Studying Disability Trends in Aging Populations

JCrossCultGerontol DOI 10.1007/s10823-014-9245-6

ORIGINAL ARTICLE

Danan Gu & Rosa Gomez-Redondo & Matthew E. Dupre

# Springer Science+Business Media New York 2014

Abstract This article reviews the current literature on disability trends in aging populations and proposes a framework for studying disability trends built upon existing models of disablement. In addition to considering disablement and its associated factors, our framework also includes factors at population level and the interplays among personal resources and health behaviors, intervention programs, technological advances, and the consequences of disability trends in the context of life course and socio-ecological perspective. The framework is abbreviated FE-BRIT-SE to denote individual-level (F)ixed attributes, including genetic factors, personality, age, sex, and earlier life conditions, and the (E)nvironment; individual (B)ehaviors, (R)esources, (I)nterventions, (T)echnology; and (S)ocioeconomic and (E)cological consequences of disability trends. The overview offers an integrated framework for understanding the disablement process, trends and their complex milieu of causes and consequences.

Keywords Disability. Disability trend . Disablement . Older adults . Conceptual framework . Activities of daily living . Instrumental activities of daily living

Introduction

There is a growing body of research examining trends in physical disability (thereafter referred to as disability, unless noted otherwise) at older ages in both developed (e.g., Freedman et al. 2002; Lafortune et al. 2007; Schoeni et al. 2006) and in developing countries (e.g., Gu et al.

The views expressed in this presentation are those of the authors’ and do not necessarily reflect those of the United Nations, Universidad Nacional de Educación a Distancia, Spain, and Duke University. D. Gu (*) Population Division, United Nations, Two United Nations Plaza, DC2-1910, New York, NY 10017, USA e-mail: [email protected]

R. Gomez-Redondo Faculty of Political Sciences and Sociology, Universidad Nacional de Educacion a Distancia (UNED, Madrid, Spain e-mail: [email protected]

M. E. Dupre Department of Community and Family Medicine, and Department of Sociology, Duke University, Durham, USA e-mail: [email protected] J Cross Cult Gerontol

2009;Lima-Costaetal.2012;Martinetal.2011; Ofstedal et al. 2007). More recently, some studies have begun to investigate the possible causes attributable to disability trends and the consequences of these trends (e.g., Cutler et al. 2009b; Freedman et al. 2007, 2008a, 2008b; Jagger et al. 2009; Schoeni et al. 2008, 2009). Collectively, these studies have enriched our understanding of the diversity and the complexity of disability trends in different countries and cultures. At the same time, numerous articles have developed a variety of conceptual frameworks for analyzing disability—although most of this work focuses primarily on the individual-based disablement process and its associated factors (at individual level and/or at neighborhood level). However, because disability trends mainly refer to disability dynamics over time at the group or population level aggregated from individual observations, characteristics of a studying population — such as demographic compositions (e.g., age, sex, etc.), socioeconomic development level, culture, and stage in epidemiological transition– are also crucial to affect the overall trends of disability. Moreover, with rapid population aging in many countries, understanding disability trends in elderly populations has become invaluable for assessing future health needs and associated public inputs (Feng et al. 2013). Only with the recognition of the burden of disability can societies formulate policies and programs to address the consequences of disability in advance. As a result, the socioeconomic consequence of disability trends is an indispensable element for studying disability trends. Currently lacking is a framework that connects these population-level factors with individual-based elements of disablement. The purpose of this article is to provide some thoughts built on extensive overviews of established literature for how to better study disability trends, their causes and consequences. Specially, we aim to draw attention to the factors that impact disability trends and the consequences of these trends that are often overlooked and/or inadequately addressed. In doing so, we extend existing frameworks by linking disablement with population-level factors that are relevant to disability trends. Below, we briefly review the literature on trends in disability across high-income and non- high-income nations for the most commonly used measures of physical disability in elderly populations (i.e., ages 65 and older), followed by a section describing the major frameworks that were previously developed for studying disablements and a proposed framework that integrates and extends existing models for studying disability trends. We then discuss the current science identifying the possible underlying causes for the disability trends and the consequences of these trends in referring to our framework. Finally, we offer recommendations for future research aimed at studying disability trends.

Disability Measures and their Overall Trends

Common Disability Measures in Aging Research

In the existing literature, most aging studies (e.g., Cutler et al. 2009a; Field and Jette 2007; Freedman 2011; Lin et al. 2012) measure disability as inability to perform activities of daily living (ADL) –as proposed by Katz et al. (1963) – and instrumental activities of daily living (IADL) -as proposed by Lawton and Brody (1969). Katz’s ADL scale is a measure of basic personal care tasks in every-day life and is the most widely used indicator of disability in the literature (Field and Jette 2007). Katz’s scale consists of six general activities in daily life: bathing, dressing, eating, indoor transferring, toileting, and continence. The IADL scale includes shopping, managing money, using transportation, doing housework, using the JCrossCultGerontol telephone, preparing one’s own meal, and taking medications (Lawton and Brody 1969), with some minor variations in activities across studies. Nagi’s scale of physical functional limitations is much less commonly used than ADL and IADL scales to measure disability in the literature. Nagi’s tasks for physical functioning typically include standing for 15 min, crouching, squatting, kneeling, stooping, raising one’s hands overhead, grasping with fingers, lifting a heavy object, climbing stairs, walking 200– 300 m, walking five kilometers, and standing up from a chair (Martin et al. 2009;Martinetal. 2011;Nagi1965; Ofstedal, et al. 2007). Among these three measures, the tasks reported in the Nagi scale are the most difficult to perform and the tasks reported in the ADL scale require the least functional capacity to perform (George et al. 2008a, 2008b). Unlike ADLs–which are almost universally applicable—a number of items included among the IADL and Nagi tasks may not be applicable in all societies or populations. For example, using a telephone may not be a common daily activity in underdeveloped regions; and similar cases for using a washing machine for laundry or a microwave for food preparation. Consequently, the number(s) of IADL and Nagi items differ across surveys, which makes it difficult to compare levels of disability across time and location. Furthermore, compared to the Nagi and IADL scales, ADL disability was more reliably reported by respondents, more closely related to long-term care needs (Lafortune et al. 2007), less influenced by culture, and collectively, more comparable across societies. Considering these relative strengths and weakness, we focus primarily on trends in ADL disability to minimize measurement bias across surveys and countries—although we present IADL trends whenever available.

Empirical Evidence of Overall Trends in Disability

Substantial epidemiological, clinical, and demographic evidence shows long-term improvements in functioning in older adults in the United States and other high-income countries for the recent several decades (e.g., Freedman et al. 2002, 2004, 2013; Lafortune et al. 2007;Lin et al. 2012; Manton 2008). Table 1 compiles data available on trends in ADL and IADL disability since the 1980s for 20 high-income countries (including Taiwan) and for six non- high income countries. The results show that these improvements in functional disability are not universal across countries and over time. For example, evidence suggests that ADL disability among older adults did not change much from the early 1980s to the late 2000s in the United States (Field and Jette 2007;Freedmanetal.2004, 2013), although the results may slightly vary if different criteria were applied (i.e., difficulties with ADLs, use help, or use equipment in performing daily activities). By contrast, studies from Europe suggest that most European Union countries witnessed a decline in ADL disability from the 1980s to the early 2000s (Gomez-Redondo et al. 2005;European Commission 2008; Jeune and Brønnum-Hansen 2008;MoeandHagen2011;ONS2011; van Gool et al. 2011). In New Zealand, ADL disability has increased since 1980, whereas in Australia, Canada, the Netherlands, and the United Kingdom, levels of ADLs have fluctuated or remained stable, respectively, from 1990 to 2000 (Davis et al. 2003; Lafortune et al. 2007; van Gool et al. 2011). Data on ADL disability for non-high income countries are limited and results also had pronounced variations. Available data suggest declines in ADL disability levels in China since the middle 1980s (Gu et al. 2009;Hu2012) and in Thailand from 1996/97 to 2004 (Karcharnubarn 2010). In contrast, studies have shown that ADL disability has increased in Brazil since the middle 1990s (Lima-Costa et al. 2012) and that ADL improvements stalled in Table 1 ADL and IADL Disability Trends of Older Adults Across Countries

