Predictors of Onset of and Recovery from Mobility Difficulty among Adults Aged Years

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1 American Journal of Epidemiology Copyright 1998 by The Johns Hopkins University School of Hygiene and Public Health All rights reserved Vol. 148, No. 1 Printed in U.S.A. Predictors of Onset of and Recovery from Mobility Difficulty among Adults Aged Years Daniel O. Clark, 12 Timothy E. Stump, 2 and Fredric D. Wolinsky 3 Relative to information on activities of daily living, information regarding the onset of and recovery from mobility difficulty has been limited. Drawing upon data gathered from 6,376 self-respondents aged years at baseline (1992) who were successfully reinterviewed in 1994 as part of the Health and Retirement Survey, the authors were able to build upon and add to knowledge gained from previous studies of the onset of and recovery from mobility difficulty. Hierarchical logistic regression was used to separate the direct and indirect effects of predictors of mobility difficulty onset and recovery at 2-year follow-up. To separate direct and indirect effects, the authors categorized various predictors as being related to sociodemographic factors, economic factors, health behavior, chronic disease, or physical impairment, and the categories were sequentially incorporated into a series of equations. The order in which the predictors were incorporated into the equations followed from a theoretical model of the disability process. In this study of mobility difficulty, the strongest direct predictors of recovery were having little baseline difficulty and the absence of diabetes mellitus, lung disease, and frequent pain. The strongest direct predictors of onset were female sex, less education, low net worth, lack of private health insurance, obesity, and frequent pain. Few indirect predictors for either onset or recovery were identified. Predictors of recovery were few and differed from predictors of onset. Further efforts are needed to identify modifiable predictors among females, persons with few economic resources, and those with frequent pain. Am J Epidemiol 1998; 148: aging; chronic disease; disability evaluation; risk factors Recent research indicates that mobility difficulty is a primary risk factor for the onset of disability. Studies have presented considerable evidence that mobility problems have a strong negative impact on the ability to perform basic and household activities of daily living, as well as on perceived health and the incidence of depression (1-4). This suggests that identification of predictors of the onset of mobility difficulty represents an important step in the development of effective disability prevention programs and policies. Thus, our objective in this analysis was to create separate models for prediction of the onset of and recovery from mobility difficulty at 2-year follow-up (1994) among the 6,376 self-respondents to the 1992 Health and Retirement Survey. In the several published reports on predictors of change in mobility status that do exist (2, 5, 6), socio- Received for publication April 17,1997, and in final form January 12, Department of Medicine, Indiana University Center for Aging Research, Indianapolis, IN. 2 Regenstrief Institute for Health Care, Indiana University School of Medicine, Indianapolis, IN. 3 School of Public Health, St. Louis University Health Sciences Center, St. Louis, MO. demographic characteristics and the prevalence of chronic health conditions received considerable attention. Race did not independently predict change in mobility status among respondents in the Longitudinal Study on Aging, but greater age and female sex did (6). Among respondents to the EPESE survey [Established Populations for Epidemiologic Study of the Elderly], age and female sex also predicted the onset of mobility limitations (2) and were associated with greater rates of decline and a lower probability of recovery (5). Lower income was a significant predictor of mobility loss among both men and women, while a lower educational level predicted loss among men only (2). In both the Wolinsky et al. (6) and Guralnik et al. (2) reports, the presence of diabetes mellitus, hypertension, stroke, and arthritis at baseline were predictive of onset of or increase in mobility difficulty. Predictors unique to one or the other study included leg pain, dyspnea, heart attack, Alzheimer's disease, and greater body mass index (weight (kg)/ height 2 (m 2 )). None of the above studies included behavioral predictors, but in a companion study to that of Guralnik et al. (2), LaCroix et al. (7) focused specifically on 63

