that they may have made to their homes in the last 12 months. Changes included rearranging furniture, keeping things within easy reach, and installing more telephones. Although we considered coding this measure dichotomously (used any home modifications vs. used none), preliminary results showed that there were substantial differences between elders who used different numbers of modifications. To capture these differences, we used multinomial logistic regression (see analysis section) to contrast those using one, two, and three or more modifications with those using zero modifications. The average number of home modifications made was 2.46 (SD = .97).
Equipment Use for Mobility or ADLs.
Respondents were asked whether they had used 20 types of equipment for mobility or ADLs in the past 12 months, including lighter weight housecleaning equipment, bath stools, canes, and walkers. As with home modifications, there were differences in elders who used different numbers of equipment types. This measure was thus coded into the same categories as home modifications. The average number of equipment types used was 1.73 (SD = 1.15).
Independent Measures
Table 1 details the means, standard deviations, and coded values for the independent measures.
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Enabling Characteristics.
The enabling characteristics included five indicators of financial status—1989 family income, whether the respondent receives any income in addition to Social Security, how well income takes care of basic needs, whether the respondent has enough to buy little extras, and whether the respondent has supplemental health insurance. Income was measured using categories that ranged from 1 to 12 (see bottom of Table 1 for details). Because of a high number of missing cases on income (about 20%), missing values were replaced with the mean value for the entire sample. Sensitivity testing was conducted by running statistical models first using income with the mean replacing missing values and second replacing missing income with means computed on the basis of gender, education, and race. There were no significant differences between models. The three additional income variables were included to give a more expanded picture of the role financial resources, as well as individuals' perceptions of the adequacy of their resources, play in determining functional adaptations. Income in addition to Social Security was a dichotomous measure, as was having enough to buy little extras. The measure for how well income takes care of basic needs was ordinal, with responses poorly, fairly well, and very well. Supplemental health insurance was measured dichotomously, and referred to health insurance that respondents possess in addition to Medicare. In addition to these individual enabling characteristics, one community-enabling characteristic, rural residence, was included as a dichotomous measure.
Need Characteristics.
Need characteristics are an important component of the behavioral model; for a person to seek care, she or he must first have or perceive some medical need. This study used one question on perceived health status, "How is your health at the present time?" with responses of 1 = excellent, 2 = very good, 3 = good, 4 = fair, and 5 = poor. Three ADL-limitation measures were used as objective indicators of health status: basic (BADL), mobility (MADL), and instrumental (IADL). BADLs included eating, dressing, bathing, and maintaining continence; MADLs included getting to the toilet, transferring from bed or chairs, getting outside, and walking; and IADLs included using a telephone, managing money, preparing meals, doing light housework, shopping, and doing heavy housework.
Chronic conditions were also used as an objective measure of need. This item was measured through two series of questions. Respondents were first asked if they had experienced several conditions in the past year: arthritis/rheumatism, diabetes, asthma/emphysema/bronchitis, gastric/peptic ulcers, and anemia. Second, respondents were asked if they had ever experienced any of the following conditions: osteoporosis, a broken hip, hypertension, coronary heart disease/angina pectoris/heart attack, stroke, cancer, glaucoma, and cataracts. A sum of the total illnesses each respondent had experienced was created through these items, with possible values ranging from 0 to 13. Because only about 11% of cases reported five or more of these, chronic conditions were recoded into the categories 0 = zero, 1 = one, 2 = two, 3 = three, 4 = four, and 5 = five or more.
As other researchers have noted, some overlap among the components of the behavioral model can be expected. If independent variables are correlated at the .85 level or higher, however, they are redundant and should not be included in the same analysis (Kline 1998). To test for possible multicollinearity, we examined bivariate correlations among all independent variables, and none of the correlations reached the .85 level. Although this method cannot completely rule out multicollinearity, it did suggest that multicollinearity was not present.