ADL IADL

1980s 1990–1994 1995–1999 2000–2004 2005–2009 1980s 1990–1994 1995–1999 2000–2004 2005–2009

High-income countries Australia −(0) 0 (0) 0 (0) +(+) 0 (0) +(+) Belgium −(−) −(−)+(+)+(+)−(−) +(+) +(+) −(−) Canada 0 (0) +(+) −(+) −(−) Demark −(0) −(−) −(−) 0 (0) 0 (0) +(+) −(+) +(−) −(−)0(0) France −(−) −(−) −(−) −(−) −(0) −(−) −(−) −(−) −(−) −(0) Germany +(0) −(−)+(0)−(−) Hong Kong −(−) −(−)+(+)+(+) Italy −(−) −(+) −(−)+(+)+(+) −(−)+(+)+(+) Japan −(−) −(−) −(−) −(−) −(−) −(−) −(−) Netherlands +(0) +(+) 0 (0) 0 (0) +(0) +(+) 0 (0) −(−) New Zealand +(+) +(+) +(+) +(+) +(+) +(+) +(+) +(+) Norway −(−) −(−) −(−) −(−)+(+)−(−) −(−) −(−) −(−)+(+) South Korea −(−) −(−) −(−) −(−) Spain −(−) −(−) −(−)0(0)+(+) −(−) −(−)0(0)+(+) Sweden −(−) −(−)+(−)+(+)−(−) 0 (0) 0 (0) 0 (0) −(−) Switzerland 0 (−) −(−) −(−) −(−) Taiwan, China* −(−) 0 (0) +(+) 0 (0) 0 (0) −(−) −(−) −(−) −(−)

United Kingdom 0 (0) 0 (0) −(0) −(−)+(−) 0 (0) 0 (0) −(0) −(−)+(−) Gerontol Cult Cross J United States 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) −(−) −(−) −(−) 0 (0) 0 (0) Non-high-income countries Brazil +(+) +(+) +(+) +(+) +(+) +(+) China −(−) −(−) −(−) −(−) −(−) −(−) −(−) Indonesia 0 (0) 0 (0) 0 (0) 0 (0) JCrossCultGerontol Table 1 (continued)

ADL IADL

1980s 1990–1994 1995–1999 2000–2004 2005–2009 1980s 1990–1994 1995–1999 2000–2004 2005–2009

Mexico +(+) Philippines −(−) 0 (0) 0 (0) +(+) +(+) Thailand −(−) −(−)+(+) −(−) −(−) −(−)

(1) The trend is for the overall trend of the entire elderly population aged 65 or older in a given country unless otherwise stated. Trends in the 1980s for many countries refer to the middle or late 1980s. Declining disability is denoted by “-“; increasing disability is indicated by “+”; and fluctuation, no change or inconsistency across major studies is indicated by “0.” Trends are reported for men and women (in parentheses), respectively. (2) Table is compiled by the authors from numerous sources that are nationally-representative for each country listed, mainly including REVES (http://reves.site.ined.fr/en/), Bebbington and Comas-Herrera (2000), Brønnum-Hansen (2005), Cambois et al. (2008), Cheung and Yip (2010), EHEMU (2010); Field and Jette (2007), Freedman et al. (2002, 2004, 2013), Freedman et al. (2008a). Graham et al.(2004), Hu (2012); Jacobzone et al. (2000); Jang et al. (2010), Jeune and Brønnum-Hansen (2008); Jiawiwatkul et al. (2012). Lin et al. (2012). Martin et al. (2011), Palacios-Ceña et al. (2012), Robine and Michel (2004),Schoenietal.(2006). U.N. Statistics Division database (http://unstats.un.org/unsd/population/ seriesy/seriesy_4e.pdf),vanGooletal.(2011); Waidmann and Manton (1998), Wong et al. (2011), and Zunzunegui et al. (2006) plus those in (3) below. (3) (a) Denmark’s IADL trend was obtained from life expectancy without longstanding illness; the longstanding illness scale has some items related to IADL (see Brønnum-Hansen 2005; Jeune and Brønnum-Hansen 2008). Denmark’s ADL trend was from Lafortune et al. (2007). (b) Trends for the Netherlands in the 1990s were from Perenboom et al. (2004), and in the 2000s were from van Gool et al. (2011) without distinguishing IADL and ADL and sex. We assumed they were same for IADL and ADL and for both sexes. The results from (EHEMU) (2010) were also considered. (c) Trends for Norway were from Moe and Hagen (2011) who constructed disability using two items in IADL and two items in ADL. (d) Sweden’s IADL trend was from Schön, and Parker (2008:113) who did not find significant change for people aged 77 or older from 1992 to 2002. We assumed that there was no fluctuation in the period. (e) UK’s disability trends were obtained from disability-free life expectancies at age 65 from the ONS (2006; 2011) without distinguishing ADL and IADL. This is similar for Belgium, Denmark, the Netherlands, France, Germany, Italy, the Netherlands and Sweden, after 2005 (see EHEMU 2010). (f) Data before 2000 for the U.S. are mainly from Freedman et al. (2004)andLinetal.(2012); data beyond 2000 are from Freedman et al. (2013)., which is for population aged 70 or older. (g) Brazil trends were obtained from Lima-Costa et al. (2012) who estimated the changes between 1998 and 2008 for both sexes combined. We assumed a linear change and both sexes shared the same trend. (h) The trends in ADL disability for China in the 1990s was from Gu and Zeng (2006) and Gu et al. (2009). The trends for IADL and ADL disability for the 2000s were authors’ own calculation using the Chinese Longitudinal Healthy Longevity Survey in 2002, 2005, and 2008 and the Sampling Survey of the Aged Population in Urban–rural China in 2000, 2006 and 2010. The results are consistent with Feng and Zeng (2014). (4)*, Taiwan (one province of China) is categorized as a high-income area by authors according to its per capita GDP in 2012. This classification does not reflect the views of the World Bank and the United Nations. J Cross Cult Gerontol

Indonesia and the Philippines in the 1990s (Ofstedal et al. 2007) and even reversed in Thailand after 2005 (Karcharnubarn 2010). Although we are unable to identify IADL trends for many countries, existing data suggest that trends in IADL disability were more or less similar to trends in ADL disability in most countries/areas. However, there is some evidence to suggest that IADL trends could be different from the trends in ADL in some countries/areas. For example, in the United States, IADL disability steadily declined before the early 2000s despite relatively stable levels of ADL functioning in older Americans (Freedman et al. 2004, 2013; Lin et al. 2012). A similar pattern was found in Taiwan from 1989 to 2007 (Martin et al. 2011;Ofstedaletal.2007). Feng and Zeng (2014) found that the decline in IADL disability was more pronounced than the decline in ADL disability among older adults in China from 2002 to 2008 (Feng and Zeng 2014). Additionally, it is worth mentioning that there exist a few gender inconsistencies in the observed disability trends across countries, in both ADL and IADL. Identifying trends in disability by individual items or components (e.g., walking, or bathing) is important for targeting improvements in specific activities and within populations. Any change in one item or component of disability may not necessarily indicate corresponding change in another (Crimmins 1996; Martin et al. 2011). For example, declines in ADL functioning among older community-dwelling Americans were primarily attributable to declines in mild to moderate levels of functionality; reductions in the prevalence of more severe ADL disability appeared to have been smaller (Freedman et al. 2004). In contrast, improvement in the specific ADL item of bathing might be the major driving force for the positive ADL disability trends in China (Feng and Zeng 2014;Zimmeretal.2014). Data from the Netherlands further suggested that increases in men’s disability in the 1990s were mainly attributable to increases in age-specific prevalence rates of mild-to-moderate disability (i.e., minor to major problems performing ADL tasks) rather than severe disability (i.e., unable to perform ADL tasks) (Perenboom et al. 2004).