2 64 Clark et al. behavioral predictors. They found current smoking among men, a body mass index at the 80th percentile or higher among women, and low levels of physical activity among both sexes to be predictors for the onset of mobility limitations. Relative to no alcohol consumption, low to moderate alcohol consumption was protective. The authors reported that the effects of the behavioral predictors were essentially unchanged when baseline age, education, and number of chronic conditions were controlled (7). The findings of the above studies represent important steps toward the identification of risk factors for mobility difficulty or loss. We incorporated each of the predictors identified in these studies into our analysis, and, using data from the Health and Retirement Survey, we were able to extend the findings of these studies by incorporating important additional predictor variables (e.g., comorbidity and physical impairments (8, 9)). In addition, we modeled difficulty onset and recovery separately, focused on a slightly younger age group, and estimated both indirect and direct effects. Separate models of onset and recovery may be important, given the fact that more than one fourth of persons who report having difficulty with activities of daily living or limitations in lower body function improve or recover by 2-year follow-up (6, 10). A focus on a younger age group may also prove valuable, because primary prevention is often most effective when the intervention population has low prevalence but elevated risk. Thus, a focus on persons aged years, the majority of whom have not yet experienced declines in lower body function, may provide important clues for the prevention of mobility difficulty and, ultimately, disability in activities of daily living. The identification of both direct and indirect effects has been noted to be a much overlooked but very important step in risk factor modeling (11, 12). Because the strongest proximate or direct causes are often not modifiable (e.g., age, sex), it is of interest to identify indirect effects, which may be modifiable. Age, for example, may have an effect on the onset of mobility difficulty via physical activity level, because quadriceps muscle activity is negatively correlated with age. Identifying this indirect effect allows for a more thorough understanding of predictors and thus greater potential for intervention (e.g., promotion of physical activity). In this study, we used the hierarchical regression approach to attempt to identify direct and indirect effects of sociodemographic factors, economic factors, health behavior, chronic disease, and physical impairment on mobility difficulty onset and recovery. MATERIALS AND METHODS Data collection The data for these analyses were obtained from self-responses to questionnaires administered during the 1992 (baseline) and 1994 (2-year follow-up) phases of the Health and Retirement Survey. The Health and Retirement Survey was funded by the National Institute of Aging and was coordinated and managed by the Institute for Survey Research at the University of Michigan (Ann Arbor, Michigan). The study was designed to provide in-depth data for the investigation of analytical and policy issues surrounding retirement; hence, it focused on persons aged years. Over 93 percent of the respondents were interviewed in person; the remainder were interviewed via telephone. The survey lasted approximately 1 hour for each respondent. The substantial breadth of data collected on work experiences, financial status, and health allowed the creation of more fully specified models than was previously possible. To identify age-eligible persons, the Health and Retirement Survey screened approximately 70,000 households obtained from an area probability sample. Census tracts containing a high density of African Americans or Mexican Americans were oversampled 2:1, as were census tracts in Florida. All spouses were interviewed regardless of age, because of the frequency of dual-earner couples and the influence of spouses in the retirement decision process. Thus, the Health and Retirement Survey also includes data on 2,912 persons either younger than 51 years or older than 61 years. These persons do not constitute a representative sample of persons in those age ranges, and they were excluded from the analyses presented here. The analyses were further restricted to baseline selfrespondents, because of the subjective nature of many of the baseline measures. The baseline proxy response rate was 5 percent, and among baseline self-respondents, the follow-up proxy response rate was 2 percent. The overall baseline response rate was 82 percent, and comparisons with 1990 US Census data provided no indication of sample bias (13). Among the 51- to 61-year-old self-respondents interviewed at baseline, 97 percent were reinterviewed in There were no significant demographic, economic, or chronic disease differences between those who were reinterviewed and those who were not. There were 6,437 selfrespondents aged years with complete mobility data at baseline and follow-up (table 1) and 6,376 self-respondents who had complete data on all measures used in the analyses (tables 2 and 3).

