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Aspects of Disability Across the Life Span: Risk Factors for Disability in Late Life

Jack M. Guralnik*

I have been working on the epidemiology of disability and risk factors for disability for 20 years. Until the Institute of Medicine (IOM) report Disability in America was published nearly 15 years ago (IOM, 1991), we were in a kind of wilderness in some respects. The IOM report, in elaborating on Nagi’s model of disability (Nagi, 1991), gave us a framework for our work. I trust that this workshop and the larger study of which it is a part will make a similar contribution to disability research in the future. It is particularly important that the study report focus on operational concepts that epidemiologists (like me) who undertake large population studies can use to measure disability and the steps from disease to disability in a valid, reliable manner.

This paper describes classic epidemiologic research on risk factors for disability and points out some of the challenges in trying to sort out the main causes of disability in the older population. I will note how aspects of this research relate to some of the mechanisms and pathways in the Nagi-IOM model.

In the early 1980s, the National Institute on Aging initiated a set of four large population-based studies called the Established Populations for Epidemiologic Studies of the Elderly (EPESE). The basic approach was to

*  

Jack Guralnik, M.D., Ph.D. Acting Chief, Laboratory of Epidemiology, Demography and Biometry, National Institute on Aging, Bethesda, Maryland. The analyses and views presented in this workshop paper are those of the author and not necessarily those of the Institute of Medicine Committee on Disability in America: A New Look.



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Workshop on Disability in america A new look: Summary and background papers H Aspects of Disability Across the Life Span: Risk Factors for Disability in Late Life Jack M. Guralnik* I have been working on the epidemiology of disability and risk factors for disability for 20 years. Until the Institute of Medicine (IOM) report Disability in America was published nearly 15 years ago (IOM, 1991), we were in a kind of wilderness in some respects. The IOM report, in elaborating on Nagi’s model of disability (Nagi, 1991), gave us a framework for our work. I trust that this workshop and the larger study of which it is a part will make a similar contribution to disability research in the future. It is particularly important that the study report focus on operational concepts that epidemiologists (like me) who undertake large population studies can use to measure disability and the steps from disease to disability in a valid, reliable manner. This paper describes classic epidemiologic research on risk factors for disability and points out some of the challenges in trying to sort out the main causes of disability in the older population. I will note how aspects of this research relate to some of the mechanisms and pathways in the Nagi-IOM model. In the early 1980s, the National Institute on Aging initiated a set of four large population-based studies called the Established Populations for Epidemiologic Studies of the Elderly (EPESE). The basic approach was to *   Jack Guralnik, M.D., Ph.D. Acting Chief, Laboratory of Epidemiology, Demography and Biometry, National Institute on Aging, Bethesda, Maryland. The analyses and views presented in this workshop paper are those of the author and not necessarily those of the Institute of Medicine Committee on Disability in America: A New Look.

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Workshop on Disability in america A new look: Summary and background papers study risk factors for the onset of disability in a population or subpopulation that was free of disability and then examine the development of incident cases of disability and the risk factors that predicted its onset. The condition that I will discuss is mobility disability, defined here as an inability to walk a quarter mile and an inability to climb a set of stairs. When my colleagues and I started our study, 72 percent of the cohort of 10,000 individuals was free of mobility problems at the baseline (Guralnik et al., 1993). Over a period that included four annual follow-ups, 53 percent of this group maintained mobility, 35 percent lost mobility, and a small percentage died without any evidence of mobility loss. At the baseline, we collected data on a number of chronic conditions that we hypothesized might predict mobility loss. We found the odds ratio for the loss of mobility to be in the range of about 1.2 to 1.5 for people with baseline reports of heart attack, stroke, diabetes, dyspnea, or exertional leg pain compared with the risk for people free of these conditions. A considerable amount of cross-sectional and longitudinal research, including some studies documented in these appendixes, has investigated a variety of potential risk factors for disability. A range of physical and behavioral risk factors have been shown to be associated with disability. These factors include low levels of physical activity, smoking, high and low body mass index, weight loss, heavy and no alcohol consumption, high levels of medication use, poor self-rated health, and reduced social contacts. Andreas Stuck did a very nice job of summarizing this body of research in a 1999 paper (Stuck, 1999). Among the chronic conditions that have been shown in epidemiologic studies to be associated with disability are heart disease and stroke, osteoarthritis, hip fracture, diabetes, peripheral artery disease, chronic obstructive pulmonary disease, cancer, visual impairment, depression, and cognitive impairment. This list of conditions is in no particular order, and people frequently ask what conditions are the primary causes of disability. This is actually a much more complicated question than it initially appears to be. Some of the issues involved in assessments of the overall impact of a chronic condition on disability include the strength of the association between the condition (risk factor) and a particular disability; the prevalence of the risk factor; and—putting these together—something called an “attributable risk,” which has been assessed for some conditions. Also, it is important to consider the disability outcome of interest, as Alan Jette has done (Jette, 1994). For example, are you assessing the difficulty of performing a certain activity or, more narrowly, the human assistance required to perform the task? Another important issue is population characteristics, such as age and gender. The main causes of disability may be different in men and women. For example, Ettinger and colleagues found arthritis-musculoskeletal dis-