Concept of Disability and its Conceptual Framwork

In order to understand the underlying causes of disability trends, it is necessary to further review the concept of disability. Disablement is considered a hierarchical process that typically begins with the onset of a chronic disease or morbidity, which leads to impairment, followed by functional limitations that ultimately cause disability (e.g., Verbrugge and Jette 1994). In this process, disability (i.e., the inability to perform normal activities/tasks in daily life) is not only an outcome of impairment resulting from pathologies or diseases—accordingtothe biomedical model—but also a ‘social construct’ that includes a constellation of conditions created primarily by the social environment—e.g., public transportation, facilities, and services—that prohibit individuals from performing daily activities or tasks (Field and Jette 2007; Iezzoni and Freedman 2008; Lutz and Bower 2003; Schoeni et al. 2008; Verbrugge and Jette 1994). WHO’s International Classification of Functioning, Disability, and Health (ICF) highlight- ed the co-existence of both the biomedical and social dimensions of disability (WHO 2001), which defines disability as difficulties an individual may have in performing any personal care, functioning, or participating in social activities because of intrinsic impairment or contextual barriers (Field and Jette 2007;Hull2012; Iezzoni and Freedman 2008; Palmer and Harley 2012;WHO2001). This concept is useful for understanding and quantifying disability trends over time (Davis et al. 2012; Field and Jette 2007), despite targeting the general population and not corresponding precisely with the widely used measures of functional disability (i.e., ADL JCrossCultGerontol and IADL) (Freedman 2011). However, because the ICF definition is more of a classification system of disability than a conceptual framework (Palmer and Harley 2012:p359), the consequences of disability—including the burden of disability on society—is essentially overlooked (see World Health Organization WHO 2001). There are several conceptual frameworks that address disablement in the existing literature (e.g., Cutler and Wise 2009; Field and Jette 2007; Freedman et al. 2008a;Martinetal.2011; Schoeni et al. 2008; Verbrugge and Jette 1994). Of these, the frameworks proposed by Cutler and colleagues (referred simply as Cutler’s framework hereafter) and by Verbrugge and Jette (referred simply as the V-J framework hereafter) are most comprehensive and influential. Cutler’s framework considers the causes (i.e., sociodemographics, advancements in medical technology, and health behaviors), characteristics (i.e., chronic conditions, core physical and cognitive functioning, and functioning with support), and the consequences of disability (i.e., disability insurance, work and economic productivity, health care cost and caregiving costs, and government finances) (Cutler et al. 2009b: p3). It also considers biomedical management, assistive technological support systems and the social environments as intervening factors that affect the characteristics of disablement. Moreover, the burden of disability is emphasized in this framework. This framework could be a good framework for studying disability trends. However, the roles of natural environmental factors and genetic characteristics as causes of disability are not explicitly presented in Cutler’s framework, and so are the linkages between the social environments and individual health behavior and between sociodemographics and characteristics and health behaviors that are important in studying disability and its trends. The V-J framework explicitly includes biological components as risk factors for disability in addition to other biomedical and social environmental factors; however, the consequences of disability are not directly presented, much like the ICF. Furthermore, aforementioned population characteristics in both frameworks are not explicitly presented as well. Based on this review, we attempt to propose an extended framework (see Fig. 1) that is mainly built upon the ICF, Cutler’s framework, and the V-J framework (1994). The white section of the central panel of the figure mainly describes the disablement process and trends (abbreviated as DIFDiT). This panel is an extension of the V-J disablement framework. The remaining sections describe factors that affect the disablement process and the consequences of disability trends. The factors affecting disability trends include older adults’ fixed (F) attributes (e.g., genetic factors, personality, age, sex, earlier-life conditions, etc.), socioeconomic and ecological environments (E) (both physical and social, or both natural and man-made), individual behaviors (B) (e.g., smoking, drinking, exercise, etc.), resources (R) (at different life stages), societal interventions (I), and advancements in medical technology (T). The consequences of disability trends mainly cover socioeconomic and ecological (SE) dimensions. Taken together, we conceptualize the framework for studying disability trends as: FE-BRIT- SE. Although the acronym for the framework does not include components in the disablement framework, it does not suggest that DIFDiT is not included in FE-BRIT-SE. Instead, DIFDiT is the core component of this extended framework because the FE-BRIT-SE framework itself is designed for the study of disability trends. However, for simplicity and convenience in practice, DIFDiT is not included in the current acronym of the framework. The FE-BRIT-SE model is intended to be parsimonious and concise, yet integrates the biomedical with social domains of disability, and incorporates individual-based elements with population-level elements as well as consequences. Every component in the framework is inextricably linked to the other and possesses its own temporal change (i.e., not static). Change in one component may alter the trend in disability over time. In the following section we J Cross Cult Gerontol

Fixed-attributes and Environments

Behaviors Resources

unctional iseases mpairment F sability Disability D I limitations Di Trends

Interventions Technologies

Socioeconomic & Ecological Consequences

Fig. 1 A Framework for Studying Disability Trends discuss each element of the FE-BRIT-SE framework that may be associated with disability trends and the possible consequences of disability trends.

Factors Attributable to Disability Trends and their Consequences

Changes in Disease Conditions

This section addresses how changes in disease conditions, impairment, and functional limitations influence disability trends–representing the white part of Fig. 1 (i.e., DIFDiT). Researchers have found that the observed disability trends could be effectively explained by the fact that chronic conditions are less debilitating now than in previous decades (Freedman and Martin 2000;Freedmanetal.2007). For example, Cutler et al. (2009b) showed that 20 to 25 % of the total decline in disability in the United States from 1982 to 1999 could be attributed to reductions in limitations associated with cardiovascular diseases. Schoeni et al. (2008) estimated that changing patterns in heart and circulatory diseases—either the share of the population with the disease or the disabling influence of the disease—could account for as much as 95 % of the drop in disability from 1982 to 1996 and 45 % of the drop from 1997 to 2004 in American older adults if other causes are not taken into account. Similarly, vision accounted for 57 % of the disability decline from 1982 to 1996 and 46 % from 1997 to 2004. JCrossCultGerontol

However, heart disease is increasingly reported as a cause of disability in some OECD countries such Canada, Japan, and the Netherlands (Lafortune et al. 2007). Diabetes accounted for only less than 10 % of changes in disability from 1982 to 1996 in the United States and was not associated with changes since 1997 (Schoeni et al. 2008), possibly because diabetes has plateaued in recent years (Martin et al. 2010). It is plausible that earlier diagnoses and better management of such conditions has led to lower reported rates of disability; with respect to less debilitating vision limitations, cataract surgery has played a central role. Psychological distress or depression, as a sort of disease condition, has been reported to be positively associated with poor functioning in ADL/IADL at older ages (Ormel et al. 2002). Furthermore, disability and depressive symptoms could mutually reinforce over time (Ormel et al. 2002; Verbrugge and Jette 1994), thus treatment is crucial for new disability with depressive symptoms. There is evidence that mental conditions (mainly depressive symptoms) among older Americans from 1982 to 2005 have increased and this trend caused a 6 % increase in ADL disability (Schoeni et al. 2008). We are unaware of existing studies on the relationship between changes in diseases and disability trends in low- or middle-income countries. However, a recent cross-sectional analysis from WHODAS II showed that dementia is the leading contributor of disability— followed by stroke, limb impairments, and arthritis—in China, India, Cuba, Dominican Republic, Venezuela, Mexico, and Peru (see Sousa et al. 2009). It was also shown that chronic diseases accounted for up to two-thirds of disability prevalence in these seven countries.

