Worldwide, the number of people living with disability and the average number of years lived with disability is increasing (GBD 2015 Disease and Injury Incidence and Prevalence Collaborators, 2016). In the United States, 16 million adults ages 65 and older live in the community with a disability—defined broadly as a sensory, cognitive, mobility, self-care, or independent living limitation (Erikson et al., 2017); another 1 million older adults with severe limitations live in nursing home settings (Freedman and Spillman, 2014a). One in four older adults successfully accommodates losses in physical capacity and continues to carry out daily activities without difficulty or assistance (Freedman and Spillman, 2014b). Yet for others, late-life disability may result in undesirable outcomes including the loss of one’s ability to live independently, the need for family members to provide care, and reduced well-being. Given the aging of the continued Baby Boom population, the number of older Americans living with disability is projected to increase dramatically in the near future (Institute of Medicine, 2007). Long-term services and supports for this population are also projected to increase sharply between now and 2050 (Congressional Budget Office, 2013).
1 Institute for Social Research, University of Michigan.
2 Prepared for the Committee on Population Conference on Future Directions of the Demography of Aging, August 17–18, 2017, Washington, DC. Funding was provided through a grant from the National Institute on Aging (U01-AG032947). The views presented are those of the author alone and do not represent those of the University of Michigan or the funding agency.
Understanding the population-level implications of late-life disability in the context of an aging population was once considered the purview of a subfield of demography referred to as medical demography (Manton and Stallard, 1994). Rooted in classical demography and bioactuarial sciences, medical demography involved estimating biologically plausible models of chronic disease, disability, and mortality. Early research on this topic focused on the shifting age distributions of mortality and implications of interactions between mortality and disability processes for the size and health of the older population (Land and Yang, 2006). The field has also given rise to a substantial literature on disability trends, including the seminal finding that the prevalence of activity limitations in the United States declined during the 1980s (Manton et al., 1993) and 1990s (Manton et al., 1997; Manton and Gu, 2001). More recent studies have documented that such declines did not continue into the first and second decades of the 21st century (Freedman et al., 2013) and the percentage of nonelderly adults reaching late life with limitations in place appears to be increasing (Martin and Schoeni, 2014).
Much of the literature to date has focused narrowly on disability defined as activity limitations. As conceptual frameworks and measures of disablement have evolved, a better understanding is emerging of the constructs that constitute late-life disablement; its physiological, environmental, and behavioral underpinnings; its progression with age and relation to mortality; and consequences for individuals and their families. Advances in modeling individual trajectories have opened up new avenues for estimating biologically and environmentally plausible models that recognize compensatory strategies adopted by older adults as they age. Trend analyses can now distinguish changes in underlying capacity from how older adults accommodate such declines.
Given these developments, late-life disablement is a topic central to the growing field of the demography of aging. The next section reviews conceptual and empirical issues, including definitions of disability and corresponding measurement advances. Although the centrality of cognition to the disablement process is recognized here, another chapter in this volume covers measurement issues related to cognitive impairment and dementia in more detail. The third section provides a broad portrait of late-life disability, drawing upon the 2015 round of the National Health and Aging Trends Study (NHATS), an annual study of U.S. older adults that began in 2011 and has an explicit focus on late-life disability trends and trajectories (Kasper and Freedman, 2014). Section four focuses on advances in modeling individual-level disability trajectories and in tracking population-level trends in the United States. The final section provides an overview of future directions.
THE LANGUAGE AND MEASUREMENT OF DISABILITY
The literature on late-life disability is characterized by a history of competing conceptualizations, inconsistent terminology, and ambiguous measurement. Over a dozen definitions of disability are in use by U.S. federal government programs, complicating the task of monitoring disability at the population level (Gregory, 2004). In this chapter, the term “disability” is used broadly to include four domains: impairments in body functions or structures; reduced physical, sensory, or cognitive functioning; difficulty carrying out self-care or household activities by oneself, receipt of help, or use of compensatory strategies that signal the need for help; and restrictions in participation in productive, social, or community life. As described below, other approaches persist that define disability more narrowly as activity limitations.
The Language of Disability
Historically, two distinct models—the medical model and the social model—governed society’s approach to addressing disability (Iezzoni and Freedman, 2008). For the 19th and most of the 20th century, the medical conceptualization of disability dominated. Disability was viewed as solely caused by disease, trauma, or other health conditions and was therefore under the purview of physicians. Their aim was to cure the underlying disease or adjust the individual’s behavior to compensate for loss of functioning. The medical model’s emphasis on the underlying medical cause of disability is evident in several U.S. programs (e.g., worker’s compensation, disability insurance), which classify applicants by the condition causing their disability. The medical model is limited for studying late-life disability because there is no explicit recognition of the social and environmental context of disability. In contrast, the social model of disability, which gained prominence in the 1970s and 1980s, views disability as a socially created problem and has as its aim the full integration of individuals into society. The social model emphasizes that disability is not an attribute of an individual but created by environmental barriers. Consequently, this model’s focus is on the removal of barriers to participation.
The Nagi Model
Recognizing both perspectives, a disablement model was suggested by Nagi (1965), adopted by the Institute of Medicine (1991), and advanced by Verbrugge and Jette (1994) and others. The Nagi model distinguishes
an individual’s underlying capacity to carry out tasks from socially defined role limitations. Disablement in this framework consists of four stages: pathology, impairment, functional limitations, and disability. Pathology is defined at the organ level and refers to compromised function as the result of a chronic or acute condition or an injury. Impairment is defined at the system level and refers to loss of system function. Functional limitation is defined at the level of the whole person and refers to limitations in physical or mental actions. Finally, disability refers to the final stage in the process: the inability to carry out a socially defined role, such as work or self-care or household activities.
The Nagi model was the dominant model used in gerontological research for many decades. Researchers who drew upon the framework benefited from the availability of well-developed measures for each stage (Guralnik and Ferrucci, 2009). They were able to accumulate a body of evidence demonstrating, for example, the importance of mobility limitations for the onset of disability and mortality (Guralnik et al., 1995). In addition, geriatricians expanded upon the Nagi framework to make the clinically relevant distinction between experiencing difficulty with an activity versus receiving help from another person (Gill et al., 1998).
Although not explicit, the Nagi model also provided a framework for studying environmental influences on disability (Verbrugge and Jette, 1994). In particular, building on Lawton’s original competence-environment press theory (Lawton, 1986), Verbrugge and Jette theorized that disability occurs when there is a gap between an individual’s capabilities and environmental demands. In this context, assistive devices (e.g., a cane) may alter an individual’s capabilities and environmental modifications (e.g., a grab bar) may reduce corresponding environmental demands. For some activities, individuals may reduce the frequency of such tasks or fundamentally change how a task is carried out, for instance, washing up at the sink instead of climbing in and out of a bathtub. Fried and colleagues (Fried et al., 2001; Weiss et al., 2007) refer to such compensatory strategies as “preclinical” disability, in which changes in behavior (e.g., the frequency or way in which an activity is carried out), not detected with conventional self-reported difficulty, signals an increased risk for needing assistance.
The ICF Framework
Over the last decade, the World Health Organization’s disability model, the International Classification of Functioning, Disability, and Health (ICF; World Health Organization, 2002), has gained prominence. Although initially somewhat slow to be embraced by the U.S. gerontological community (Jette, 2009), the ICF is recognized as providing an internationally agreed-upon terminology for understanding the consequences of health conditions
for participation in society (Institute of Medicine, 2007). Like the Nagi model, the ICF integrates both medical and social perspectives into a framework that reflects biologic, individual, and social processes. Unlike the Nagi model, the ICF defines disability as an umbrella term encompassing multiple domains, rather than as a final stage in the disablement process.