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Workshop on Disability in america A new look: Summary and background papers TABLE H-1 Most Common Diseases Reported to Cause Difficulty with Specific Tasks   Men Women Activity Disease Percent Disease Percent Walking ½ mile Arthritis 33 Arthritis 43 Heart disease 13 Heart disease 12 Injury 9 Lung disease 9 Doing heavy housework Heart disease 26 Arthritis 45 Arthritis 24 Heart disease 15 Lung disease 11 Old age 8 Bathing Stroke 25 Arthritis 57 Arthritis 21 Injury 11   SOURCE: Adapted from Ettinger et al. (1994). ease and injury to be more important causes of disability for women than for men, whereas men are more likely to experience disability in association with heart disease, lung disease, and stroke (Ettinger et al., 1994). For both women and men, arthritis and musculoskeletal disease led as causes of disability by a considerable margin (reported by 50 percent of women and 30 percent of men), with heart disease being the next most often reported (reported by 13 percent of women and 16 percent of men). When the investigators looked not only at overall disability but also at specific conditions, they again found different results for men and women and found different results depending on the type of disability. As shown in Table H-1, for limitations related to the ability to walk one-half mile, do heavy housework, or bathe, women reported arthritis as the main cause for each of these limitations. For men, arthritis was the top reported cause for limitations in walking one-half mile; but for heavy housework, heart disease is slightly more important as a cause, and for limitations in bathing, stroke has a slightly greater role than arthritis. As mentioned earlier, the way in which disability is assessed affects what is found. For example, Suzanne Leveille has been interested in pain and its effect on functioning and disability. She and her colleagues have found consistent results in a number of different studies that show a significant association between pain and difficulties in climbing stairs and lifting as well as difficulties with activities of daily living (ADLs) (Leveille et al., 1999). However, if the measure is whether someone is not able to perform an activity at all, there is some increase related to levels of pain, but the relationship (the odds ratio) is not significant.

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Workshop on Disability in america A new look: Summary and background papers Another study by Leveille and colleagues shows similar results (Leville et al., 2001). For the categories of mild pain (at least one site), moderate pain (at least two sites), and widespread pain (at least three sites and both upper and lower body sites), people with pain report more difficulty with ADLs than people without pain; but people with pain are not more likely to be unable to perform ADLs or to need help from another person. In addition to individual chronic conditions, the co-occurrence of multiple conditions or comorbidities is very important in the older population. EPESE data show that as the number of chronic conditions increases, the risk of developing a new disability goes up rather dramatically for both men and women (Guralnik et al., 1993). Those who are not disabled at the baseline but who have four or more chronic conditions at that time are almost three times as likely to report mobility loss at follow-up. Some research suggests that synergistic or multiplicative effects on disability levels may exist for specific combinations of chronic conditions. This is difficult research to do, even with fairly large sample sizes. It is still not clear that a greater effect results from such combinations of conditions than would be expected simply from the additive effects of each condition. In addition to identifying relationships, colleagues and I have also tried to identify mechanisms by which risk factors operate in contributing to disability. Two examples of this work involve diabetes and low socioeconomic status as risk factors for disability. For diabetes, we first looked at the association between diabetes and several different functional or disability outcomes: mobility problems, ADL disability, and severe walking limitation (i.e., an inability to walk or walking less than 0.4 meters per second). We also included an additional objective measure of lower-extremity performance, the Short Physical Performance Battery (SPPB). Next we added into our statistical models several specific conditions and impairments (e.g., peripheral neuropathy, hypertension, and visual impairment) that are associated with diabetes. We then looked at the attenuation of the diabetes-function association, as measured by the odds ratio (for discrete outcomes) or the beta coefficient (for continuous outcomes). For each outcome, we found that each of the diabetes-related conditions reduced the initial association between diabetes and the functional outcomes (Volpato et al., 2002). No condition predominates, but when taken together, the conditions explain about 80 percent of the statistical association between diabetes and, especially, mobility and ADL outcomes. In addition, most of the conditions appear to be clinically plausible as explanatory factors. In other studies, we have looked at socioeconomic status (specifically, educational level) as a risk factor related to both total life expectancy and disability-free life expectancy. From the EPESE cohort from North Caro-