(F)ixed Attributes

There is little doubt that genetic and biological characteristics are considered fixed attributes since they are usually constant throughout the lifetime. Personality of an individual is stable over time and thus it is adequate to take it as a fixed attribute. From individual perspective, age, sex, or race/ethnicity can be grouped in this category because these factors are non-modifiable or difficult to modify. We also place the population structures in terms of these basic demographics in this category. Earlier life conditions also can be included in this category because these conditions reflect the events in the past and are unable to change for older adults.

Genetic or Biological Factors

The genetic precursors of disease are increasingly studied and identified. It has been reported that genetic factors could explain about 10 % of the variation in functional ability among older Danish male twins and 30 % among female twins (Christensen et al. 2003). The ApoE gene is associated with Alzheimer Disease and heart diseases (Eichner et al. 2002); other genetic abnormalities also are shown to be responsible for early-onset hypertension (Yang et al. 2009) and diabetes (Lodigiani et al. 2009). Furthermore, several localized samples have shown that interactions of ApoE-e4 with gender and baseline functional status predicted functional decline; the ApoE-e4 allele was not independently associated with declines in functional status or cognition (Blazer et al. 2001). Recent studies have also identified specific genes that are linked to rheumatoid arthritis (Toonen et al. 2008) and osteoarthritis (see arcOGEN Consortium and arcOGEN collaborators 2012); both types of arthritis are leading causes of disability in the elderly population worldwide. There also is evidence related to biological indicators of physiological dysregulation, commonly called biomarkers, and diseases that are major causes of physical limitations and death (Finch et al. 2001). Biomarkers (e.g., high cholesterol) are considered antecedent and J Cross Cult Gerontol manifested prior to the pathology stage in the disablement process (see Crimmins 2004). There is even some evidence to suggest potential gender differences in trends in measured biomarkers. For example, women generally experience declines in cholesterol level after age 40; however, C-reactive protein has been shown to increase (Kim et al. 2006). Results from Crimmins et al. (2005) indeed show reductions in high-risk total cholesterol and high-risk homocysteine among older Americans, and increases in hypertension, high-risk C-reactive protein, and obesity. With few exceptions, however, studies have not examined the role of potential genetic components involved in disability trends.

Personality

The association between personality and disability is receiving increasing attention. Although the reason for the associations is complex and the pathways are inclusive, there is little doubt that individuals with different traits have different risks for disease and disability (Friedman 2000; Krueger et al. 2006). Higher levels of extraversion and conscientiousness were associated with reduced risk of incident disability (Jang et al. 2003;Kempenetal.1999; Krueger et al. 2006). A higher level of neuroticism was associated with an increased disability risk of disability, yet the association is mediated by depression. The reason is possibly because there is a strong association between functional disability and depressive symptoms (Ormel et al. 2002) and because neuroticism is a risk factor for depression. The association between depressive symptoms and neuroticism might account for the association of neuroticism with disability. Studies further show that personality also determines adjustment or adaptation after the onset of disability (Boyce and Wood 2011). People with different traits have different psychophysiological reactions that could affect health behaviors such as smoking, drinking, eating, exercise (Krueger et al. 2006).

Major Demographic Characteristics

From an individual perspective, chronological age (hereafter age), gender, and race/ethnicity are the most basic individual-level demographic characteristics that have both biological and social meanings. The fact that disability increases with advancing age is obvious. For example, according to the 2007 National Health Interview Survey, only 3 % of adults aged 65–74 needed help with ADL performance; whereas this figure increased to nearly 22 % among adults aged 85 and older—with corresponding increases in IADLs from 6.3 % and 38 %, respectively (Freedman 2011). Gender differences in disability are also pronounced at older ages, with men exhibiting lower prevalence of disability then women. Although only 5 % and 8 % of men aged 65 or older reported needing help with ADL and IADL in 2007, the respective percentages for women were 8 % and 16 % (Freedman 2011). Over the past 20 years, racial/ethnic disparities in ADL disability have been persistently observed in older U.S. adults, with whites consistently exhibiting lower prevalence rates of disability (Dunlop et al. 2007;Schoeni,etal.2005). Researchers have also reported differing trends by these fixed attributes. For instance, studies in the United States have shown a precipitous decline in ADL disability among adults aged 85 and older yet a stable trend among adults aged 65 to 84 (Freedman et al. 2004, 2013). Furthermore, although women generally share similar trends as men, differences in trajectories exist over time (see Table 1). Whites often exhibit larger declines in ADL disability than African Americans, although these differentials disappear when socioeconomic factors are taken into account (Dunlop et al. 2007;Schoeni,etal.2005). A recent study from Taiwan also showed that Mainlanders had a less decline in IADL disability than non-Mainlanders from JCrossCultGerontol

1997 to 2007; and although urban older adults witnessed a greater decline in Nagi disability, they had a less decline in IADL disability compared to their rural counterparts (Martin et al. 2011). Gu and Zeng (2006) also reported a larger decline in ADL disability in men than in women. Studies suggest that the gender gap may be attributable to a complex set of socioeconomic, psychological, and biological factors such as health care access, lack of autonomy, biological differences (e.g., immune markers, hormones, and body composition), physical activity, domestic violence, and other stressors encountered throughout the life course (Alvarado et al. 2007;Zunzuneguietal.2009). Research further shows that levels of disability at older ages and trends over time are strongly associated with race and ethnicity (Field and Jette 2007; Schoeni et al. 2008). Comparable to gender, studies indicate that these associations are related to differences in socioeconomic status, behavioral factors, stressors encountered throughout life (Freedman et al. 2008a, 2008b), and some disease conditions such as obesity and diabetes mellitus (Whitson et al. 2011). Compositional changes in demographics in a population can alter the aggregate-level trends even if individual-level disability and its trajectory remain unchanged. This is why disability trends experts recommend the use of age-sex standardization (at minimum) or adequate adjustments (controlling for age, sex, etc.) in disability analyses to remove the influence of such structural differences (Freedman et al. 2004).

Earlier Life Conditions

There is plenty of evidence of the link of earlier life conditions with late life disabilities and the evidence is still growing (Schoeni et al. 2008). For example, one empirical study reported that suffering from infectious diseases in early adult-hood among U.S. Civil War veterans was associated with a higher rate of heart and respiratory diseases after age 50 (Costa 2000). Others showed that among Americans currently in their 50s, those who had some major childhood illness are 15 % more likely to report having a cardiovascular condition and twice as likely to have cancer or a chronic lung condition (Blackwell et al. 2001). These disease conditions are all major causes for disability. These findings provide the evidence of linkages between earlier conditions and the disablement process at later stages and support the hypotheses proposed by Preston et al. (1998) that adverse conditions experienced earlier in life have long-term negative effects on health or disability at old ages. It is recommended placing earlier-life conditions related to socioeconomic conditions in the resource section because SES in different life stages echoes each other, although social mobility is also not uncommon (Luo and Waite 2005). As such, the associations between SES at different life stages with disability trends at older ages can be investigated at a fuller perspective. However, earlier life exposures to adverse environments/events (such as wars, famines, epidemic, natural diseases, religion or political persecutions, etc.) can be considered as fixed attributes at late ages. In the current literature, although several studies have examined the impact of earlier life conditions on disability decline at later ages, they mainly focused on earlier SES conditions (e.g., Freedman et al. 2004; Schoeni et al. 2008). How the experience in other conditions in earlier ages could affect later age disability trends is unclear.