In the ICF, there are four main domains: health conditions, body functions and structures, activities, and participation. The last three domains have negative analogues that constitute disability: impairments, activity limitations, and participation restrictions. Health conditions include diseases, disorders, and injuries. Body functions and structures include basic physiological functions and anatomical parts of the body such as organs and limbs, whereas impairments are considered a significant deviation or loss in body function or structures. Activities are the tasks of daily life, and activity limitations are difficulties an individual has in completing such activities. Participation includes involvement in productive activities such as work or volunteering, in social activities with friends or family, and in community and civic activities. Participation restrictions are defined as problems an individual may experience with these life situations. The framework also explicitly recognizes both personal (internal) and environmental (external) contextual factors. Personal factors include social, demographic, and other background characteristics of the individual, whereas environmental factors include social, physical, and legal environments. These contextual factors influence the entire disablement process.
The ICF approach offers several advantages over the Nagi framework. First, it applies to all individuals regardless of their level of functioning. Second, contextual factors are explicit rather than implied. Third, it recognizes the value of participation in activities that are central to people’s lives beyond self-care and household activities. Finally, the framework recognizes that new (“secondary”) health conditions may emerge as a consequence of the disablement process.
The NHATS Framework
Begun in 2011 with funding from the National Institute on Aging, NHATS is a national panel survey of Medicare beneficiaries ages 65 and older that is specifically designed to promote research to reduce disability, maximize independent functioning, and enhance quality of life at older ages. The sample is refreshed periodically to allow study of national-level disability trends as well as individual-level trajectories. Annual, in-person interviews provide detailed information on the disablement process and its consequences.
NHATS developed an enhanced framework for studying disablement—highlighted in Figure 10-1—that builds upon both the Nagi and
ICF approaches (Freedman, 2009). The main pathway recognizes that health conditions influence body functions and structures, which in turn influence activities and participation. However, the NHATS framework incorporates three distinct features, which open up new opportunities for research (Kasper and Freedman, 2014). First, the framework explicitly distinguishes between the underlying capacity of individuals and the accom-
modations that they make in order to carry out activities. Capacity refers to an individual’s physiological, cognitive, and sensory capabilities that form the building blocks to carry out activities. Accommodations are identified in a separate domain that captures how activities are carried out. Common accommodations include using assistive technology, adapting the physical environment (e.g., adding a grab bar), changing the demands of the activity (e.g., cleaning up at the sink instead of bathing), reducing how often an activity is undertaken, and receiving help from another person. Second, the NHATS framework makes a distinction between the ability to carry out by oneself essential self-care, mobility, and household activities and the extent of participation in valued but elective activities. Third, the framework acknowledges that the physical, social, technological, and service-related environment of an older adult influences the entire process. As described in the next section, NHATS has re-engineered traditional measures of late-life disability to provide researchers with a new set of validated measures mapping to each of these domains.
The NHATS framework fosters testing of theories about disablement from multiple disciplinary perspectives. For instance, it can be used to test psychological and social theories linking compensatory strategies to quality-of-life outcomes such as continued participation in activities of value and maintenance of subjective well-being. It can also be used to test hypotheses about behavioral responses to declines in capacity—for instance, the circumstances under which preclinical disability occurs before reports of difficulty, or the pathways from intrinsic changes in activity performance (e.g., more slowly, less often) to extrinsic changes (modifications to the environment or use of devices). It can also be used to evaluate questions of particular interest to the geriatric research community, such as the link between specific conditions—for example, obesity, sarcopenia, chronic pain—and physical capacity or the biological underpinnings of physical capacity. For demographers, the approach is valuable because it allows fuller characterization of the disablement process and therefore supports estimation of models linking chronic disease, disability, and mortality that are physiologically, environmentally, and behaviorally plausible.
The Measurement of Disability
As the conceptual underpinnings of disablement have evolved, so too have the measures available to researchers. Initially researchers were restricted to activity limitation measures—most often, Katz’s activities of daily living (ADLs) and Lawton and Brody’s instrumental activities of daily living (IADLs) (Katz et al., 1963; Lawton and Brody, 1969)—and Nagi’s functional limitation measures that assessed upper and lower body limitations (Nagi, 1965). Since that time, however, notable advances in the
assessment of disability domains have occurred (National Research Council, 2009). Many have been incorporated into the comprehensive, validated NHATS disability protocol (Kasper and Freedman, 2014), making it possible for the first time to examine for a national sample annual changes in physical capacity, compensatory strategies, activity limitations, participation restrictions, and consequences such as unmet need. Both classical and modern approaches to test development have been used to evaluate the protocol (Freedman et al., 2011; Kasper et al., 2017).
Limitations in Self-Care and Household Activities
ADL and IADL measures were developed half a century ago for use by clinicians evaluating older patients. Difficulty with daily tasks (without help or special equipment) and receipt of help were typically ascertained. However, wording—and meaning—varied considerably across studies. Some protocols asked about whether the older adult needed help; others asked about receipt of help. Some protocols specified without help or special equipment, others were ambiguous. Although widely used for research and programmatic purposes, these items do not provide insight into the physiology of disablement or the role of environmental and compensatory strategies. Such distinctions are valuable for promoting independent functioning in later life (Freedman et al., 2014).
In NHATS, measures of activity limitations have been re-engineered to explicitly measure difficulty by oneself, with devices if used, and have been broadened to include various behavioral adaptations (e.g., assistive device use, environmental modification use, less frequent performance, and receipt of help). The measures have been used to develop a spectrum of accommodation that reflects a hierarchy of underlying physical and cognitive capacity and is more strongly related than age to well-being (Freedman et al., 2014). The spectrum includes four groups, those who either are fully able, have successfully accommodated using devices, remain a target for accommodation (because they have reduced their activity level or report difficulty despite current accommodations), or receive assistance from another person. Previous approaches to measurement in national studies did not allow identification of individuals who successfully accommodated or those who reduced their activity level. In their evaluation of this spectrum, Gill and Williams (2017) confirmed its monotonic gradient in risk of functional dependence and mortality and its success in identifying a very low-risk, fully able group. Their findings underscore that the various adaptive behaviors are unlikely to represent an orderly set of stages and that more research is needed on the complex pathways to dependence and death.
Nagi (1965) developed functional limitation measures to assess the rehabilitation potential of applicants for disability benefits to the federal Social Security program. A team of medical evaluators and the applicant both scored the applicant’s maximum ability using a scale from 0 (no ability) to 7 (no restriction). The physical requirements of the applicant’s job were also scored. The general approach of asking older adults to rate their physical restrictions has been widely implemented in national surveys. Protocols generally include questions about ability to carry out both upper (reaching up, reaching out, grasping) and lower (bending, lifting and carrying, climbing stairs) body movements. Most often respondents are asked about difficulty without help from another person or use of special equipment. Several shortcomings of these measures have been identified (Freedman et al., 2011). First, they cover a relatively narrow range of functioning (for an exception, see Simonsick et al., 2001). Second, some older adults may not carry out a particular activity without their devices (e.g., walking several blocks) and thus are not able to report on level of difficulty. Third, as with activity limitations, the Nagi items rely on subjective assessments of ability.
Increasingly common on surveys of older adults are standardized performance tests (Gill, 2010). Individuals are asked to carry out specific movements while trained observers make ratings using predetermined, objective criteria (Guralnik et al., 1989). One of the most common protocols to measure lower body functioning is the Short Physical Performance Battery (SPPB) (Guralnik et al., 1995). This set of standardized procedures includes a short walking test to evaluate usual gait speed, a series of progressively more difficult balance tests, and an assessment of the individual’s ability to rise from a chair multiple times. The SPPB has been established as a strong predictor of subsequent disability and mortality (Guralnik et al., 1994, 1995) and is able to detect change within individuals (Ostir et al., 2002; LIFE Study Investigators, 2006). Additional performance batteries have been developed to measure upper body functioning, such as grip strength and lung function (Roberts et al., 2011; Fragoso et al., 2008).
