This study provides evidence that functional impairment accelerates with time among Americans older than 50 years of age. It lends support to the hypothesis that women not only have a higher level of functional impairment than men but also experience a faster decline in functional status after age 50. In addition, the increase in functional impairment is more accelerated in older age groups than younger age groups. Finally, gender differences in functional decline are more substantial in older age groups than younger age groups. These differences are a result of gender by age variations in the level of functional impairment instead of how fast functional impairment changes. To the best of our knowledge, no other study has focused on the gender by age interaction effects on the trajectory of functional status.
Methodologically, the present study differs from prior studies in that it is based upon multiwave longitudinal data derived from a national sample of Americans older than 50 years of age over an extended period of time. Moreover, by incorporating time-varying covariates and their changes during two adjacent waves, we analyzed how functional status evolves over time in a more dynamic fashion. Previous research was largely cross-sectional (e.g., Denton, Prus, & Walters, 2004
; Verbrugge, 1989
) or focused on health transitions between two points in time (e.g., Anderson et al., 1998
; Strawbridge, Camacho, Cohen, & Kaplan, 1993
). Even when these studies used multiwave longitudinal data, they often drew the data from limited locations or narrower age ranges (e.g., Beckett et al., 1996
; Maddox & Clark, 1992
; Mendes de Leon et al., 2005
The present study complements prior studies based on cross-sectional data and health transitions by offering quantitative estimates of the parameters of the growth curve for functional health across gender and age groups. Whereas prior studies document that women have worse functional status than men and are more likely to experience functional decline than men, we are able to depict gender differences in terms of the level and the speed of change over a more extended period of time. At the same time, we are able to show that the trajectory of functional status is significantly more accelerated in older age groups than younger age groups. Although this result is consistent with the perspective of compression of morbidity, one needs to exercise caution because age and cohort effects are confounded in a time-based analysis such as ours.
Why is the gender gap in changes in functional status smaller in younger age groups than in older age groups? At least three mechanisms may account for this observation. First, improvement of women’s SES—such as education, occupation, income, and wealth—may lead to a significant reduction in functional impairment (Guralnik, Land, Blazer, Fillenbaum, & Branch, 1993
; House et al., 2005
). Indeed, younger cohorts of women fare better than older cohorts in terms of educational attainment, employment status, and division of household labor, and the large gender gaps that once existed have significantly diminished or disappeared altogether (Bae, Choy, Geddes, Sable, & Snyder, 2000
). Nevertheless, the gender gap is far from erased for even the youngest cohorts in the HRS.
Second, the prevalence of chronic diseases has been increasing, and the rate of this increase may differ between men and women. Based on three repeated observations of a national sample of Americans aged 65 and older during the period 1986–1994, Kahng and associates (2004)
observed that the rate of increase in chronic diseases was greater among men than women. Analyzing the prevalence of diseases between 1984 and 1994 among Americans 70 years of age or older, Crimmins and Saito (2000)
found a significantly greater increase in most of the diseases among men than women.
Third, the effects of chronic diseases on functional status have become less debilitating (Freedman & Martin, 2001
). If the diminishing effects are greater among women than men, then the gender gap may become smaller in younger age groups. Indeed, Crimmins and Saito (2000)
reported that the severity of disability among women with most diseases (e.g., heart disease, stroke, and arthritis) has reduced, whereas there has been no reduction among men. Moreover, on the basis of data from the National Long Term Care Surveys, Manton and colleagues (1993
, Table 5) observed that women aged 65 and older experienced greater reduction than men in rates of ADLs and IADLs between 1982 and 1989.
According to our findings, SES and prior health status partially mediate gender and age variations in the changes of functional status. This underscores the view that differences in SES and prior health status are not merely confounders of gender differences in functional status but an essential part of the causal pathway through which gender influences physical functioning. Gender differences in functional status reflect not only biological differences between men and women but also differences in privilege and power entailed in gender identities (Williamson & Boehmer, 1997
). This calls for the examination of the total, direct, indirect, and interaction effects due to gender on health changes in later life.
Although the present study does not focus on ethnic differences in functional health, it offers some interesting observations in this regard. For instance, older Black Americans suffer a higher level of functional impairment as well as a greater rate of decline. In contrast, Hispanics experience a similar rate of functional decline as White Americans. Despite this, Hispanics have a significantly more elevated level of functional impairment. More important, ethnic differences in functional health appear to be explained by differences in SES and prior health. The extent to which gender differences interact with ethnic variations remains to be explored.