3 Onset of and Recovery from Mobility Difficulty 65 Measures considered The sociodemographic characteristics considered included a continuous measure for age; indicator variables for female sex, marital status, residence in the Southeast, and non-us birth; and a set of three dummy variables for ethnicity (African-American, Mexican- American, and white; white was the reference category). Economic measures consisted of indicator variables for an income (respondent and spouse combined) of $10,000 or less in the past year, $10,000 or less in total net worth, private health insurance, and Medicaid insurance. The questions regarding net worth covered real estate, business ownership, pensions, stocks and bonds, checking accounts, savings accounts, certificates of deposit, and all other sources of income. For missing information on income and net worth, values imputed by the Institute for Survey Research were used. By dichotomizing income and net worth at $10,000 or less, we minimized the potential implications of the imputation of missing values. Education was represented by a continuous measure for years of schooling completed, and an indicator variable identified persons currently working for pay. For the latter respondents, a nine-point occupational physical demands score (not shown) was created from responses to three statements: 1) "My job requires lots of physical effort," 2) "My job requires lifting heavy loads," and 3) "My job requires stooping, kneeling, or crouching." There were four possible responses, ranging from "all or almost all of the time" (coded as a 1) to "none or almost none of the time" (coded as a 4). The three items formed one factor, with factor loadings all above 0.75 and Cronbach's alpha at This variable was only used in subgroup analyses of persons who were currently working. The behavioral predictors studied consisted of alcohol consumption, smoking status, overweight, and physical inactivity. Alcohol consumption. The CAGE scale is a fouritem alcohol use scale (14) designed to determine whether an individual has ever abused alcohol. The four CAGE items are: 1) "Have you ever felt you should cut down on your drinking?"; 2) "Have people ever annoyed you by criticizing your drinking?"; 3) "Have you ever felt bad or guilty about drinking?"; and 4) "Have you ever taken a drink first thing in the morning to steady your nerves (an eye-opener)!". A score of 2 or more is indicative of alcohol abuse. Smoking. Markers were created for current smokers and for former smokers who had quit smoking within the past 10 years. Nonsmokers and those who had quit more than 10 years previously formed the reference group. Overweight. Respondents at or below 90 percent of their ideal body mass were considered underweight, while those at or above 140 percent of their ideal body mass were considered obese. Those between 91 and 139 percent of their ideal body mass formed the reference group. Physical activity. A set of three dummy variables was used to capture physical activity levels. A high level of physical activity was represented by a selfreport of vigorous physical activity (e.g., swimming, aerobics, biking) at least once per week. A medium level of physical activity was represented by a selfreport of moderate physical activity (e.g., gardening) at least once per week but no weekly vigorous physical activity. Reports of no weekly vigorous or moderate physical activity formed the reference category. Disease was represented by a series of indicator variables for self-reports of chronic disease. In the Health and Retirement Survey, it is possible to identify those who report having been told by a doctor that they have high blood pressure or hypertension, diabetes or high blood sugar, cancer (other than skin cancer), or chronic lung disease (excluding asthma) such as chronic bronchitis or emphysema. Respondents were also asked, in a single question, whether they had ever been told by a doctor that they had angina, coronary heart disease, congestive heart failure, heart attack, or another heart problem. In a separate question, respondents were asked whether they had ever been told by a doctor that they had had a stroke. Finally, respondents were asked whether they had ever had or had ever been told by a doctor that they had arthritis or rheumatism, and whether they had seen a doctor for psychiatric problems in the past year. Measures for physical impairments captured information on sensory deficits and pain. Vision impairment has important consequences for difficulty and functional limitation, while the role of hearing impairment appears to be less significant (15, 16). Vision and hearing were self-rated by the use of glasses, contact lenses, or hearing aids, with responses dichotomized as fair/poor eyesight or hearing versus other. Pain was defined as a positive response to the question, "Are you often troubled with pain?" Experimental studies have demonstrated that such self-reports of pain are reliable (17). Dependent variable The lower body mobility scale consisted of reports of any difficulty in performing three mobility tasks: 1) walking one block, 2) walking several blocks, and 3) climbing one flight of stairs without resting. At baseline, the questions read, "How difficult is it for you to...?", and the possible responses were "not at