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Workshop on Disability in america A new look: Summary and background papers FIGURE H-1 Models of the pathway from disease to disability. lina, we found that for both white and African-American men and women, higher education is associated with longer life expectancy and longer disability-free life expectancy at age 65 years (Guralnik et al., 1993). Many other studies likewise show a relationship between socioeconomic status and disability outcomes. What might be the mechanisms here? Some findings from the InChianti Study (so named because it was undertaken in the Chianti region of Italy) are interesting. The focus was different physiologic subsystems that affect walking. These include the central and peripheral nervous systems, the muscular system, bones and joints, sensory systems, and the energy delivery system. Antonia Coppin, who is in my research group, looked at a variety of different impairments in these subsystems and how they mediate the relationship between low levels of education and both lower-extremity functioning and gait speed (Coppin et al., in press). She found two conditions that have a particularly high impact: trail making (a test of executive functioning that is related to educational status) and leg power. When all the factors are added into the analysis, they explain a very large proportion of the difference in lower-extremity function between people with lower and higher levels of education. Beyond this research, we have also attempted to do empirical research using the IOM model (Figure H-1). This has worked quite well. I present here examples that look at disease and impairment and subsequent functional limitations and then functional limitations and subsequent disability. We are trying to sort out just how our work will relate to the new International Classification of Functioning, Disability and Health (ICF) model (WHO, 2001), but so far we have based a lot of our research on the Nagi-IOM model. In operationalizing this model, the work of Lois Verbrugge and Alan Jette (1994) has been very valuable. One way that we have measured functional limitations uses the SPPB mentioned earlier, an approach first used in the EPESE study (Guralnik et

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Workshop on Disability in america A new look: Summary and background papers al., 1994; Guralnik et al., 2000). This battery has three components: standing balance, timed 4-meter walk, and the time required to rise from a chair five times. Each component is scored categorically from 0 to 4, and these scores contribute to a summary performance score that ranges from 0 to 12. In the Women’s Health and Aging Study, colleagues and I looked at individuals every 6 months. We analyzed data for people who had documented acute medical events—hip fracture, stroke, myocardial infarction (MI), and congestive heart failure (CHF). Women with none of these conditions clearly had the least decline in performance; those with hip fracture fared the worst (Ostir et al., 2002). (Changes in summary scores were −0.29 for no condition, −1.48 for CHF, −2.30 for MI, −2.63 for stroke, and −3.09 for hip fracture over the 6 month period when these events occurred.) Another study looked at depression as a risk factor for declines in the same objective performance measure (Penninx et al., 1998). That study found that people with greater numbers of symptoms of depression had greater declines in the SPPB. We have also studied the transition from functional limitation to disability. In one part of the EPESE study, we found that the higher (better) that the performance was on the SPPB, the less likely it was that an individual who was nondisabled at the start of the study would have an ADL or mobility limitation 4 years later (Guralnik et al., 1995). Using the results of this study in a clinical trial of exercise to prevent disability, we are employing the SPPB to target people without disabilities who have functional limitations and who are therefore at high risk of progressing to disability (Rejeski et al., 2005). The pace and the course of disability in the older population are also of interest. Colleagues and I have evaluated, using annual interviews over a 6-year period, what we call severe catastrophic and severe progressive disability, defined as follows: Severe disability: the individual needs help with three or more of six ADLs (eating, dressing, bathing, transferring, using the toilet, and walking across a small room) Catastrophic severe disability: an individual with severe disability in whom no ADL disability was identified in the preceding two interviews Progressive severe disability: an individual with severe disability in whom one or two ADLs were identified in the preceding interview We found that catastrophic disability is more common in the young old. Progressive disability is most common in those ages 85 years and older, a pattern consistent with what we think of the frailty of old age (Ferrucci et al., 1996). Although I have not considered them here, environmental and personal

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Workshop on Disability in america A new look: Summary and background papers FIGURE H-2 Percentage of American men and women age 50 years or older projected to survive to age 90 years and older. Compiled from U.S. life tables, National Center for Health Statistics. factors should not be forgotten, as they may affect the progression of potentially disabling conditions. The disabling potential of many of the conditions that I have discussed is affected by the physical environment, access to assistive technologies, and other environmental conditions. Let me close by recalling the demographic context for this discussion. Figure H-2 shows data that I developed from U.S. life tables starting in 1900. It shows the proportion of 50-year-old people expected to live to age 90 years or older. That proportion was tiny in 1900, but in 2000 more than 25 percent of 50-year-old women were projected to live to be 90 years old and older. Figure H-3 shows data that I compiled from EPESE data, specifically, data on disability in the year before death. For people in their 90s, the rate of disability in the years before death is extremely high. So, although the age-adjusted or age-specific rates of disability are declining in the United States, the overall numbers of older people with disabilities and the societal impact of disability will grow because so many more people will be in the very old age groups. Thus, identification of the causes of disability and interventions that can mitigate these causes or their effects will be increasingly important.