NHATS measures physical capacity using an expanded set of self-reported measures adapted from Nagi (Freedman et al., 2011) and a standard battery of physical performance tests (Kasper et al., 2012). The self-reported items assess ability (yes/no) to carry out six pairs of more and less challenging tasks by oneself and without special equipment, if used. The pairs of tasks include walking three and six blocks, going up 10 and 20 stairs, lifting and carrying 10 and 20 pounds, bending over and kneeling down, reaching up and putting a heavy book on an overhead shelf, and grasping small objects and opening a sealed jar. For each pair, respondents
were first asked about the more-challenging task; those who reported being unable or having no opportunity to carry out that task were then asked about the less-challenging task. Rather than use the phrase “special equipment,” NHATS tailored the questions to name devices mentioned earlier in the interview (e.g., “without using your cane” rather than “without special equipment”). The NHATS physical-performance battery includes the full SPPB (usual walking speed, nested balance tests, chair stand tests), along with tests of grip strength and peak air flow. Item Response Theory analyses suggest the two sets of measures are complementary (Kasper et al., 2017). Specifically, the self-reported items discriminate at the lower end of the physical capacity range, whereas the performance tests distinguish across a broader range. A score drawing upon both self-reports and performance tests provides better measurement precision across the full spectrum and appears better suited than either approach alone for studying age-related changes in physical capacity.
The Physical, Service, and Technological Environments
For many years, measures of the home environment, developed primarily for clinical interventions (Gitlin, 2003), have been mostly absent from national health surveys, as have environmental measures related to mobility disability (Satariano et al., 2012). Surveys were also not well equipped to capture the expansion in different types of residential settings, in part because respondents were often not able to accurately report place type or available services. Measures of the technological environment in which tasks are conducted have also been lacking. With respect to the latter, the Internet is a potentially important tool for persons with limited mobility, since a number of household activities—shopping, banking, ordering prescriptions—can be carried out online. And, with the exception of items to measure equipment use, which often produce marked underestimates (Cornman et al., 2005), compensatory strategies have rarely been assessed in a national context.
The NHATS protocol measures the physical, service, and technological environments of older adults. For instance, there are measures of the physical structure of the living environment (e.g., multifloor homes, multiunit buildings) and the existence and addition of environmental features that support functioning (e.g., grab bars, raised toilets, stair glides). An additional questionnaire administered to a facility informant allows NHATS to distinguish the level of services available for the nearly 6 million older adults (15%) living in residential care settings (Freedman and Spillman, 2014a). The technological environment and use of the Internet to carry out social, household, and health-related activities is also assessed (Levine et al., 2016).
Participation in valued activities and participation restrictions are now recognized as integral to disability models (World Health Organization, 2002), but measures in national U.S. studies of health and aging remained limited until recently. For eight activities, NHATS assessed whether the respondent participated in the last month, whether their health or functioning limited their participation, and how important it was to the respondent to be participating in the activity. Given the central role of transportation to participation in community activities, there are also follow-up items on whether a transportation problem limited the respondent’s participation. Activities included socializing in person, attending religious services, attending organized club meetings, going out for enjoyment, caring for another person, working for pay, and volunteering. In addition, a favorite activity was assessed, along with participation in the last month and whether the respondent’s health or functioning limited participation. Analyses confirm that the construct of participation restriction is reliable and distinct from limitations in self-care and household activities (Freedman et al., 2011).
Unmet Need and Adverse Consequences
Although not explicit in existing frameworks, the concept of unmet need has appeared in the social gerontological literature for many years. Earliest measures asked respondents directly whether they “needed more help” with daily activities, making it difficult to interpret findings. Allen and colleagues developed an alternative approach that asks directly about adverse consequences that are the result of unmet need (Allen and Mor, 1997). In NHATS, respondents who had difficulty or received assistance with a given activity were asked if a particular consequence occurred—for instance, not being able to get dressed, go out, or eat a hot meal—because no one was there to assist with the activity or because it was too difficult to perform the activity alone (Allen et al., 2014).
A PORTRAIT OF LATE-LIFE DISABLITY IN THE UNITED STATES
This section provides a snapshot of late-life disability in the United States for several major demographic groups and highlights the distinctive disadvantage of minority women. Although disablement is a fundamentally dynamic phenomenon, understanding cross-sectional differences in the domains that constitute the components of disablement is instructive. Drawing upon the 2015 round of NHATS, the tables provide a nuanced description of the activity limitations experienced by different groups of older adults, along with underlying physical, sensory, and cognitive capac-
ity; the physical, service, and technological environments in which older adults carry out daily activities; and consequences of those limitations. There are many additional factors—such as regional and local variation, factors from early and midlife, and current behaviors—that influence late-life disablement, but they are beyond the scope of this portrait.
Portrait by Stage of Disablement
Focusing on limitations in daily activities, reflected in reports of help from another person or difficulty by oneself (with devices, if used), about 38 percent of older adults have a self-care or mobility activity limitation (see Table 10-1). In addition, about 6 percent of older adults do not report difficulty but carry out their self-care and mobility activities less often than a year ago. Another one in four older adults (28%) report no difficulty, assistance, or change in frequency but use assistive devices when carrying out self-care or mobility activities (“successful accommodation”). Finally, just under 30 percent of older adults are fully able to carry out their self-care and mobility activities. For household activities, 39 percent of older adults have a limitation (have difficulty or receive help related to their health or functioning), about 8 percent have reduced the frequency of their activities, and the remaining 54 percent are fully able.
Patterns for difficulty and assistance are consistent with past studies highlighting disparities in such outcomes (Stuck et al., 1999; Schoeni et al., 2005, 2009). Differences in behavioral accommodations are also apparent. For instance, the percentage performing activities less often is relatively low for all groups (ranging from 4.5% to just over 7%) but, like difficulty and assistance outcomes, is higher for women and those with fewer years of completed education. Successful accommodation, however, follows a distinctive pattern, increasing from ages 65–69 through ages 75–79 and then decreasing with each successive age group. Successful accommodation is also more common among women, among older adults who are White and those who live with a spouse or partner, and for those at higher levels of education. Other studies suggest that successful accommodation is more likely for those with more children and those living in homes with environmental features already installed and that those who successfully accommodate report well-being on par with, and participation restrictions only slightly below, those who are fully able to carry out activities (Freedman et al., 2014, 2017).
Physical, Sensory, and Cognitive Capacity
Underlying the patterns in activity limitations are important differences in older individuals’ capacity to carry out daily activities. Focusing on a composite measure of physical capacity that blends self-reported items with performance tests, mean scores range from 23 (out of 32) for 65-to-69-year-olds to just 9.2 for those ages 90 and older (see Table 10-2). Men and older adults who are White, live with a spouse or partner, or have more years of education are more likely to have a high physical capacity score (≥27) than other groups.
The percentage of older adults with poor vision (either blind or unable to see far or near even when wearing glasses) and poor hearing (deaf or unable to use the telephone, hear conversation in a quiet room, or hear conversation with background noise) increases steadily with age, from a low of 7 percent and 10 percent for poor vision and poor hearing, respectively, among 65-to-69-year-olds, to a high of 24 percent and 34 percent among those ages 90 and older. The percentages with poor vision or poor hearing decrease as educational attainment increases. Poor vision is also higher for women and minorities, whereas poor hearing is lower for women and for Black older adults.
Table 10-2 also shows the percentage of older adults classified as having probable and possible dementia. The classification (see Kasper et al., 2013) is based on reported diagnosis of dementia, proxy reports from a validated informant instrument, and scores on domains of cognitive functioning (executive functioning, memory, orientation). For these estimates, individuals may not “recover” in the subsequent round and it is assumed that 72 percent of long-stay nursing home residents have dementia. Overall, more than 9 percent of older adults can be considered to have probable dementia and another 6 percent have possible dementia. The risks of having probable dementia are substantially higher for minority groups, those living alone or with someone other than a spouse or partner, and those with lower levels of educational attainment.