(E)nvironmental Factors

This category includes a broad category of factors, both physical and social, and both natural and man-made. Characteristics of a study population are also covered by this set of variables. J Cross Cult Gerontol

A growing number of studies have shown that beyond the individual level, macro- socioeconomic development and ecological (or socio-ecological) factors also play a role in individual physical functioning and disability at older ages (e.g., Beard et al. 2009; Clarke et al. 2009;Freedmanetal.2008a; Pruchno et al. 2012;WenandGu2011; Zeng et al. 2010). Collectively, these studies show that lower rates of disability are associated with more affluent neighborhoods/communities. The explanations of this phenomenon argue that socioeconomically resourceful communities often enjoy healthful physical environments that feature, for example, greater amounts of “green” space (Ellaway et al. 2005), more recreational areas (Wen and Zhang 2009), higher-quality food options (Dubowitz et al. 2008), and adequate health and social services (Andersen et al. 2002). In addition, safe sidewalks and well-lit streets may lead to fewer injuries; better street connectivity, sidewalks, and curbs can facilitate physical activities and exercise; and access to health care facilities can improve chronic health care (World Health Organization 2002). Communities with better socioeconomic conditions are also positively associated with local interpersonal features such as neighborly trust, norms and social cohesion that are associated with lower risks of disability, because social capital could delay onset of one’s disability by influencing health-related behavior through diffusion of health information and promoting social participation (e.g., Aida et al. 2013; Kawachi and Berkman 2003). All these promote enhanced functioning for daily activities. Furthermore, improved and more widespread use of assistive devices have enabled disabled people to function more effectively, and neighborhood infrastructure modifications and innovations have made physical and mental disabilities less of an impediment to independence for older adults living in the community (Cutler and Wise 2009). Theses advancements in ‘age- friendly’ environments at home and neighborhoods have contributed to declines in disability (Daniels et al. 2010). Freedman et al. (2006) found that from 1992 to 2001, a large portion of the decline in numbers of older adults receiving help for ADL limitations was related to greater usage of assistive technology. Other non-medical technology innovations such as washing machines, microwaves, and wheelchairs may have impacted older adults’ daily functioning as well. However, to our knowledge, so far no studies have investigated how changes in socio- ecological factors over time influence the disability trends. As some studies have shown that IADL disability is generally more sensitive than ADL disabilities to contextual changes, such as improved accessibility to physical environments and social services (e.g., wheeled walkers with baskets, ready-made meals, and microwave ovens) (Parker and Thorslund 2007). This possibly explains the greater improvements in IADL functioning than in ADL functioning in some countries (Freedman et al. 2004; Feng and Zeng 2014). Nevertheless, understanding the relative importance of these environmental components to changes in disability remains largely unexplored with empirical data. The stage of the epidemiological transition in which a society is experiencing is another sort of environmental factor that should be considered in studying disability trends because the stage of disability transition may determine the levels and trends of disability experienced by populations (Myers et al. 2003). In the early stages of disability transition, the incidence of disability is high with a relatively high level of prevalence because the underlying causes of disablement are communicable diseases. As the transition progresses, levels of prevalence and levels of incidence reverse; and the non-communicable diseases such as somatic and metabolic diseases, chronic cardiovascular and degenerative disorders become major causes (Harper and Armelagos 2010). During the transition, men and women may experience different trends. With increased longevity, women usually tend to have a higher prevalence or incidence of disability than men. In later stages, differentials in socioeconomic status play a greater role in JCrossCultGerontol affecting the prevalence, incidence, and trends of disability because the socioeconomic condition of individuals largely determines access to health care, environmental exposure, nutritional status, lifestyle and behavioral risks that are important for morbidity and mortality patterns. This indicates that disability trends in urban and rural areas may differ–especially in non-high income countries where rural people are likely to have very limited access to health care (Myers et al. 2003). However, all these issues have not been adequately explored in the literature of disability trends.

(B)ehaviors

Most health researchers and practitioners agree that changes in health behaviors are crucial to reduce the incidence of chronic disease and disability. Infrequent social contacts, little or no physical activity, smoking, weight gain, and poor nutrition are all risk factors associated with disability (Bartali et al. 2006;Heikkinen2003; Stuck et al. 1999). Smoking is one of the most significant issues in this regard. For example, recent studies in the United States have demonstrated that reductions in smoking and improvements in diet and exercise are associated with decreases in disability (Berk et al. 2006). Other studies have showed that older Americans in the early 2000s were more likely to exercise, consume more fruits and vegetables, have routine medical checkups, and were less likely to smoke or drink alcohols than their counterparts in the early 1990s (e.g., Mokdad et al. 2004). Schoeni et al. (2008)havereportedthat current smokers and former smokers are more likely to have disability than nonsmokers. Interestingly, however, smoking did not contribute to disability declines among U.S. older adults from 1982 to 2004 because of increases in smoking after age 70 (which offset the gain in disability decline). Consequently, reductions in U.S. disability rates would be larger if smoking among adults aged 70 and older did not increase during this period (Schoeni et al. 2008). Collectively, these behavioral factors could be attributable to declining trends in disability in older Americans. Changes in body mass is another important factor accounting for disability trends. The prevalence of obesity in the United States, which is related to diet, physical activity, and metabolism, steadily increased from 22 % in 1988–94 to 38 % in 2009–2010, with a slightly higher prevalence in women (Federal Interagency Forum on Aging-Related Statistics 2012: p124). Such drastic increases in weight have raised serious concerns about the physical functioning of the aging U.S. Baby Boom generation (Leveille et al. 2005). However, most studies have only examined the association between obesity and disability rather than trends in obesity as an antecedent of disability trends in later life (e.g., Villareal et al. 2005). Studies using data from the Hispanic Established Population Epidemiological Studies of the Elderly showed that older Americans with the highest body mass index (BMI >30 kg/m2)weremost likely to exhibit the onset of disability during a seven-year follow-up period (Al Snih et al. 2007). Although the contribution of obesity to disability is still relatively small, the proportion of adults reporting obesity as a cause of their disability almost tripled in American older adults from 1997 to 2004 (Freedman et al. 2007). Considering this, we suspect that physical functioning among older Americans would have improved further without the precipitous increase in body weight. In addition to smoking and elevated body mass, it is plausible that the increasing prevalence of certain risk factors and chronic conditions (e.g., diabetes) may be neutral- izing the positive impact of other factors (e.g., medical therapeutics) on the prevalence of disability at older ages in some countries (Schoeni et al. 2008). Moreover, although there are declining trends in disability among current elderly generations in many countries, there is some indication of an increasing prevalence of potentially impairing conditions J Cross Cult Gerontol among future elderly generations—partly because new medical technologies have saved many severely impaired people who would have once died (Iezzoni and Freedman 2008). Some researchers have further warned that trends in obesity and other potentially disabling conditions among working-age adults could offset future improvements in late-life functioning and that the beneficial effects of education will not be as large in the future (see Martin et al. 2010 for a review).

