As you were reading about the Verbrugge and Jette (1994) model, you may have been thinking about Brian’s situation and those of other adults you know. If you and your classmates created a list of all the conditions you believe cause functional limitations

and disabilities in older adults, the list undoubtedly would be long. (Try it and see for yourself.) Indeed, over the years researchers have discovered the same thing and have reported numerous conditions that cause these problems (Boult et al., 1994). But by strategically combining a large representative sample of conditions with sophisticated statistical analyses, this list can be shortened greatly. If these steps are taken, what conditions best predict future problems in functioning?

One answer to this question comes from a clas­sic study by Boult and colleagues (1994), which was designed to identify chronic medical conditions that result in severe functional limitations. They studied nearly 7,000 noninstitutionalized people over age 70 living in the United States at two points in time (1984 and 1988). At each point in time they classi­fied people as being functionally intact, functionally limited because of their inability to perform at least one of seven target activities, or deceased.

An important aspect of this investigation was that the researchers took exercise habits and demo-

graphic, socioeconomic, and psychosocial factors into account. Although the study was not designed as a direct test of the Verbrugge and Jette (1994) model, all these factors are considered important in it. Boult and associates (1994) reported that two chronic conditions were strong predictors of functional limitations: cerebrovascular disease and arthritis. In addition, the findings suggested that coronary artery disease may also be a predictor, but the statistical evidence for this was weaker. The authors concluded that focusing attention on identifying these conditions as early as possible may reduce the incidence of severe functional limita­tions in older adults. Because Brian has arthritis, it is a good bet that he will experience greater dis­ability in the future. In addition to specific chronic diseases, several additional predictive factors for subsequent disability have been identified.

In another classic longitudinal study over three decades, Strawbridge and colleagues (1998) found that smoking, heavy drinking, physical inactiv­ity, depression, social isolation, and fair or poor

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Note: Children under 6 years old were not asked about activities of daily living.

Figure 4.11 Prevalence of disability and the need for assistance by age, 2002 (percentage).

perceived health also predicted who would become disabled in some way.

How Important Are Socioeconomic Factors? Once we have identified the specific conditions that are highly predictive of future functional limitations, an impor­tant question is whether the appropriate intervention and prevention programs should be targeted at par­ticular groups of people. That is, would people who are well educated and have high incomes have the

142 CHAPTER 4
same rate of key chronic conditions as people in lower socioeconomic groups? If not, then people with differ­ent socioeconomic backgrounds have different needs.

One of the best large-scale studies of this question was conducted by Reed and colleagues (1995), who tackled this question by comparing roughly 2,000 res­idents of Marin County, California, over age 65 with the total U. S. population in the same age group. The researchers chose Marin County because it is among the most affluent in California and because it could be

viewed as a healthy community environment. Their findings came as a surprise. As expected, residents of Marin County lived on average longer than the typical American. However, despite their privileged status, the Marin County residents had the same prevalence of disease and disability as the U. S. population at large. The implications of these findings, if further research substantiates them, are sobering. Because of their greater average longevity, people from affluent communities can expect to spend a longer period of their later lives living with disabilities and in need of medical care (Reed et al., 1995); they will have longer dependent life expectancy. This is especially true for women, because of their greater average longevity. Chronic conditions do not appear to be postponed in affluent people; indeed, such people may simply live with disabilities for a longer period of time.

Do Gender and Ethnicity Matter? Throughout this and previous chapters, we have encountered important differences between men and women and between various ethnic groups. Thus we might expect such differences in the area of disability. This is par­ticularly the case when it comes to cross-cultural comparisons involving developing countries that focus on adults’ abilities to perform routine tasks, an important component in the model of health we examined earlier in this section.

In one of the few studies in this area, Rahman and colleagues (1994) compared representative samples of men and women in the United States, Jamaica, Malaysia, and Bangladesh. In making their compari­sons, Rahman and colleagues made corrections for gender differences in mortality and socioeconomic differences. Two of their findings are noteworthy. First, women’s self-reported health was worse in all the countries studied. Second, self-reported health problems were much more prevalent in the devel­oping countries than in the United States. Rahman et al.’s (1994) findings indicate that gender makes a difference in health and that the differences between men and women hold up across selected cultures. Although access to health care and lifestyle factors are likely explanations, it is too early to know for certain how these factors create the observed differences.

Of additional concern to researchers is whether ethnic groups differ from each other. In a large study of more than 5,100 older African American and European American men and women, Johnson and Wolinsky (1994) used the concepts of pathology, functional limitation, and disability to predict peo­ple’s perceived health. Several of their findings are important. First, they discovered that the compo­nents of some scales used to measure such things as ability to care for oneself had different measurement properties for each group (e. g., African American men versus women, European American men ver­sus women, African Americans versus European Americans). This means that the scales may be measuring different things in different groups, mak­ing it very difficult to generalize findings from one group to another. However, some scales (e. g., lower body disabilities) were equally valid across ethnic groups. Second, Johnson and Wolinsky found sev­eral gender differences, especially in the European American group. For both ethnic groups, women’s perceived health status was predicted by both the ability to perform several basic functions (e. g., per­sonal care) and disability involving body mobility, whereas men’s perceived health status was predicted mainly by ability to perform basic functions. In the European American group, ability to perform complex daily tasks, such as managing money, was more predictive of men’s than of women’s perceived health status.

Rahman et al.’s (1994) cross-cultural findings and Johnson and Wolinsky’s (1994) results point to important gender, ethnic, and cultural differences in health, as well as differences in which specific aspects of chronic conditions, functional limitations, and disabilities predict what people perceive their health status to be. Such differences must be taken into account in designing intervention programs; a one-size-fits-all approach will not be equally suc­cessful across these different groups of people.

Concept Checks

1. What factors should be included in a model of adults’ disability?

2. How is functional health determined? What are the major issues in ADLs and IADLs?

3. What are the primary causes of disability in older adults?

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