Age gradients for four of the measures (low physical capacity, poor vision, poor hearing, and probable dementia) and a summary measure of poor physical, sensory, or cognitive capacity are displayed in Figure 10-2. These age-specific prevalence estimates are the result of several distinct underlying forces: onset among those without poor capacity, recovery among those with poor capacity, and the history of mortality for each group. Just over 20 percent of adults ages 65–69 are considered to have poor capacity in any of the four domains; this figure increases to more than 80 percent by age 90. Low physical capacity (score of ≤ 14 out of 32) has the strongest age gradient whereas poor vision, poor hearing, and probable
TABLE 10-1 Activity Limitations among U.S. Adults, Ages 65 and Older, 2015
|Self-Care and Mobility Activities|
|Fully Able||Successful Accommodation||Less Often||Difficulty||Assistance|
|< High school||23.8||19.8||7.3||22.0||27.2|
NOTES: N = 7,859; for education groups, nursing home residents are omitted and N = 7,499; p-values for F-tests are significant at p ≤ .001.
SOURCE: Data from National Health and Aging Trends Study.
|Fully Able||Less Often||Difficulty by Oneself||Help for Health/Functioning Reasons|
TABLE 10-2 Physical, Sensory, and Cognitive Capacity among U.S. Adults, Ages 65 and Older, 2015
|Mean Score||% High Score||% Low Score|
|< High school||16.3||10.5||41.3|
NOTES: N = 7,859; nursing home residents are omitted for physical capacity by education groups and for poor vision, poor hearing (N = 7,499); p-values for F-tests are significant at p ≤ .001 except gender differences in dementia (p = .116).
SOURCE: Data from National Health and Aging Trends Study.
|% Poor Vision||% Poor Hearing||% Probable Dementia||% Possible Dementia|
dementia increase at more moderate slopes, reaching about 25–35 percent for those ages 90 and older, depending on the measure.
Physical, Service, and Technological Environment
The environments in which older adults carry out their daily activities also vary for older adults (see Tables 10-3 and 10-4). Overall, 10 percent live in a home without a bedroom, bathroom, and kitchen on one floor, just over 4 percent live in mobile homes, and 16 percent live in multiunit buildings such as apartments. The latter are more common at older ages and for women, minority groups, those who live alone, and those with less than a high school education. These differences can influence the types of environmental modifications that older adults have and can put into place. Modifications, such as grab bars, seats for the tub or shower, and raised toilet seats are less common among (non-Black) minorities. Others have demonstrated that among those with activity limitations, mobile home dwellers report fewer bathroom safety modifications (Al-rousan et al., 2015).
Also shown in Table 10-3 are the service environments in which older adults live. Overall, about 13 percent live in settings where services may be available, 2.5 percent live in nursing homes, 4.5 percent live in other residential care settings (e.g., assisted or independent living) and nearly 6 percent live in retirement or senior housing communities. These settings are more common with age; 42 percent of those ages 90 and older live in a setting that is not a traditional community setting, compared with only 6 percent of those ages 65–69. Such settings are more common for women and for individuals who live alone.
Differences in older adults’ technological environments are illustrated in Table 10-4. Nearly two-thirds of older adults living in settings other than nursing homes in 2015 reported going online in the last month. Internet use was much higher at younger ages, for White respondents, for those living with a spouse or partner, and for those with more completed years of education. Going online for social activities was much more common than using the Internet for household or health-related activities. Nevertheless, more than one-third of older adults used the Internet to carry out basic household activities, such as shopping, ordering prescriptions, or banking, although racial and education-related differences were substantial.
Participation Restrictions and Unmet Need
The negative consequences of living with activity limitations can be substantial for some older adults (see Table 10-5, which focuses on older adults in settings other than nursing homes). More than one in four (27%) older adults report being unable to participate in a valued activity in the last month because of their health or functioning. Overall, nearly 11 percent of older adults report having an unmet need for mobility or self-care and 6 percent report an unmet need for household activities (and 14% report either type of unmet need, not shown in Table 10-5). These figures translate to 31 percent of older adults with activity limitations (difficulty or assistance) reporting at least one unmet need in the last month. Unmet needs and restrictions increase with age and decrease with education and are greater for minority groups and those living with people other than a spouse or partner.
The Distinctive Disadvantage of Minority Women
The distinctive disadvantage of older minority women in the United States is illustrated in Table 10-6. This analysis is limited to older adults ages 80–89 in order to partially control for differences in the age distributions across groups. The age group is also of substantive interest, since disability risks increase sharply in the eighth decade of life. Note that the table
TABLE 10-3 Physical and Service Environments in Which U.S. Adults Ages 65 and Older Live, 2015
|Type of Home||Environmental Modifications|
|2+ Floors||Mobile Home||Multiunit Building||Had Any Features||Added Any Features|
|< High school||4.0||6.2||18.7||66.4||17.7|
NOTES: N = 7,859; nursing home residents are omitted for education groups and environmental features (N = 7,499); p-values for F-tests are significant at p ≤ .001 except for education differences in having any features (p = .292); gender, racial, and living arrangement differences in adding features (p = .114, .041, .116, respectively); and racial and education differences in the service environment (p = .029, .777, respectively).
SOURCE: Data from National Health and Aging Trends Study.
|Nursing Home||Assisted/Independent Living||Retirement/Sr. Housing||Community|
TABLE 10-4 Internet Use by U.S. Adults Ages 65 and Older, 2015
|Goes Online||Goes Online for|
|Social Activities||Household Activities||Health Activities|
|< High school||22.5||18.4||8.1||4.2|
NOTES: Nursing home residents are omitted, N = 7,499; p-values for F-tests are significant at p ≤ .001 except for gender differences in going online (p = .204), going online for social activities (p = .678), and going online for health activities (p = .004).
SOURCE: Data from National Health and Aging Trends Study.
TABLE 10-5 Consequences of Activity Limitations for U.S. Adults Ages 65 and Older, 2015
|Participation Restriction||Unmet Need for Assistance with|
|Self-Care or Mobility Activity||Household Activity|
|< High school||33.4||18.5||7.5|
NOTES: Nursing home residents are omitted, N = 7,499; p-values for F-tests are significant at p ≤ .001 except for racial differences for participation restrictions (p = .004).
SOURCE: Data from National Health and Aging Trends Study.
TABLE 10-6 An Overview of Disablement Domains by Sex and Race among U.S. Adults Ages 80–89, 2015
|Non-Hispanic White Men||Non-Hispanic White Women||NonWhite Men||NonWhite Women|
|Activity Limitations and Accommodations|
|Self-Care and Mobility Activities|
|Help for health/functioning reasons||30.1||43.7||42.0||53.9|
|Low physical capacity||23.9||50.6||46.5||65.7|
|Unmet need for assistance with self-care or mobility activity||10.7||16.7||14.4||26.4|
|Unmet need for assistance with household activity||6.2||6.5||3.7||10.7|
NOTES: N = 2,564; nursing home residents are omitted for poor vision and hearing and outcomes (N = 2,415); p-values for F-tests are significant at p ≤ .001 except for poor hearing (p = .079).
SOURCE: Data from National Health and Aging Trends Study.
masks considerable racial and ethnic heterogeneity within this group, and it does not shed light on differences in life expectancy across groups or in life course influences that lead to disparities in late-life health and functioning. Nevertheless, it demonstrates the vast gap between older minority women and other groups on most disablement domains.
Older minority women have about two to three times the risk of non-Hispanic White men of receiving assistance with self-care, mobility, or household activities or of having low physical capacity, poor vision, or probable dementia. They are also much less likely to successfully accommodate with assistive devices to carry out their self-care and mobility activities. Although they live in nursing homes at about the same rate as non-Hispanic White women (6%), they are about one-third as likely as non-Hispanic White women and half as likely as non-Hispanic White men to live in an assisted living or independent living environment (3.8% versus 12.7% and 8.0%, respectively). In terms of outcomes, they have the highest rates of unmet need and participation restrictions.
DISABLEMENT OVER TIME: TRAJECTORIES AND TRENDS
Individual patterns of disablement—including the speed and severity with which decline occurs, the duration of each phase of disablement, and prospects for recovery—unfold over time. At the population level, trends are influenced by the aging of cohorts who have been exposed to distinctive life experiences. Thus, time—whether parameterized as age, period, or cohort—is of fundamental interest when studying individual-level patterns and population-level dynamics.