Substantively we are not quite sure why a higher level of baseline functional impairment is associated with a lower rate of functional decline. Nevertheless, we may suggest some clues for future inquiries. First, there could be a selection effect. Older adults who have a functional deficit and live in the community may get worse functionally, but the rate of the decline cannot be very rapid. Those with rapid functional decline would be institutionalized or would die quickly and thus would no longer be in the community. Although we have mortality as a control variable in our model, it is conceivable that this may not have eliminated all of the selection bias. Second, there might be significant heterogeneity in how functional status changes that was not explored in the present study. Depending on the underlying causes, as well as individual and environmental factors, disability may begin abruptly, progress slowly, remain stable, and even diminish over time. The average survival time after disability onset is highly variable, and it is not clear which factors determine length of survival (Ferrucci et al., 1996
). Hence, among individuals who are significantly impaired, there is a possibility of a reduced rate of functional decline or even some modest improvement, whereas for those with no functional deficit at the baseline, the only possible change would be to remain functionally intact or to get worse at an accelerated pace. Further research on this is certainly required.
The present study can be improved in several respects. First, we based our analysis on time-based models, i.e., intrapersonal changes over the period of observation; (Alwin, Hofer, & McCammon, 2006
). In such a specification, change is modeled as a function of time since the baseline, whereas in an age-based analysis, age rather than time since baseline is used in estimating the growth parameters. Even with multiple birth cohorts involved, a time-based analysis does not differentiate age effect from cohort effect.
We decided not to pursue an age-based analysis in this study because the HRS data are currently not suitable for the correct identification of cohort effects on changes in functional health. Such an analysis would require members of all cohorts to have been observed at the same ages over an extended period of time (i.e., 40 or more years). Most longitudinal studies, including the HRS, yield only data collected from members of different birth cohorts at different ages over a period of less than 20 years (Miyazaki & Raudenbush, 2000
; Willson, Shuey, & Elder, 2007
; Yang, 2007
). Because different birth cohorts were observed at different ages, cohort and age effects were highly confounded. Although this is well known in a cross-sectional design, it is less obvious that such confounding exists in a longitudinal design with limited duration as well. Attempts to identify cohort effects by using age-based models with such data require extrapolations of intrapersonal changes to unobserved age ranges. This may lead to serious bias.
Second, because respondents of the HRS were sampled in middle and later life, differential mortality had already altered the representativeness of the original birth cohorts before they were eligible for inclusion (George, 2005
). This is often referred to as left truncation
, and it may lead to selection bias. The left-truncated cases sampled at the beginning of the observation period tend to overrepresent low-risk cases in a given cohort, or those who have a greater probability of survival. Furthermore, if the dependent variable of interest (e.g., functional status) is related to the risk, repeated observations over time derive from a biased population. In a sense, the selection process in a longitudinal study is actually a survival process. Currently there is very limited research addressing left truncation in longitudinal data analysis. This is because to control for selection bias due to left truncation, one would require information concerning the survival process from birth (or a very early age) up to the point that the respondents were recruited for the baseline observation. Such data are rarely available (for an exception, see Willson et al., 2007
Left truncation is a salient issue when a dependent variable is highly correlated with the risk of dying and when survival is increasingly selective. Within the context of the present study, left truncation is likely to lead to a higher proportion of healthy respondents being included in the sample. This might underestimate the rate of increase in functional impairment over time. However, survival may be more selective among men than women. If this were the case, more women with poor health than men might be included in the HRS panel. This could lead to overstated gender differences in health. Nevertheless, it is unclear how large such biases are. More research on ways of adjusting for the biases due to left truncation is clearly warranted.
Third, assessing functional status once every 2 or 3 years could miss a significant portion of health dynamics. More frequent observations (i.e., weekly, monthly, or every 3–6 months) can yield much fine-grained data on changes in health and functioning (Hardy et al., 2005
; Verbrugge, Reoma, & Gruber-Baldini, 1994
). Such information is of great clinical and management value in improving the quality and efficiency of health care for the elderly. To synthesize data and knowledge of long-term as well as short-term health changes, the recently developed Bayesian hierarchical changepoint and mixture models could be quite useful (Skates, Pauler, & Jacobs, 2001
Fourth, functional status is but one of the multiple dimensions of health and well-being. The disabling process consists of pathology, impairment, functional limitation, and disability. In contrast, key components of well-being include performance of social roles, physical status, emotional status, social interaction, intellectual functioning, economic status, and self-rated health (Pope & Tarlov, 1991
). Conceivably, researchers can chart health trajectories in terms of all of these dimensions, and, more important, they need to examine how these trajectories vary across gender and age groups. The structural linkages among various dimensions of health and well-being have been of long-standing interest to researchers (e.g., Liang, 1986
). Nevertheless, they have not been cast in a dynamic framework.
Finally, significant gaps remain in researchers’ knowledge concerning gender differences in health. A promising strategy would be to incorporate biomedical factors with psychosocial variables in future research. Genetic factors can explain up to one third of the variations in human life expectancy. Moreover, overall functioning, muscle strength, and gait speed have shown substantial heritability, suggesting genetic variations in the timing of the development of physical impairments (Melzer, Hurst, & Frayling, 2007
). Neither biological nor social research alone can explain the complexity of gender differences in health. Only an integration of these perspectives can lead to the interdisciplinary dialogue and investigations required to close the gaps in the current knowledge (Rieker & Bird, 2005