Analysis
Multinomial logistic regression was used to examine both functional adaptations. This type of analysis has the capacity to analyze outcomes with more than two discrete categories and describes the odds of response in one category instead of another (Agresti 1996, p. 205). For a categorical variable with J categories, J - 1 regression equations are estimated. In this case, the number of categories for each outcome variable was four, so three regression equations were estimated (displayed in the columns of Table 2 and Table 3 ). We used SPSS (SPSS Inc., Chicago, IL) which is capable of fitting all three equations simultaneously.
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). Interpretation may be done through odds ratios (exp{ß}) or by converting coefficients into a percentage change in odds [(exp{ß}-1) x 100]. For instance, a coefficient of .25 in the equation comparing those who used one versus zero home modifications could be interpreted in two ways: a one-unit increase in the independent variable multiplies the odds of using one home modification instead of zero by 1.28 (exp{.25}). Or, a one-unit increase in the independent variable increases the odds of using one home modification instead of zero by 28% [(exp{.25}-1) x 100]. The type of interpretation used is a matter of preference, and we use both types in discussing results.
The Nagelkereke pseudo R2 was used to estimate the amount of variance in home modifications and equipment use explained by the predictors. This measure is an adjusted version of the Cox and Snell R2, which is based on the log likelihood for the fitted model compared with the log likelihood for the null model (with no predictors). Although the Cox and Snell R2 has a maximum value of less than 1, even for a perfect model, the Nagelkerke R2 adjusts the scale of the statistic to cover the full range from 0 to 1 (Nagelkerke 1991). Separate hierarchical analysis was also conducted to determine the relative importance of predisposing, enabling, and need characteristics in explaining each outcome variable. Although the results of the hierarchical analyses are not shown, they are discussed in the context of other findings.
Whereas our first hypothesis states that poor health is directly related to functional adaptations, once a certain level of need is reached, this relationship may diminish. For example, if a person is bedridden, she or he may be less likely to use adaptations compared with someone who simply has trouble moving about, reaching items on shelves, and so on. Put simply, it is possible that the direct effect of poor health is more pronounced for those with better health and less pronounced for those with worse health. Thus, we conducted separate analyses to test whether an inverted 
-shaped relationship existed between need characteristics and each outcome. This was done by separately entering quadratic terms (e.g., BADLS x BADLS) for each need characteristic into the analysis for home adaptations and equipment use, respectively. Quadratic terms show whether the effects of a variable are constant or whether they change across levels of that variable. These results, too, are not shown but are discussed.
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Results |
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The effects of the enabling characteristics in Table 2 are interesting as well as somewhat counterintuitive. Household income had a significant negative effect in Equation 2. That is, higher household incomes decreased the odds of making home modifications. This is the opposite of what we expected. Two other income-related variables, however, were significant and positive. Those who believed their income took care of their basic needs had higher odds of using modifications (Equation 3), as did those who had enough income to buy little extras (Equation 1). These results seem contradictory. It may be that the actual level of household income is less important than respondents' perceptions of the adequacy of their income; the perceived measures had much more substantial effects (more robust coefficients) than did household income. Additionally, given that the survey concerned only the past 12 months, it is possible that those with adequate income were able to implement adaptations earlier.
Supplemental insurance had significant positive effects on home modifications in Equations 1 and 3, lending further support to the enabling role of financially related resources. In Equation 3, being a rural resident increased the odds of using three or more versus zero modifications by 46%. Although several enabling characteristics had significant and robust effects on home modifications, in hierarchical analysis enabling characteristics explained only about 2% of the variance in home modifications over and above that explained by predisposing characteristics (Nagelkerke R2 = .088). Thus, enabling characteristics as a whole did not add substantial explanatory power. This is consistent, however, with past research using the HBM; enabling characteristics typically explain only a small proportion of health behaviors (Andersen and Newman 1973; Wolinsky and Johnson 1991).