For many years, studies of disability dynamics focused on the chances of making a transition between discrete states—often between “none” and “any” activity limitations or among levels of activity limitations (Wolf, 2016). The important distinction between catastrophic and progressive disability helped sharpen understanding of the distinction between sudden onset of impairment as the result of an injury, stroke, or other discrete event and the more gradual set of changes often associated with frailty (Ferrucci et al., 1996). Together, such studies emphasized age profiles and identifying higher-risk groups (e.g., females, minorities, individuals with low education levels) as well as modifiable risk factors (see Stuck et al., 1999, for a comprehensive review). Studies have also highlighted the substantial proportion of older adults who regain functioning (Crimmins and Saito, 1993) and factors linked to the chances of recovery (Hardy and Gill, 2004, 2005), such as being cognitively intact and having a history of regular physical activity.
More recently, aided by the availability of data from long-running national panel studies and the release of trajectory estimation procedures in statistical packages, researchers have focused on the implications of risk factors for trajectories, defined as pathways over time rather than simple transitions. Most often, such studies focus on trajectories in activity limitations. Occasionally, studies will emphasize differences in trajectories by indicators of earlier stages of the disablement process—that is, by the presence of a particular health condition (Chiu and Wray, 2011) or functional limitation (Martin et al., 2017). Nevertheless, understanding of how individuals move through the broader disablement process is still limited.
Comparing trajectory study findings is complicated not only by differences in study outcomes but also by choices about modeling approach and depiction of age (Wolf, 2016). There are at least three distinct modeling approaches in use today. Latent growth curve models assume individuals, who are represented by random effects, diverge from a single underlying average pathway (see, for example, Warner and Brown, 2011). Latent class trajectory models assume individuals in an (unobserved) group share a common pathway (see, for example, Taylor and Lynch, 2011). Finally, growth mixture models assume that there are person-level random effects within a set of discrete classes (see, for example, Han et al., 2013). With respect to depiction of time, there are also several different approaches in use. Most often, time is modeled as chronological age. However, in other cases, time from the first survey wave (baseline) is modeled (and age controlled). More recently Wolf et al. (2015) incorporated time until death (along with age) in their analysis of trajectories of activity limitations. Irrespective of modeling approach and depiction of time, researchers typically identify three to five trajectories for activity limitations: for instance, high functioning with no decline, moderate decline, and steep decline.
A fundamental question in the demography of aging is whether increases in life expectancy at age 65 have been accompanied by more or fewer years lived with limitations. Between 1990 and 2016, life expectancy at age 65 increased by more than 2 years (from 17.2 to 19.4 years); men gained nearly 3 years (from 15.1 to 18.0), and women gained 1.7 years (from 18.9 to 20.6) (National Center for Health Statistics, 2017; Kochanek et al., 2017). The small downturn in life expectancy at birth in 2015 and 2016, which is attributed to the opioid epidemic, is not apparent for life expectancy conditioned on surviving to age 65.
Several competing theories have been proposed to explain how population health changes with population aging. Forty years ago, Gruenberg (1977) suggested that increases in survival of persons with chronic disease
and disability would result from medical advances. In contrast, Fries (1980, 1983) asserted that chronic disease would be postponed to later ages and the period of morbidity and disability would be compressed into a shorter time before death. Manton (1982) added a third perspective that recognized that interventions designed to reduce mortality also would have an influence on morbidity, and vice versa. Thus, additional years of life would be gained through postponement of disease onset, slowing of disease progression, and improved clinical management of disease, and the relative amounts of each contribution could not be predetermined.
These theories have guided two broad types of studies. One set has focused on changes over time in disability prevalence—that is, the proportion of the population at a point in time reporting a particular outcome. The other set has focused on active life expectancy, which combines age-specific disability and mortality rates. Several excellent reviews have been published in recent years (Beltrán-Sánchez et al., 2015; Crimmins, 2015; Martin et al., 2010; Wolf, 2016). A brief overview focused on the U.S. experience follows.
Trends in Activity Limitations
Manton and colleagues provided the earliest evidence that a decline in the prevalence of activity limitations may be occurring for older adults in the United States (Manton et al., 1993). Although studies were initially inconsistent (Crimmins et al., 1997a), a systematic review found a convergence of evidence suggesting substantial declines in IADL limitations (Freedman et al., 2002). Notably large were declines from 1984 to 1999 in three IADL activities—managing money, shopping for groceries, and doing laundry (Spillman, 2004). Smaller declines for the 1990s in the use of help and difficulties with ADLs were also found across studies (Freedman et al., 2004). More recent analyses have consistently found that such declines did not continue into the first and second decades of the 21st century, except for those at ages 85 and older, and the share of nonelderly adults reaching late life with limitations in place has been increasing (Freedman et al., 2013; Martin and Schoeni, 2014).
Exploration into reasons for trends has been limited, in part because of data limitations. The most comprehensive investigation of the decline during the 1980s and 1990s revealed that shifting education was the most important factor (Schoeni et al., 2008). Improvements in vision (possibly linked to the diffusion of cataract surgery) and increases in the use of assistive technology to carry out daily activities are also likely to have been responsible in part (Freedman et al., 2006, 2007). Investigations into reasons for the recent pause have pointed to shifts in the obesity profiles and smoking histories of adults now reaching late life (Martin and Schoeni, 2014).
Trends in Active Life Expectancy
Evidence regarding active life expectancy trends in the United States reinforces the notion that neither a compression nor an expansion of late-life disability is an inevitable consequence of mortality declines. Instead, patterns have varied over time periods and for different demographic and socioeconomic groups. For instance, from 1970 to 1980, most of the increase in life expectancy consisted of years lived with activity limitations (Crimmins et al., 1997b). However, during the 1980s and 1990s, the percentage of life expectancy to be lived without limitations increased (Crimmins et al., 2009).
Several long-term studies of active life expectancy have highlighted that not all groups have benefited equally. Looking over the whole life cycle, Crimmins and colleagues (2016) found increases between 1970 and 2010 in life spent “limited in any way in the performance of one’s usual or other activities” was greater than the increase in life without such limitations (expansion), but at age 65, changes were consistent with compression for both men and women. Focusing on a different time period and measure (1982–2011 for ADL and IADL limitations), Freedman and colleagues found evidence consistent with a compression for men but not women, and for those reporting their race as White but not those reporting as Black. Older Black women were particularly disadvantaged (Freedman and Spillman, 2016; Freedman et al., 2016).
Although different definitions have been used, studies to date have focused mainly on activity limitations. Yet, as Crimmins (2004) pointed out with respect to prevalence trends, the various domains of disablement will not necessarily change in the same direction, nor are they necessarily consistently related to mortality shifts. Drawing upon the 2011 and 2015 NHATS and published mortality rates for the United States, Table 10-7 provides an overview of years expected to be lived in each stage of the disablement process. Age-specific rates used in the calculations are also provided. Even over this relatively short period, declines are evident in expected years lived fully able to carry out self-care and mobility activities (from 5.7 years in 2011 to 5.3 years in 2015). During the same period, increases were experienced in years expected to be lived successfully accommodating (from 4.7 years in 2011 to 5.3 years in 2015). There were no significant shifts in expected years lived in any of the other categories of activity limitations, nor were there significant changes in years lived with low physical capacity (5.5 versus 5.4 years) or poor vision (1.8 versus 1.9 years). However, years lived with poor hearing did decline (from 3.0 to 2.6 years) because of declines among all age groups except the youngest. With respect to years expected to be lived in residential care, no significant changes were evident, but years to be lived in the community increased from 16.1 to 16.6.