(R)esources

Resources in the framework mainly refer to individual-level resources. Community-level resources such as social capital and development level are considered as a part of social environments. Social disparities in disability are large and well documented (Freedman et al. 2008a, 2008b;Wolfetal.2005). In addition to numerous conventional pathways, there is evidence that socioeconomic discrepancy is an important contributor to disparities in disability at older ages (Engstrom et al. 2002; Freedman 2011;Jangetal.2010; Turrell et al. 2002). It is well known that lower socioeconomic status (SES) increases the incidence of some disability- related diseases (Grundy and Holt 2001), such as cardiovascular disease (Mieczkowska and Mosiewicz 2008), musculoskeletal disease (Sendi and Palmer 2000), and comorbid conditions (Macleod, Mitchell, Black, and Spence 2004). Research also shows that the linear correlation between poverty and disability rates among U.S. older adults is 0.93 (Schoeni et al. 2008)and that better financial resources may reduce disability by enabling individuals to purchase assistive technology, healthful foods, exercise equipment, safe homes in safe neighborhoods, and personal and medical care (Freedman 2011). Education is perhaps the most widely cited resource factor related to recent improvements in late-life functioning (Deeg 2005). Adults today have greater educational attainment on average than they did in the past. The proportion of older people with less than a high school education in the United States decreased from 72 % in 1970 to 20.5 % in 2010 (Administration on Aging 2011). Recent evidence shows that SES accounted for almost two-thirds of the linear trend from 1982 to 2004 in the United States and education alone accounted for half of the total decline in disability in the period (Schoeni et al. 2008). This pattern persisted in 2008 (Martin et al. 2010) and will likely continue as younger cohorts are attaining more years of formal schooling (Manton 2008). ADL functioning improved in adults aged 65–84 in Finland from 1993 to 2003 (Sulander et al. 2006), where older adults with 8 years of education or less were two times less likely to exhibit improvements in disability compared to older adults with nine or more years of education (Sulander et al. 2006). In Taiwan, education accounted for at least 16 % of the trend in any of nine Nagi physical functions and at least 47 % of the trend in vision limitation (Martin et al. 2011). Gu and Zeng (2006) also reported that more educated older adults in mainland China had greater declines in ADL disability than their low educated counterparts. Explanations for why individuals with higher SES have better physical functioning is generally attributed to health knowledge, behaviors, psychosocial factors (e.g., social support), and material resources (e.g., housing conditions and access to healthcare) (see Martin et al. 2011; Wen and Gu 2011). The avoidance of smoking and a sedentary lifestyle, maintaining social ties, purchasing health commodities, and receiving timely medical treatment and care are all likely mechanisms that explain why people with higher socioeconomic status have lower levels of disability than those with lower socioeconomic status. However, recent research in Korea showed that Korean older adults with low education and income witnessed a greater decline in disability from 1994 to 2004 than those with higher JCrossCultGerontol education and income (Jang et al. 2010). The main reasons for this non-intuitive finding are possibly because of higher mortality in the lower SES sample during follow-up, greater access to healthcare among higher SES groups leading to increased reporting of functional limitations, and possible changes in perception and/or reporting of disability over time (Jang et al. 2010). Research on associations between SES at different life stages and disability at late ages has flourished in last two decades Most studies have demonstrated that a strong association between early life or mid-life stage and physical functioning at later ages in various countries with different levels of income (Freedman et al. 2013;McEniry2013; Schoeni et al. 2008; Wen and Gu 2011;Zengetal.2007). Zeng et al. (2007) found that receiving adequate medical services in childhood could reduce the risk of being ADL disabled among Chinese oldest-old men and women by 18 % and 22 %, respectively. Luo and Waite (2005) similarly found that U.S. adults aged 50 and older who had relatively disadvantaged childhoods or adulthood socioeconomic conditions had greater functional limitations than their counterparts who grew up in more well-off families; however, the negative impact of poor childhood conditions can be ameliorated by better SES in adulthood. Luo and Waite further found a strong gradient between cumulative socioeconomic conditions over childhood and adulthood and functional limitation at old ages. Freedman et al. (2008a) investigated the role of early life factors on late life disability decline in elderly Americans and found that mother’s educational level (a proxy of earlier socioeconomic condition) is strongly linked with a lower odds ratio of ADL disability. This is likely because early socioeconomic status could influence education and labor market outcomes in mid-life, which translate into higher income in later life, allowing older adults to purchase services to avoid or accommodate functional decline (Freedman et al. 2008a: p1589). Finally, and much like socioeconomic resources, family support, emotional supportive resource, is also strongly linked to better physical functioning. For example, one U.S. study showed that the estimated annual rate of disability fell from 1.50 to 1.35 % once marital status was taken into account (Schoeni et al. 2008). More recently, research showed that the ADL and IADL disability gaps between married older White Americans and their unmarried counterparts narrowed from 1997 to 2010, and that socioeconomic resources almost had almost no explanatory power for this pattern (Liu and Zhang 2013).

(I)nterventions

The positive effect of interventions on improvement of disability is frequently documented. In the U.S., there is evidence that relatively long-lasting and high-intensive exercise programs (but not nutritional interventions) have had a positive impact on ADL and IADL functioning for community-living, moderately frail older persons in the U.S. (Daniels et al. 2008). Gill et al. (2002) also found that participants in a physical therapy intervention group of older frail Americans had approximately 18 % reductions in IADL disability at 7 months and 12 % at 12 months compared to older adults in the control group. The intervention group also showed gains in mobility and integrated physical performance at 7 and 12 months that ranged from 7 to 16 %, respectively. A more recent meta-analysis indicated that intervention programs on physical activity with a mediate level of intervention reduces the onset of ADL disability by nearly half, adjusting for age, length of follow-up, study quality, and differences in demographics, health status, functional limitations, and lifestyle (Tak et al. 2013). The benefits of exercise programs for improvements in physical functioning even persist into the oldest-old ages and among various patient groups. Another recent study found that each additional 10,000 activity counts per day (a 24/h summation of speed and motion activity for every J Cross Cult Gerontol fifteen seconds measured by an actigraph) reduced the risk of developing ADL disability by 25 % among the oldest-old Americans in Chicago (Shah et al. 2012). In terms of intervention, the benefits of exercise programs could improve physical function before hip replacement surgery is needed as a medical treatment (Gill and McBurney 2013). Evidence from hospitalized patients engaging in function-focused care (ADL, IADL and other physical function related physical therapy and exercise programs) also showed that these programs significantly slowed functional decline and increased earlier discharges (Boltz et al. 2012; Jones et al. 2006). Similarly, intervention programs that target muscle strengthening and aerobic activities among patients with osteoarthritis and rheumatoid arthritis have demonstrated improvements in physical and self-care function (Dorothy, et al. 2011). Patients in intensive care units and residents in nursing homes and assisted-living settings also benefit with improvements in physical functioning and reductions in disability when function-focused care programs are implemented (Boltz et al. 2011). Under- or malnutrition, which adversely impacts physical function, is not uncommon in community-residing older adults in some societies (Cuervo et al. 2009). Some nutritional programs have demonstrated significant improvements in functional outcomes and even lower mortality in community-residing and hospitalized adults at high risk (Chevalier et al. 2008; Smoliner et al. 2008). Nonetheless, there have been numerous reported dietary intervention programs among older adults to reduce weight and promote healthy diet (e.g., Sahyoun et al. 2004; Bernstein and Munoz 2012), yet the benefit of these programs may have limited significant impact on functional improvements or their direct contribution is at most measur- ably modest (Rydwik et al. 2010).

(T)echnologies

In our framework, technology mainly means medical progress. Medical advances have significantly contributed to improvements in physical functioning in older adults (Cutler et al. 2009a, 2009b; Field and Jette 2007; Spillman 2004). For example, the utilization of coronary artery stents per 100,000 population among U.S. older adults nearly tripled within just 4 years, from 1996 to 2000 (National Center for Health Statistics 2003), with utilization continuing to grow after 2000 at a slower rate (National Center for Health Statistics 2012: Table 107). However, there is evidence that while coronary stents have clear benefits in reducing mortality and morbidity in patients with acute coronary syndrome (e.g., myocardial infarction), its benefit is much less in stable patients; thus, the overall benefit of coronary stents has limited advantage in improvement of survival beyond the best medical treatment (Boden et al. 2007). More recently, drug-eluting stents have been developed and have yielded significantly better clinical outcomes such as lower mortality and lower incidence of stroke (compared to previous stents) (Douglas et al. 2009). Nevertheless, the physical side-effects of drug-eluting stents are not trivial (van der Hoeven et al. 2005). Similarly, hip and knee replacement surgeries today are much less risky than in the past because of advancements in surgery that have made the procedure significantly safer. As a result, the utilization of knee replacement surgeries increased from 310 per 100,000 in 1991 to 620 per 100,000 in 2010 among Medicare beneficiaries (Cram et al. 2012). Studies have shown that older patients with osteoarthritis had significantly better ADL and IADL functioning after knee/hip replacement than patients who were not treated (George et al. 2008a, 2008b). Technological improvements for the early detection of certain diseases have also created opportunities for interventions to slow the progress of disease and reduce its impact on functional status (Mor 2005). Improvements in the diagnosis, treatment, and rehabilitation JCrossCultGerontol for the major causes of death have also contributed to the decline in disability among older adults (Crimmins 2004). It has been estimated that pharmaceutical innovations and discoveries have reduced functional limitations among nursing home residents by 1.2–2.1 % annually from 1990 to 2004 (Lichetenberg, 2012). Overall, advancements in medical technology have played an important role in disability reductions among the current elderly population. However, there is evidence of an increasing prevalence of potentially impairing conditions among future elderly generations—because new medical technologies have saved the lives of many severely impaired people who would have otherwise died (Iezzoni and Freedman 2008).