The results for the need characteristics show that chronic conditions, perceived health, and MADL limitations were significant for all numbers of home modifications. In Equation 3, a one-unit increase in chronic conditions multiplies the odds of using three or more modifications instead of zero by 1.17; a one-unit increase in poor perceived health (e.g., from fair to poor) multiplies the odds of using three or more modifications instead of none by 1.31; and a one-unit increase in MADL limitations multiplies the odds of using three or more home modifications instead of none by 1.68. Similarly, IADL limitations had significant positive effects in Equations 2 and 3. In addition, need characteristics were very important in predicting use of home modifications; in hierarchical analysis the explained variance rose from about 9% to 24% (Nagelkerke R2 = .236) when need characteristics were added to predisposing and enabling characteristics.
In the separate analysis to test whether the effects of need characteristics were linear, we found that BADL, IADL, and MADL limitations actually had inverted -shaped effects. Quadratic terms for these ADL measures (e.g., ADLs x ADLs) were statistically significant and negative, whereas the nonquadratic terms (e.g., ADLs) remained significant and positive. Thus, the direct effects of these ADL limitations on home modifications were diminished at higher levels of limitation.
Equipment Use for Mobility or ADL
Table 3 details the multinomial regression results for equipment use. Age and gender had significant effects regardless of the number of equipment types. Older age was associated with increased odds of equipment use, as was female gender. Education and retirement status were significant in Equation 1. Having at least a high school education decreased the odds of using one versus zero equipment types by 20%, and being completely retired increased the odds of using one versus zero equipment types by 43%. In hierarchical analysis, predisposing characteristics accounted for about 12% of the variance in equipment use (Nagelkerke R2 = .117).
The effects of enabling characteristics on equipment use were similar to those for home modifications. Whereas household income was significant and negative (Equation 3), having income in addition to Social Security had positive effects and was significant across equations. Again, differences in the magnitude of the effects are worth noting. In Equation 3, for example, a one-unit increase in household income decreased the odds of using three instead of zero equipment types by only 7%, compared with a 55% increase in odds caused by having income in addition to Social Security. Furthermore, having supplemental health insurance appears to act as an important enabling characteristic for the use of equipment. In Equation 3, for example, possessing insurance in addition to Medicare multiplied the odds of using assistive equipment by 1.76. Similar results between home modifications and equipment use emerged with regard to explanatory power. Enabling characteristics explained only 2% of the variance in equipment use over and above that explained by predisposing characteristics (Nagelkerke R2 = .136).
As with home modifications, the importance of need characteristics for equipment use is clear. Chronic conditions, BADL limitations, IADL limitations, and MADL limitations all had significant positive effects in at least two equations. Furthermore, in hierarchical analysis need characteristics accounted for an additional 15% of the variance in equipment use beyond that explained by predisposing and enabling characteristics combined (Nagelkerke R2 = .294). Similar to the findings for home modifications, two need characteristics, MADL and IADL limitations, had inverted -shaped effects. Thus, the direct effects of these ADL measures on equipment use were diminished at higher levels of limitation.
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Discussion |
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TOPAbstractNeed and Enabling...MethodsResultsDiscussionReferences |
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Considerable support was found for our first hypothesis that poor health and physical limitations have positive effects on the practice of functional adaptations. Many health-related variables (i.e., need characteristics) are important in predicting functional adaptations, including chronic conditions, perceived health, and MADL limitations for home modifications and chronic conditions, IADL limitations, and MADL limitations for equipment use. Moreover, a sizeable portion of variance in functional adaptations is explained by these need characteristics, as is in keeping with past research using the behavioral model.
Although our findings lend credence to the additive hypothesis that increased need translates into increased use of adaptations (Gitlin et al. 1996; Hartke et al. 1998; Zimmer and Chappell 1994), they also inform this hypothesis by showing that the effect of need is not linear, but diminishes at higher levels of impairment. That is, whereas the effects of ADL limitations on functional adaptations remain positive, these effects are more substantial for elders with less disability and less substantial for elders with more disability. From a public health perspective, these findings can inform efforts to increase functional independence among seniors through the use of home modifications and/or equipment use. Specifically, promotion of functional adaptations may benefit from using a proactive approach and targeting those who have few or no limitations, as these lifestyle changes may help seniors adapt to additional limitations in the future.