4 66 Clark et al. all," "a little," "somewhat," "very," and "don't do." At follow-up, the questions read, "Do you have any difficulty with...?", and the response set consisted of "yes," "no," and "don't do." We coded the baseline responses of "a little," "somewhat," and "very" and the follow-up response of "yes" as l's, indicating difficulty. Accordingly, possible scores on the threeitem scale ranged from 0 (no difficulty on any item) to 3 (difficulty on every item). Persons who reported that they did not do the activity (less than 2 percent of the sample) were not included in our analyses. Exploratory factor analysis models yielded minimum factor loadings of 0.79; Cronbach's alpha was 0.71 at baseline and 0.72 at follow-up. Analyses In hierarchical logistic regression, variables are entered sequentially into successive models, allowing the estimation of both direct and indirect effects of independent variables. In the analyses described here, we estimated mobility difficulty onset and recovery in separate models and sequentially incorporated categories of variables one at a time. We first incorporated sociodemographic characteristics and then economic measures, followed by health behavior measures, chronic disease indicators, and finally physical impairments. In this way, we were able to separate the indirect effects and direct effects of each of the categories of variables. Model fit was assessed through the use of the receiver operating characteristic curve and the Hosmer-Lemeshow goodness-of-fit-statistic (18). RESULTS The incidence of recovery from mobility difficulty was substantially greater than the incidence of onset (table 1). In fact, 56 percent of the 1,420 respondents who reported having difficulty at baseline no longer reported difficulty at follow-up, while slightly more TABLE 1. Baseline prevalence (proportion) and 2-year follow-up reports of mobility difficulty onset and recovery among 6,437 self-respondents to the Health and Retirement Survey, 1992 and 1994 Baseline (1992) Difficulty No difficulty Total Difficulty 0.44 (n = 621) 0.06 (n = 303) 0.14 (n = 924) Follow-up (1994) No difficulty 0.56 (n = 799) 0.94 (n = 4,714) 0.86 (n = 5,513) Total 0.22 (n= 1,420) 0.78 (n = 5,017) 1.00 (n = 6,437) than 6 percent of the 5,017 respondents with no difficulty at baseline reported having difficulty at followup. In addition, 53 percent of those who had recovered by the time of the 2-year follow-up had recovered from a self-report of "a little" difficulty on just one lower body item at baseline (data not shown). To conserve space, we have not shown comparisons between the measured characteristics of persons with and without difficulty at baseline. Because of the large sample size, most differences were statistically significant. With the exception of sex, however, sociodemographic characteristics did not vary substantially by baseline difficulty status; 52 percent of respondents without difficulty were female versus 66 percent of those with difficulty. On the other hand, several of the economic indicators varied substantially by difficulty status. The percentages of respondents with low family income, low net worth, and Medicaid insurance were over two times higher among those with baseline mobility difficulty. Similarly, those with difficulty were twice as likely to be at 140 percent or more of their ideal body mass and to report low physical activity, and they were 50 percent more likely to currently smoke. Chronic diseases and physical impairments were all percent more prevalent among persons with difficulty. Adjusted odds ratios from the hierarchical analysis of difficulty onset are shown in table 2. Model 1 indicated that Mexican Americans were 2.8 times and African Americans 1.7 times as likely to experience difficulty onset as were Caucasians, while females were nearly 1.7 times as likely to experience onset as males. Each year of age was associated with a 1.06 greater risk of difficulty onset. In a separate model (data not shown), we tested the assumption of linearity by replacing the continuous age variable with a set of five 2-year dummy variables (51-53 years was the reference age group). Those analyses showed that the risk of difficulty onset was equivalent for persons aged years but greater for persons aged years. Model 2 incorporated economic indicators. The excess risk of mobility difficulty among Mexican Americans and African Americans was an entirely indirect risk that operated through economic status. The effects of female sex and age, on the other hand, remained and did not appear to indirectly affect difficulty onset via economic status. Each year of education was associated with an 11 percent lower likelihood of onset, while a net worth of $10,000 or less was associated with a 96 percent increase in the likelihood of onset. Persons with private health insurance were 0.56 times as likely to experience onset as those without it. In analyses limited to those working for pay (data not shown), we tested whether physical demands at work

5 Onset of and Recovery from Mobility Difficulty 67 TABLE 2. Adjusted odds ratios obtained from hierarchical logistic regression models of onset of lower body difficulty among 4,974 self-respondents to the Health and Retirement Survey, 1992 and 1994 Independent variable Sociodemographic (actors Age (years) Female sex Married (vs. not married) Not bom in the United States Living in the South Mexican American (vs. white) African American (vs. white) Model! Model 2 Model 3 Model **** 0.72* **** 1.70*** 1.54** *** 1.78**** *** Model *** Economic factors Family income below $10,000/year Net worth below $10,000 Years of education Medicaid insurance (vs. no Medicaid) Private health insurance (vs. no private insurance) Working for pay *** 0.89**** *** 0.71* *** 0.90**** *** 0.69* *** 0.91* ** *** 0.70* ** 0.91**** *** 0.72 Health behaviors Body mass indext 90% of ideal body weighty Body mass index 140% of ideal body weighty Current