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Workshop on Disability in america A new look: Summary and background papers FIGURE H-3 Percentage of individuals age 70 years or older defined as disabled by year before death. REFERENCES Coppin AK, Ferrucci L, Lauretani F, Phillips C, Chang M, Bandinelli S, Guralnik JM. Low socioeconomic status and disability in old age: evidence from the InChianti Study for the mediating role of physiological impairments. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, in press. Ettinger WH Jr, Fried LP, Harris T, Shemanski L, Schulz R, Robbins J. Self-reported causes of physical disability in older people: the Cardiovascular Health Study. CHS Collaborative Research Group. Journal of the American Geriatric Society 1994 Oct;42(10):1035–1044. Ferrucci L, Guralnik JM, Simonsick E, Salive ME, Corti MC, Langlois J. Progressive versus catastrophic disability: A longitudinal view of the disablement process. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences 1996;51:M123-M130. Guralnik JM, LaCroix AZ, Abbott RD, et al. Maintaining mobility in late life. I. Demographic characteristics and chronic conditions. American Journal of Epidemiology 1993;137:845–857.

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Workshop on Disability in america A new look: Summary and background papers Guralnik JM, Simonsick EM, Ferrucci L, Glynn RJ, Berkman LF, Blazer DG, Scherr PA, Wallace RB. A short physical performance battery assessing lower extremity function: Association with self-reported disability and prediction of mortality and nursing home admission. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences 1994;49:M85-M94. Guralnik JM, Ferrucci L, Simonsick EM, Salive ME, Wallace RB. Lower-extremity function in persons over the age of 70 years as a predictor of subsequent disability. New England Journal of Medicine 1995; Mar 2;332(9):556–561. Guralnik JM, Ferrucci F, Pieper CF, Leveille SG, Markides KS, Ostir GV, Studenski S, Berkman LF, Wallace RB. Lower extremity function and subsequent disability: Consistency across studies, predictive models, and value of gait speed alone compared to the short physical performance battery. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences 2000;55:M221-231. IOM (Institute of Medicine). Disability in America. Washington, DC: National Academy Press, 1991. Jette, AM. How measurement techniques influence estimates of disability in older populations. Social Science Medicine 1994; 38:937–942. Leveille SG, Guralnik JM, Hochberg M, Hirsch R, Ferrucci L, Langlois J, Rantanen T, Ling S. Low back pain and disability in older women: independent association with difficulty but not inability to perform daily activities. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences 1999; Oct;54(10):M487–M493. Leveille SG, Ling S, Hochberg MC, Resnick HE, Bandeen-Roche KJ, Won A, Guralnik JM. Widespread musculoskeletal pain and the progression of disability in older disabled women. Annals of Internal Medicine 2001; Dec 18;135(12):1038–1046. Nagi, S. Disability Concepts revisited: implications for prevention, pp. 309–327. In Disability in America. Institute of Medicine. Washington, DC: National Academy Press; 1991. Ostir GV, Volpato S, Fried LP, Chaves P, Guralnik JM. Women’s Health and Aging Study. 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 2002; Sep;55(9):916–921. Penninx BW, Guralnik JM, Ferrucci L, Simonsick EM, Deeg DJ, Wallace RB. Depressive symptoms and physical decline in community-dwelling older persons. Journal of the American Medical Association 1998; Jun 3;279(21):1720–1726. Rejeski WJ, Fielding RA, Blair SN, Guralnik JM, Gill TM, Hadley EC, King AC, Kritchevsky SB , Miller ME, Newman AB, Pahor M. The Lifestyle Interventions and Independence for Elders (LIFE) pilot study: Design and methods. Contemporary Clinical Trials 2005;26:141-54. Stuck AE, Walthert JM, Nikolaus T, Bula CJ, Hohmann C, Beck JC. Risk factors for functional status decline in community-living elderly people: a systematic literature review. Social Science Medicine 1999; Feb;48(4):445–469. Verbrugge LM, Jette AM. The disablement process. Social Science Medicine 1994; Jan;38(1):1–14. Volpato S, Blaum C, Resnick H, Ferrucci L, Fried LP, Guralnik JM. Women’s Health and Aging Study. Comorbidities and impairments explaining the association between diabetes and lower extremity disability: The Women’s Health and Aging Study. Diabetes Care 2002; Apr;25(4):678–683. WHO (World Health Organization). International Classification of Functioning, Disability, and Health. Geneva, Switzerland: World Health Organization; 2001.