Enabling characteristics such as health insurance and income did not add substantially to the explained variance in either type of functional adaptation. This, however, is consistent with the findings of past research using the behavioral model. That is, enabling characteristics typically do not account for much of the variance in health behaviors. In addition, although some findings are counterintuitive, the effects of income and health insurance on functional adaptations are intriguing and offer some support for our second hypothesis that enabling characteristics have positive effects on the practice of functional adaptations. For both home modifications and equipment use, household income had significant negative effects in one equation, whereas other income-related factors (income takes care of basic needs, have enough to buy little extras, and income in addition to Social Security) had significant positive effects. The magnitude of the household income effects, however, is much smaller than that of the other income-related variables. As noted, the effect of more subjective income measures, such as a belief that income takes care of basic needs, may be more important than actual income level. There are other possibilities as well. For example, gender may interact with income. That is, older women are more likely to use adaptations and have lower incomes than older men do. Conversely, older men are less likely to use adaptations and have higher incomes than older women do. We tested an interaction between gender and income, however, and none of the results were statistically significant. It is also possible that older men, who have higher incomes and are more likely to have a spouse that is still living, have less need for adaptations because they get help from their spouses. This could contribute to the negative relationship we found between income and adaptations. Most likely, the relationships are complex and potentially involve multiple factors such as age, gender, marital status, living arrangements, and financial resources. Further research is needed to explore the relationship between household income and functional adaptations in more depth.
Supplemental insurance does appear to act as an enabling factor for both home modifications and equipment use, although for home modifications the relationship is not consistent across equations. This could be due to the fact that some home modifications are easily made at no cost, such as rearranging furniture or moving items to lower shelves, whereas equipment must almost always be purchased, and is more often covered by health insurance. These results build on recent findings on the role of supplementary health insurance in decreasing the likelihood of becoming ADL disabled and improving survival outcomes among Medicare beneficiaries (Porell and Miltiades 2001). Supplemental health insurance appears to act as an enabling factor not only with regard to health and disability status, but also with regard to adaptations that can increase independence among elders.
Some limitations of this study are worth noting. First, the amount of missing data on the income variable is high, and although we strove to handle this problem in a methodologically sound way, we cannot conclude that we fully solved this problem. It may be that some of the contrasting findings on objective and perceived financial measures are due to the large amount of missing data on income. In addition, some limitations arise from the fact that secondary data were used. For instance, there are independent measures included in other studies that use the HBM that were not available in the present data, such as health beliefs, attitudes toward health services, price of health services, and provider-to-population ratios. If we were to design a study with newly collected data, we would include the complete set of independent measures associated with the HBM. Similarly, the data used in this study are cross-sectional, and thus provide only a "snapshot" of statistical relationships. Furthermore, it is possible that some functional adaptations were made prior to the 12-month period respondents were asked about and were thus missed in this study. For these reasons, we feel longitudinal investigations have great potential for shedding more light on the complex factors relating to functional adaptations and how these affect health and functioning among elderly people.
The findings of this study have several important implications for public health and future research. First, considering that the effects of ADL impairments on home modifications and equipment use are diminished at higher levels of impairment, promotion of functional adaptations may benefit from targeting elders who have few or no limitations. With this type of proactive approach, elders may implement or be aware of functional adaptations that can help them cope with additional limitations in the future. Second, given our provocative findings on the effects of financial enabling characteristics, we suggest that future research continue to explore how both objective and subjective economic resources influence the use of functional adaptations among elders. In particular, because Medicare Part B helps pay for certain types of medically necessary equipment, future research should examine to what extent this coverage removes financial barriers and enables individuals to implement assistive equipment adaptations. Finally, programs aimed at promotion of functional adaptations to improve the quality of life among elders will likely be more effective if financial factors as well as level of need are taken into consideration.
Practice Concepts
The Forum
Book Reviews
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Acknowledgments |
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Received for publication March 23, 2001. Accepted for publication September 28, 2001.
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