FUTURE RESEARCH ON THE DEMOGRAPHY OF LATE-LIFE DISABILITY
In the Committee on Population’s 1994 volume on the Demography of Aging, Manton and Stallard outlined a mathematical “framework for analyzing the relationships among health-related behaviors, genetic predisposition, disease incidence and fatality, population aging, and morbidity and mortality” (National Research Council, 1994, p. 6). The editors noted the value of such models for demonstrating the potential effects of interventions and policies on late-life disability rates. At the time, the language and conceptual models of disablement had not yet been fully developed, panel data were still in their infancy, and statistical algorithms for computing complex models were not yet widely available. The Baby Boom generation was in the prime of its working life (ages 30–48), and concerns about Baby Boomers’ retirement, health, and long-term care needs were problems for the distant future.
Since that time, research on late-life disability has benefited enormously from the conceptual advances made by scholars and by the data infrastructure investments in national panel studies, in particular those made by the National Institute on Aging. Statistical advances have also furthered investigations into late-life disability trajectories. The leading edge of the Baby Boom generation began to turn age 65 in 2011 and over the next decade will reach ages 65–82. The personal and societal costs of caring for a large generation of older adults, many of whom are living with disability, is now a more imminent concern (Freedman and Spillman, 2016).
The methods and materials of the demography of aging will continue to be central to addressing issues of late-life disablement and improving the lives of older adults. With respect to understanding how individual trajectories unfold, there is much work yet to be done. As additional rounds of NHATS become available, researchers will be able to refine identification and understanding of signature trajectories, whether “postevent” (such as after an injurious fall or stroke) or in the absence of a discrete event (e.g., cognitive impairment, frailty). Understanding how such trajectories evolve and the factors that influence the speed with which a trajectory unfolds can help clinicians build, tailor, and target interventions to support at-risk groups. Of particular interest is how to promote successful accommodation, including home modification, so that older adults can manage their lives in the least restrictive setting possible for as long as possible.
At the population level, continued tracking of the Baby Boom’s experience with respect to disablement is also a priority. This generation has had a unique set of life course experiences (Colby and Ortman, 2014) that will shape its disablement in later life. As a group, the Baby Boom generation is better educated, with more complex families than previous generations,
TABLE 10-7 Age-Specific Percentage with Activity Limitations, Poor Capacity, and Living in Residential Care Environments, 2011 and 2015
|65â€“69||70â€“74||75â€“79||80â€“84||85â€“89||â‰¥ 90||Expected Years Lived|
|Self-Care and Mobility Activities|
|Low composite score||11.5||18.0||27.3||37.4||56.0||77.2||5.5|
NOTES: N = 8,077 (2011) and 7,859 (2015); nursing home residents are omitted for poor vision and hearing, N = 7,609 (2011) and N = 7,499 (2015). Test for difference between 2015 and 2011 in expected years lived: * = p < .10; ** = p < .05; *** = p < .01.
SOURCE: Data from National Health and Aging Trends Study.
|65â€“69||70â€“74||75â€“79||80â€“84||85â€“89||â‰¥ 90||Expected Years Lived|
but also has higher rates of obesity and mobility-related impairments. As members of this generation continue to reach the years of highest risk for long-term-care services and supports, researchers should continue to not only track trends but also explore reasons for such trends. Although restricted in past studies because of data limitations, going forward NHATS offers researchers the unique opportunity to understand the extent to which shifts in activity limitations are related to changes in underlying capacity versus changes in choices about how activities are carried out.
Finally, to guide policy into the future, tying these new frameworks and measures back to the models and projections at the core of formal medical demography is an important priority. Projection models can now be built that recognize not only biological but also environmental and behavioral underpinnings of the disablement process. They can now also recognize the shifting relationships among disease, capacity, accommodations, limitations, and outcomes such as participation restrictions and unmet need. Enhanced modeling of late-life disablement will also facilitate identification of public health interventions that will be most likely to maximize independent functioning and extend quality of life.
Allen, S.M., and Mor, V. (1997). The prevalence and consequences of unmet need. Contrasts between older and younger adults with disability. Medical Care, 35, 1132–1148.
Allen, S.M., Piette, E.R., and Mor, V. (2014). The adverse consequences of unmet need among older persons living in the community: Dual-eligible versus Medicare-only beneficiaries. Journals of Gerontology: Psychological Sciences and Social Science, 69(Suppl 1), S51–S58.
Al-rousan, T.M., Rubenstein, L.M., and Wallace, R.B. (2015). Disability levels and correlates among older mobile home dwellers, an NHATS analysis. Disability and Health Journal, 8, 363–371.
Beltrán-Sánchez, H., Soneji, S., and Crimmins, E.M. (2015). Past, present, and future of healthy life expectancy. Cold Spring Harbor Perspectives in Medicine, 5, ii. doi: 10.1101/cshperspect.a025957.
Chiu, C.J., and Wray, L.A. (2011). Physical disability trajectories in older Americans with and without diabetes: The role of age, gender, race or ethnicity, and education. Gerontologist, 51, 51–63. doi: 10.1093/geront/gnq069.
Colby, S.L., and Ortman, J.M. (2014). The Baby Boom Cohort in the United States: 2012 to 2060 (Current Population Reports P25-1141). Washington, DC: U.S. Census Bureau. Available: https://www.census.gov/prod/2014pubs/p25-1141.pdf [April 2018].
Congressional Budget Office. (2013). Rising Demand for Long-Term Services and Supports for Elderly People. Washington, DC: Congressional Budget Office. Available: https://www.cbo.gov/publication/44363 [April 2018].
Cornman, J.C., Freedman, V.A., and Agree, E.M. (2005). Measurement of assistive device use: Implications for estimates of device use and disability in late life. Gerontologist, 45, 347–358.
Crimmins, E.M. (2004). Trends in the health of the elderly. Annual Review of Public Health, 25, 79–98.
Crimmins, E.M. (2015). Lifespan and healthspan: Past, present, and promise. Gerontologist, 55, 901–911. doi: 10.1093/geront/gnv130.
Crimmins, E.M., and Saito, Y. (1993). Getting better and getting worse. Journal of Aging and Health, 5, 3–36.
Crimmins, E.M., Saito, Y., and Reynolds, S.L. (1997a). Further evidence on recent trends in the prevalence and incidence of disability among older Americans from two sources: The LSOA and the NHIS. Journal of Gerontology: Social Sciences, 52, S59–S71.
Crimmins, E.M., Saito, Y., and Ingegneri, D. (1997b). Trends in disability-free life expectancy in the United States, 1970-1990. Population and Development Review, 23, 555–572.
Crimmins, E.M., Hayward, M.D., Hagedorn, A., Saito, Y., and Brouard, N. (2009). Change in disability-free life expectancy for Americans 70-years-old and older. Demography, 46, 627–646.
Crimmins, E.M., Zhang, Y., and Saito, Y. (2016). Trends over 4 decades in disability-free life expectancy in the United States. American Journal of Public Health, 106, 1287–1293. doi: 10.2105/AJPH.2016.303120.
Erikson, W., Lee, C., and von Schrader, S. (2017). Disability Statistics from the American Community Survey (ACS). Ithaca, NY: Cornell University Yang-Tan Institute (YTI). Available: http://www.disabilitystatistics.org [April 2018].
Ferrucci, L., Guralnik, J.M., Simonsick, E., Salive, M.E., Corti, C., and Langlois, J. (1996). Progressive versus catastrophic disability: A longitudinal view of the disablement process. Journals of Gerontology: Biological Sciences and Medical Sciences, 51, M123–M130.
Fragoso, C.A., Gahbauer, E.A., Van Ness, P.H., Concato, J., and Gill, T.M. (2008). Peak expiratory flow as a predictor of subsequent disability and death in community living older persons. Journal of the American Geriatrics Society, 56, 1014–1020.
Freedman, V.A. (2009). Adopting the ICF language for studying late-life disability: A field of dreams? Journals of Gerontology A: Biological Sciences and Medical Sciences, 64, 1172–1174, doi: 10.1093/gerona/glp095.
Freedman, V.A., and Spillman, B.C. (2014a). The residential continuum from home to nursing home: Size, characteristics and unmet needs of older adults. Journals of Gerontology: Psychological Sciences and Social Sciences, 69(Suppl 1), S42–S50. doi: 10.1093/geronb/gbu120.