(S)ocioeconomic and (E)cological Consequences

Although studies on the future consequences of disability are limited, the current literature is consistent in showing that the number of disabled older adults worldwide is large and their economic costs are enormous, and growing. Even if the prevalence of disability has declined in recent years in many countries, the increasing sheer size of aging populations with increased longevity are expected to produce increasing numbers of older adults with severe disability, and make it more important to focus on the improvement of disability trends. Lafortune et al. (2007) showed that aging populations would contribute approximately 40–75 % more older persons with ADL disability by 2030 in most of the twelve countries represented in the Organization for Economic Cooperation and Development (OECD). In fact, the numbers of older adults with ADL disability in Australia, Canada, and Finland are expected to double within this time frame. In the United States, the number of older adults with ADL disability in 2030 would be double the number in 2004 if the prevalence of ADL disability remained unchanged since 2004. To keep the same health care expenditures for the average older American as in 1998, it would require a 3.9 % annual decline in disability from 1998 to 2004 (far greater than the actual decline) if the age-sex-expenditure rates and discount values held constant (Gandjour 2013). In Belgium, the number of older adults with ADL disability would triple by 2030 if disability rates followed patterns observed from 1997 to 2004 (Lafortune et al. 2007). In the United Kingdom, it has been demonstrated that population aging alone will increase the disabled older population by over 80 % (or more than 700,000 persons) from 2006 to 2026 (Jagger et al. 2009). Likewise, Gu and Vlosky (2008) show that the number of older disabled Chinese would increase by 20 million from 2005 to 2050 (five times higher than current levels) due to population aging. They further show that a 1 % annual decline in disability would reduce the number of older disabled Chinese in 2050 by 9 million (two times the current level). The burden of functional disability on government-reimbursed healthcare service is substantial. Disability has been shown to increase the healthcare expenditures of a nation and the out-of-pocket payments for families. The disability-associated healthcare expenditures accounted for nearly 30 % of all healthcare expenditures for adults residing in the United States in 2006 and totaled almost $400 billion (Anderson et al. 2011); the aggregated long- term care expenditures (i.e., healthcare costs mainly associated with ADL disability) were approximately $43 billion—with $13 billion and $30 billion, respectively, for older men and women—in the same year (Janssen et al. 2004). According to the National Health Policy Forum (2011), the United States spent $203–$243 billion on long-term care costs in 2009. If we approximate this amount to 36 % (the proportion of older adults in the total disabled population), the estimated spending in long-term care costs for older adults would be around $72–87 billion (Brault 2012). J Cross Cult Gerontol

In the United States, out-of-pocket healthcare expenditures as a percentage of household income nearly doubled from 12 % in 1977 to 22 % in 2009 among older adults (FIFARS 2012). Webb and Zhivan (2010) estimated that for a couple turning age 65, the expected out- of-pocket spending on total healthcare in their remaining years would be approximately $260,000—of which $60,000 would be for long-term care. It has been shown that 25 % of the gross domestic product (GDP) would be spent on Medicare and Medicaid in 2080 if age- sex specific disability rates remained constant after 2000; however, a 1.5 % per annum decline in disability would reduce this proportion of the GDP to 14 %, an approximately 40 % reduction (Manton 2008). Furthermore, the estimated economic costs of unpaid contributions of family care/support were around $450 billion in 2009, a sizeable increase from an estimated $375 billion in 2007 (Feinberg et al. 2011). If unpaid services were included in the calcultion, the estimates on the family burden of functional disability would be substantially higher. In the Netherlands, the national aggregated total long-term care expenditure for adults aged 55 or older is expected to increase from €10.7 billion in 2007 to €16.8 billion in 2030 if disability trends from 1989 to 2007 persist (de Meijer et al. 2012). Although the share of GDP from long- term care expenses is still relatively small in OECD-EU member countries, research shows that long-term care spending could double from around 1.2 % of GDP in 2007 to 2.4 % in 2050 for these countries and to 2.9 % in 2050 for non-European OECD countries (Colombo and Mercier 2011). Jagger et al. (2009) used a dynamic macro-simulation model to project that the total disabled population of older adults in the United Kingdom would decrease by 15,000 from 2006 to 2026 (1 % reduction of the disabled population) under a scenario of delayed onset, reduced disability, and improved survival. Furthermore, they explored the possible consequences of treatments that delay the onset of cognitive impairment and associated disability and found that a 50 % reduction in dementia-related disability will reduce the number of disabled by 10 %. Healthcare spending varies by disability severity. Chernew et al. (2005) have shown that spending has increased faster among the least disabled compared to those with more disability. After accounting for demographic differences and health status, spending by the nondisabled and beneficiaries with only IADL disability grew 23 and 28 %, respectively. This compares to a 10 % increase for those with one or two ADLs, a 0.6 % increase for those with three or four ADLs, and a 10 % decrease for the most disabled. Taken together, the ratio of spending among the ADL disabled relative to the nondisabled, declined over time. Ample attention has been given to how ecological or environmental factors affect disability and associated trends. Yet, insufficient attention has been paid to how disability and disability trends could have a negative impact on environments. While the ways that disability and its trends impact surrounding environments are mainly indirect, in light of the rapid growth of the elderly population, it is important to take the impact of disability and disability trends into consideration, in spite of the previously possible positive contribution of this subpopulation to environmental protection. More age-friendly living environments need to be established to accommodate increasing numbers of disabled older adults. This growing population will consume a considerable amount of energy and resources that could impact the ecosystem as awhole–directly and indirectly. In this regard, the ecological or environmental consequences of disability trends should not be underestimated. Indeed, increasing awareness of this issue is a reason for adding this element in our framework.

Future Perspectives

Building on existing literature, we proposed an integrated framework for studying disability trends that includes components from biomedical models and the social consequences of JCrossCultGerontol disability trends. Rather than replacing existing frameworks, our goal is to provide a comprehensive overview and offer insights for the better study of disability trends from a wider perspective—with particular attention given to population level characteristics and the potential socio-environmental consequences of disability trends. We outline our framework from an extensive literature review and the current state-of-science from research on aging. Because disability in later life is influenced by a complex set of biological, biomedical, behavioral, economic, social, and environmental factors that interact throughout the life course (Schoeni et al. 2008:p49), and because data on the breadth of these factors does not exist, we drew on evidence from a variety of sources—some data more robust than others, some based on original analyses, and some drawn from other scientists’ work. Currently, the majority of existing studies have only reported data for only one or just a few of the components in the FE-BRIT-SE model. This has prevented researchers from examining disability trends, their causes, and their consequences in a more holistic manner. Understanding the causes of and factors attributable to disability trends will remain a challenge. Ideally, it will require more complete data and appropriate multi-level modeling to account for levels (and changes) in each component of the FE-BRIT-SE framework and the mechanism of the DIFDiT. Listed below are several issues we think that need to be considered in the study of disability trends.