Freedman, V.A., and Spillman, B.C. (2014b). Disability and care needs among older Americans. Milbank Quarterly, 92(3), 509–541. doi: 10.1111/1468-0009.12076.
Freedman, V.A., and Spillman, B.C. (2016). Active life expectancy in the older U.S. population, 1982-2011: Differences between blacks and whites persisted. Health Affairs (Millwood), 35, 1351–1358. doi: 10.1377/hlthaff.2015.1247.
Freedman, V.A., Martin, L.G., and Schoeni, R.F. (2002). Recent trends in disability and functioning among older adults in the United States: A systematic review. Journal of the American Medical Association, 288, 3137–3146.
Freedman, V.A., Crimmins, E., Schoeni, R.F., Spillman, B.C., Aykan, H., Kramarow, E., Land, K., Lubitz, J., Manton, K., Martin, L.G., et al. (2004). Resolving inconsistencies in trends in old-age disability: Report from a technical working group. Demography, 41, 417–441.
Freedman, V.A., Agree, E.M., Martin, L.G., and Cornman, J.C. (2006). Trends in the use of assistive technology and personal care for late-life disability, 1992–2001. Gerontologist, 46, 124–127.
Freedman, V.A., Schoeni, R.F., Martin, L.G., and Cornman, J.C. (2007). Chronic conditions and the decline in late-life disability. Demography, 44, 459–477.
Freedman, V.A., Kasper, J.D., Cornman, J.C., Agree, E.M., Bandeen-Roche, K., Mor, V., Spillman, B.C., Wallace, R., and Wolf, D.A. (2011). Validation of new measures of disability and functioning in the National Health and Aging Trends Study. Journals of Gerontology: Biological and Medical Sciences, 66, 1013–1021. doi: 10.1093/gerona/glr087. Epub June 29.
Freedman V.A., Spillman, B.C., Andreski, P.M., Cornman, J.C., Crimmins, E.M., Kramarow, E., Lubitz, J., Martin, L.G., Merkin, S.S., Schoeni, R.F., et al. (2013). Trends in late-life activity limitations in the United States: An update from five national surveys. Demography, 50(2), 661–671. doi: 10.1007/s13524-012-0167-z.
Freedman, V.A., Kasper, J.D., Spillman, B.C., Agree, E.M., Mor, V., Wallace, R.B., and Wolf, D.A. (2014). Behavioral adaptation and late-life disability: A new spectrum for assessing public health impacts. American Journal of Public Health, 104, e88–e94. doi: 10.2105/AJPH.2013.301687.
Freedman, V.A., Wolf, D.A., and Spillman, B.C. (2016). Disability-free life expectancy over 30 years: A growing female disadvantage in the U.S. population. American Journal of Public Health, 106, 1079–1085. doi: 10.2105/AJPH.2016.303089.
Freedman, V.A., Kasper, J.D., and Spillman, B.C. (2017). Successful aging through successful accommodation with assistive devices. Journals of Gerontology: Social Sciences, 72, 300–309. doi: 10.1093/geronb/gbw102.
Fried, L.P., Young, Y., Rubin, G., Bandeen-Roche, K., and the WHAS II Collaborative Research Group. (2001). Self-reported preclinical disability identifies older women with early declines in performance and early disease. Journal of Clinical Epidemiology, 54, 889–901.
Fries, J.F. (1980). Aging, natural death and the compression of morbidity. New England Journal of Medicine, 303, 130–135.
Fries, J.F. (1983). The compression of morbidity. Milbank Memorial Fund Quarterly, 61, 397–419.
GBD 2015 Disease and Injury Incidence and Prevalence Collaborators. (2016). Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990-2015: A systematic analysis for the Global Burden of Disease Study 2015. Lancet, 388(10053), 1545–1602. doi: 10.1016/S0140-6736(16)31678-6.
Gill, T.M. (2010). Assessment of function and disability in longitudinal studies. Journal of the American Geriatric Society, 58(Suppl 2), S308–S312. doi: 10.1111/j.1532-5415.2010.02914.x.
Gill, T.M, and Williams, C.S. (2017). Evaluating distinctions in the assessment of late-life disability. Journals of Gerontology: Biological and Medical Sciences, 72, 1538–1546. doi: 10.1093/gerona/glx022.
Gill, T.M., Robison, J.T., and Tinetti, M.E. (1998). Difficulty and dependence: Two components of the disability continuum among community-living older persons. Annals of Internal Medicine, 128, 96–101.
Gitlin, L.N. (2003). Conducting research on home environments: Lessons learned and new directions. Gerontologist, 43, 628–637.
Gregory, S.R. (2004). In Brief: Disability: Federal Survey Definitions, Measurements, and Estimates. Washington, DC: AARP.
Gruenberg, E.M. (1977). The failures of success. Milbank Memorial Fund Quarterly, 55(1), 3–24.
Guralnik, J.M., and Ferrucci, L. (2009). The challenge of understanding the disablement process in older persons: Commentary responding to Jette AM. Toward a common language of disablement. Journal of Gerontology: Medical Sciences 64A(11), 1169–1171.
Guralnik, J.M., Branch, L.G., Cummings, S.R., and Curb, J.D. (1989). Physical performance measures in aging research. Journals of Gerontology: Medical Sciences, 44, M141–M146.
Guralnik, J.M., Simonsick, E.M., Ferrucci, L., Glynn, R.J., Berkman, L.F., Blazer, D.G., Scherr, P.A., and Wallace, R.B. (1994). A short physical performance battery assessing lower extremity function: Association with self-reported disability and prediction of mortality and nursing home admission. Journals of Gerontology: Medical Sciences, 49, M85–M94.
Guralnik, J.M., Ferrucci, L., Simonsick, E.M., Salive, M.E., and Wallace, R.B. (1995). Lower-extremity function in persons over the age of 70 years as a predictor of subsequent disability. New England Journal of Medicine, 332, 556–561.
Han, L., Allore, H., Murphy, T., Gill, T., Peduzzi, P., and Lin, H. (2013). Dynamics of functional aging based on latent-class trajectories of activities of daily living. Annals of Epidemiology, 23, 87–92.
Hardy, S.E., and Gill, T.M. (2004). Recovery from disability among community-dwelling older persons. Journal of the American Medical Association, 291, 1596–1602.
Hardy, S.E., and Gill, T.M. (2005). Factors associated with recovery of independence among newly disabled older persons. Archive of Internal Medicine, 165, 106–112.
Iezzoni, L.I., and Freedman, V.A. (2008). Turning the disability tide: The importance of definitions. Journal of the American Medical Association, 299, 332–334. doi: 10.1001/jama.299.3.332.
Institute of Medicine. (1991). Disability in America. Washington, DC: National Academy Press.
Institute of Medicine. (2007). The Future of Disability in America. Washington, DC: National Academy Press.
Jette, M. (2009). Toward a common language of disablement. Journals of Gerontology: Medical Sciences, 64, 1165–1168.
Kasper, J.D., and Freedman, V.A. (2014). Findings from the 1st round of the National Health and Aging Trends Study (NHATS): Introduction to a special issue. Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 69(Suppl 1), S1–S7. doi:10.1093/geronb/gbu125.
Kasper, J.D., Freedman, V.A., and Niefeld, M.R. (2012). Construction of Performance-based Summary Measures of Physical Capacity in the National Health and Aging Trends Study (NHATS Technical Paper No. 4). Baltimore: Johns Hopkins University School of Public Health.
Kasper, J.D., Freedman, V.A., and Spillman, B. (2013). Classification of Persons by Dementia Status in the National Health and Aging Trends Study (NHATS Technical Paper No. 5). Baltimore: Johns Hopkins University School of Public Health.
Kasper, J.D., Chan, K.S., and Freedman, V.A. (2017). Measuring physical capacity: An assessment of a composite measure using self-report and performance-based items. Journal of Aging and Health, 29, 289–309.