Harmonization of Disability Measurements and Data Collection

Harmonization of measures and criteria is the first and most important step in disability trend analysis. Differences (or changes) in definitions of disability and the wording of questionnaires impact the outcomes of disability prevalence or trends. Freedman and colleagues (2004) emphasized the incomparability between difficulties in performing activities and needing help with activities. In addition, certain social groups (or cultures) also may have different interpretations for the same survey question (Banks et al. 2008). Similarly, an individual’s knowledge and perception of disability may change over time (Jang et al. 2010;Palmerand Harley 2012), especially when new devices and technology make some tasks easier to perform (e.g., microwave cooking). Indeed, for results even within the same country, the disability trends are dependent on particular disability measures (see Freedman et al. 2004; Lin et al. 2012; Schoeni et al. 2005). We are aware of some recent calls for improvement for construct validities of the conventional measurements of the ADL and IADL disability scales due to their ceiling effects (i.e., many people reported no disability in these two scales) (Fieo et al. 2011). We also notice that the United Nations (Washington City Group 2009) has recently developed a new measure called the Washington City Group general disability measure. This measure addresses six functions—seeing, hearing, walking or climbing stairs, remembering or concentrating, body washing or dressing, and communicating. Although the validity of this measure has been pre- tested in many countries and an international database has been established (see Palmer and Harley 2012), the availability of these data is insufficient to study disability trends. Furthermore, the measure is operationalized for census applications and may have limited application for aging research that requires more detailed information on functioning in elderly populations. Therefore, we suggest continuing to use widely accepted ADL or IADL measures to analyze disability trend in elderly populations. Sophisticated data collection and data quality control are also essential to advancing our understanding of disability and its trends. Variations in disability trends across countries and over time may be influenced by survey coverage, population age structure, and other compositional features in addition to aforementioned factors. Ideally, surveys should be population J Cross Cult Gerontol based and include a sizeable representation of vulnerable groups (e.g., institutionalized adults). Otherwise, non-representative or incomplete sampling could produce biased estimates in disability trends and their underlying causes. Furthermore, population estimates and comparisons may be misleading without appropriate age standardization in the aggregated rates of disability within and across older adult populations (Freedman et al. 2004). To achieve generalizability and comparability across locations, multi-national surveys should be implemented with standardized international guidelines for questionnaire design and methodology (European Commission 2008; Field and Jette 2007;Jagger et al. 2008;PalmerandHarley2012). Neglecting potential variations in culture and/or socioeconomic environments may induce a flawed survey design that includes inappli- cable items for certain areas—e.g., the use of telephones in rural areas where phone service is limited or unavailable. A major challenge for researchers is how to make generalizations and compare findings when the diversity of cultures, norms, and values in surveyed populations has not been taken into account. Although few, there are several regional and international studies that have been implemented and provide valuable examples—such as the Survey on Health, Ageing, and Retirement in Europe (SHARE). An international organization– International Network on Health Expectancy and the Disability Process (REVES)–has pooled efforts in harmonizing the data collection and measurements on trends of disability-free life expectancy by combining mortality and disability data for different countries/regions in the past two decades. Collecting more data on the consequences of disability for families and individuals is another area in which we should mobilize efforts. By and large, disability studies overlook the role of family and only a few have addressed the impact that family-level factors may have on disabilities of members and the lives (and behaviors) of those living with disabled family members (Field and Jette 2007;Ferreretal.2005). Indeed, the family is a key resource to which members turn for emotional support and instrumental resources such as money, skills, and care (National Institute on Aging 2008). Studies should further identify these familial resources and their health-related utility, as well as increase awareness among households with disabled members on how to improve the lifestyles and behaviors of the entire family network. Furthermore, if trend analysis is based on individual-based observations, it is technologically crucial for researchers to collect data for at least three waves (or data points)—preferably spanning six or more years—to avoid interpretations and policies based on potentially short-term variations (Freedman et al. 2002). For population- based trend analyses, comparisons should take major demographic compositions into consideration. And for international comparisons, the possibility of age exaggeration in most developing countries should be taken into account in that inaccurate age reporting may bias estimates of disability trends at older ages in these countries (Johnson et al. 2009; Denic et al. 2004;ZengandGu2008).

Integration of Life-Course and Ecological Perspectives

Despite a growing number of theoretical and empirical articles on earlier and later life conditions on health, studies linking earlier life conditions to disability trends at older ages are almost nonexistent—with only a handful of exceptions (Freedman et al. 2008a, 2008b; Schoeni et al. 2008). In one of these studies, Freedman et al. (2008a) examined the association between early life conditions and disability trends among U.S. older adults from 1995 to 2004 and found that increases in mothers’ education and improvements in education, wealth, and lifetime occupation were associated with declines in the onset and prevalence of ADL JCrossCultGerontol disability. Although these findings are insightful, the pathways by which these early- and mid- life factors impacted late-life disability trends are still insufficiently explored. Furthermore, increasing research has shown that high-quality neighborhood environments are linked to factors that have a bearing on the disability of older adults. However, with few exceptions (e.g., Clarke et al. 2009), no studies have investigated the role of changes in the environments on disability trends. Wen and Gu (2011) integrated the socio-ecological framework with a life-course perspective to examine disability, yet they did not examine how the change in disability trends was affected by ecological factors and did not consider the time- varying nature of the ecological factors. According to disability transition, in many of today’s low- or middle-income countries, older adults may witness disability declines in the near future as they experience the early stages of the disability transition. Improvements in hygiene, nutrition, and general public health will likely reduce the toll of infectious diseases on those with disabilities. At the same time, many populations of today’s high-income countries are in later stages of the transition and disability may increase (or fluctuate) commensurate with changes in health behaviors, advances in medical technology, interventions, and access to healthcare services (Harper and Armelagos 2010). Together, all of these factors could influence the levels and trends of disability (Robine and Michel 2004), and should be modeled properly under an integrated framework. We call for more research on investigations of the impact of environmental changes on disability trends that integrate an ecological model with a life course approach. Several other recommendations are omitted due to space limit. These recommendations include more research on trends by specific disability item, trends of onset of disability, trends of recovery, and changes in disease conditions in developing countries where such research is almost nonexistent. It is worth exploring the increased attention in recent years given to trends in subpopulations of the oldest-old (Christensen et al. 2013;Hungetal.2011; Jylhä et al. 2013). Some researchers have noted the importance of disability trends in oldest-old adults in that the majority of disabled older adults are from this subpopulation and, compared to disability trends among the young old, their disability condition and dynamic changes have more profound impacts on long-term care provision and finance. Another important yet understudied area is the lack of exploration of the possible impact of disability trends on the environment. This oversight is partly related to the indirect nature of these sorts of impacts, which makes it difficult to quantify, but also due to the fact that researchers and policymakers have a poor awareness about the existence of this issue. More research on this topic is certainly warranted. Some shortcomings of our proposed framework and this research are also noteworthy. First, the relatively detailed biomedical mechanism of disability and its trends is not depicted in the framework. Second, psychological distress and depression are considered as cognitive impairments in the central part of the framework, and thus are embedded in the framework yet were not extensively reviewed in the text. Related to this, the trends of cognitive/mental or social disability are not covered because only a very limited number of studies on these themes are available. Third, the interrelationship between different sets of factors and how they jointly affect the disability are not included. Fourth, it might be is difficult to distinguish medical interventions from changes in disease conditions due to medical progress in some cases, and distinguish behavioral interventions from changes in lifestyles by individual himself or herself, which may introduce some biases in estimating independent contribution of each element to disability trends. We have made no attempt to delineate each set of variables. The major goal of classification is to help better understand the interplay of different factors in affecting disability trends. Nevertheless, we believe the proposed framework provides a generalized yet holistic approach for studying disability trends in aging populations, and we hope this study provides J Cross Cult Gerontol some new evidence and insights to help better understand the disablement process, trends and their complex milieu of causes and consequences.

Acknowledgments R.D-R’s work was partly supported by Grants CSO2010-18925 from the Ministry of Science and Innovation. We are grateful to Professor Qiushi Feng at National University of Singapore, Dr. Alexander M. Kulminski at Duke University, and two anonymous reviewers.

Conflict of Interest None.

Authors’ Contributions D.G. and initiated and designed the study. D.G and R.G-R. conducted the literature view, drafted, and revised the manuscript. M.E.D. assisted in drafting and revising the manuscript.

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