Katz, S., Ford, A.B., Moskowitz, R.W., Jackson, B.A., and Jaffee, M.W. (1963). Studies of illness in the aged. The index of ADL, a standardized measure of biological and psychosocial function. Journal of the American Medical Association, 185, 914–919.
Kochanek, K.D., Murphy, S.L., Xu, J.Q., and Arias, E. (2017). Mortality in the United States, 2016 (NCHS Data Brief No 293). Hyattsville, MD: National Center for Health Statistics.
Land, K.C., and Yang, Y. (2006). Morbidity, disability, and mortality. In R.H. Binstock and L.K. George (Eds.), Handbook of Aging and the Social Sciences (pp. 41–58). Amsterdam: Academic Press.
Lawton, M.P. (1986). Environment and human behavior. In M.P. Lawton (Ed.), Environment and Aging (2nd ed., pp. 10–19). Albany, NY: Center for the Study of Aging.
Lawton, M.P., and Brody, E.M. (1969). Assessment of older people: Self-maintaining and instrumental activities of daily living. Gerontologist, 9, 179–186.
Levine, D.M., Lipsitz, S.M., and Linder, J.A. (2016). Trends in seniors’ use of digital health technology in the United States, 2011-2014. Journal of the American Medical Association, 316, 538–540. doi: 10.1001/jama.2016.9124.
LIFE Study Investigators. (2006). Effects of a physical activity intervention on measures of physical performance: Results of the Lifestyle Interventions and Independence for Elders Pilot (LIFE-P) Study. Journal of Gerontology: Biological Sciences and Medical Sciences, 61, 1157–1165.
Manton, K.G. (1982). Changing concepts of morbidity and mortality in the elderly population. Milbank Memorial Fund Quarterly: Health and Society, 60, 183–244.
Manton, K.G., and Gu, X. (2001). Changes in the prevalence of chronic disability in the United States Black and non-Black population above age 65 from 1982 to 1999. Proceedings of the National Academy of Sciences of the United States of America, 98, 6354–6359.
Manton, K.G., and Stallard, E. (1994). Medical demography: Interaction of disability dynamics and mortality. In National Research Council, The Demography of Aging (Ch. 7, pp. 217–278). L.G. Martin and S.H. Preston (Eds.). Washington, DC: National Academy Press.
Manton, K.G., Corder, L., and Stallard, E. (1993). Estimates of change in chronic disability and institutional incidence and prevalence rates in the U.S. elderly population from the 1982, 1984, and 1989 National Long-Term Care Survey. Journal of Gerontology: Social Sciences, 48, S153–S166.
Manton, K.G., Corder, L., and Stallard, E. (1997). Chronic disability trends in elderly United States populations: 1982–1994. Proceedings of the National Academy of Sciences of the United States of America, 94, 2593–2598.
Martin, L.G., and Schoeni, R.F. (2014). Trends in disability and related chronic conditions among the forty-and-over population: 1997-2010. Disability and Health Journal, 7(1 Suppl), S4–S14. doi: 10.1016/j.dhjo.2013.06.007.
Martin, L.G., Schoeni, R.F., and Andreski, P.M. (2010). Trends in health of older adults in the United States: Past, present, future. Demography, 47(Suppl), S17–S40.
Martin, L.G., Zimmer, Z., and Lee, J. (2017). Foundations of activity of daily living trajectories of older Americans. Journals of Gerontology: Psychological and Social Sciences, 72, 129–139.
Nagi, S.Z. (1965). Some conceptual issues in disability and rehabilitation. In M.B. Sussman (Ed.), Sociology and Rehabilitation (pp. 100–113). Washington, DC: American Sociological Association.
National Center for Health Statistics. (2017). Health, United States, 2016: With Chartbook on Long-term Trends in Health. Hyattsville, MD: National Center for Health Statistics. Available: https://www.cdc.gov/nchs/data/hus/2016/015.pdf [April 2018].
National Research Council. (1994). The Demography of Aging. L.G. Martin and S.H. Preston (Eds.). Washington, DC: National Academy Press.
National Research Council. (2009). Improving the Measurement of Late-Life Disability in Population Surveys: Beyond ADLs and IADLs: Summary of a Workshop. G.S. Wunderlich, rapporteur. Washington, DC: The National Academies Press.
Ostir, G.V., Volpato, S., Fried, L.P., Chaves, P., Guralnik, J.M. and the Women’s Health and Aging Study. (2002). Reliability and sensitivity to change assessed for a summary measure of lower body function: Results from the Women’s Health and Aging Study. Journal of Clinical Epidemiology, 55, 916–921.
Roberts, H.C., Denison, H.J., Martin, H.J., Patel, H.P., Syddall, H., Cooper, C., and Sayer, A.A. (2011). A review of the measurement of grip strength in clinical and epidemiological studies: Towards a standardised approach. Age Ageing, 40, 423–429.
Satariano, W.A., Guralnik, J.M., Jackson, R.J., Marottoli, R.A., Phelan, E.A., and Prohaska, T.R. (2012). Mobility and aging: New directions for public health action. American Journal of Public Health, 102, 1508–1515. doi: 10.2105/AJPH.2011.300631.
Schoeni, R.F., Martin, L.G., Andreski, P.M., and Freedman, V.A. (2005). Persistent and growing socioeconomic disparities in disability among the elderly: 1982–2002. American Journal of Public Health, 95(11), 2065–2070. doi: 10.2105/AJPH.2004.048744.
Schoeni, R.F., Freedman, V.A., and Martin, L.G. (2008). Why is late-life disability declining? The Milbank Quarterly, 86(1), 47–89. doi: 10.1111/j.1468-0009.2007.00513.x.
Schoeni, R.F., Freedman, V.A., and Martin, L.G. (2009). Socioeconomic and demographic disparities in trends in old-age disability health at older ages. In D.M. Cutler and D.A. Wise, The Causes and Consequences of Declining Disability among the Elderly (pp. 75–102). Chicago: University of Chicago Press.
Simonsick, E.M., Newman, A.E., Nevitt, M.C., Kritchevsky, S.B., Ferrucci, L., Guralnik, J.M., Harris, T., and Health ABC Study Group. (2001). Measuring higher level physical function in well-functioning older adults: Expanding familiar approaches in the Health ABC Study. Journals of Gerontology: Biological and Medical Sciences, 56, M644–M649.
Spillman, B.C. (2004). Changes in elderly disability rates and the implications for health care utilization and cost. Milbank Quarterly, 82, 157–194.
Stuck, A.E., Walthert, J.M., Nikolaus, T., Büla, C.J., Hohmann, C., and Beck, J.C. (1999). Risk factors for functional status decline in community-living elderly people: A systematic literature review. Social Science & Medicine, 48(4), 445–469.
Taylor, M.G., and Lynch, S.M. (2011). Cohort differences and chronic disease profiles of differential disability trajectories. Journals of Gerontology: Social Sciences, 66, 729–738.
Verbrugge, L.M., and Jette, A.M. (1994). The disablement process. Social Science & Medicine, 38, 1–14.
Warner, D.F., and Brown, T.H. (2011). Understanding how race/ethnicity and gender define age-trajectories of disability: An intersectionality approach. Social Science & Medicine, 72, 1236–1248.
Weiss, C.O., Hoenig, H.M., and Fried, L.P. (2007). Compensatory strategies used by older adults facing mobility disability. Archives of Physical Medicine and Rehabilitation, 88, 1217–1220.
Wolf, D.A. (2016). Late-life disability trends and trajectories. In L.K. George and K.F. Ferraro (Eds.), Handbook of Aging and the Social Sciences (8th ed., Ch. 4). New York: Elsevier.
Wolf, D.A., Freedman, V.A., Ondrich, J.I., Seplaki, C.L., and Spillman, B.C. (2015). Disability trajectories at the end of life: A “countdown” model. Journals of Gerontology: Psychological Sciences and Social Sciences, 70, 745–752. doi: 10.1093/geronb/gbu182.
World Health Organization. (2002). Towards a Common Language for Functioning, Disability, and Health, ICF. Geneva: World Health Organization.
This page intentionally left blank.