smoker (vs. never smoker) Former smoker (vs. never smoker) CAGE scale score Z2 High level of physical activity (vs. medium level) Low level of physical activity (vs. medium level) Chronic diseases High blood pressure Current diabetes mellitus Cancer Lung disease Heart disease Arthritis Stroke Psychiatric problems in past year Physical impairments Fair or poor eyesight Fair or poor hearing Bothered by pain **** 1.64** * 0.629** **** 1.65** * * 1.51** **** 1.65** * ** **** Model statistics Receiver operating characteristic Hosmer-Lemeshow p value * p<, 0.05; * p <, 0.01; *** p <, 0.001; * p <, t Weight (kg)/height* (m*). $ Reference group: body mass index of See text (14). were responsible for the modest association between work status at baseline and difficulty onset. We found no association between scores on the occupational physical demands scale and risk of difficulty onset. The direct effects of model 2 were essentially unchanged by the incorporation of health behaviors (model 3). Persons with a baseline body mass index of 140 percent or more of their ideal weight were 2.3 times as likely to experience difficulty onset as those with a body mass index in the 91st 139th percentile,

6 68 Clark et al. and those who smoked at baseline were 1.6 times as likely to experience onset as nonsmokers. Respondents who scored 2 or more on the CAGE alcohol abuse scale were 1.4 times as likely to experience onset as those who scored less than 2, while those who reported participating in vigorous physical activities at least once per week at baseline were 0.63 times as likely to experience onset as those who engaged in no physical activity. Although there is some indication that a small portion of the effects of obesity may have operated indirectly via chronic disease, the majority of the effects of model 3 were unchanged in model 4. Persons who reported having been diagnosed with arthritis or heart disease were 1.5 times as likely to experience difficulty onset as those who did not. Physical impairments were incorporated in model 5. Persons who reported being often bothered by pain at baseline were 2.1 times as likely to experience difficulty onset as those who did not, and those reporting fair or poor eyesight were 1.6 times as likely. The effects of arthritis and heart disease were no longer significant with the addition of physical impairments, and separate analyses (not shown) indicated that this modest drop was the result of an indirect effect via pain. The receiver operating characteristic (0.766) and Hosmer-Lemeshow (p = 0.322) statistics suggested that this final model had an acceptable fit to the data. We incorporated a few select interaction terms one at a time into model 5. Following the findings and speculations of other authors, we tested for interactions between indicators for arthritis and obesity (19, 20), arthritis and heart disease (16), obesity and African-American ethnicity (21), diabetes and African- American ethnicity (22), and non-us birth and Mexican-American ethnicity (23). None of these terms were statistically significant. Table 3 shows results from logistic regression analysis for recovery of lower body function. The five models created were identical to those for difficulty onset, with the exceptions that no interaction terms were estimated (there are no existing reports or hypotheses with which to guide tests of interaction terms for recovery from mobihty difficulty) and an indicator variable was included in each model for persons who recovered from a report of "a little" difficulty on just one item at baseline. (These recoveries were modest and would be the most likely to be influenced by instrumentation changes between baseline and followup.) The estimates shown at the top of table 3 under "Model 1" indicate that persons with "a little" difficulty on just one item had a 3.2 times' greater likelihood of recovery than those with greater levels of baseline difficulty. In addition, with all variables controlled (i.e., model 5), persons with "a little" difficulty on just one item at baseline had a 2.3 times' greater likelihood of recovery. Although model 1 of table 2 showed that Mexicanand African-American respondents were more likely to experience difficulty onset, the estimates shown in model 1 of table 3 do not indicate any effects of ethnicity on recovery; neither are any sex effects apparent. Age had a modest negative association with recovery in model 1, and the incorporation of economic indicators in model 2 did not alter that effect. Each year of education (model 2) was associated with a 1.06-fold greater likelihood of recovery. Respondents with a net worth of less than $10,000 were 0.69 times as likely to recover as those with a greater net worth, and those with Medicaid insurance were 0.41 times as likely to recover. Baseline health behaviors were incorporated in model 3, and none of them affected the odds of recovery. The associations of age and Medicaid insurance shown in model 3 were reduced somewhat by the incorporation of chronic disease in model 4, suggesting that older respondents and those on Medicaid were less likely to recover in part because of greater comorbidity. Having high blood pressure, diabetes, lung disease, or arthritis lowered the likelihood of recovery percent. Model 5 indicated that persons who reported at baseline that they were often bothered by pain were 0.55 times as likely to experience recovery as those who were not often bothered by pain. As with difficulty onset, model 5 suggested an indirect effect of arthritis via pain. A CAGE score of 2 or more had a marginal positive association with the odds of recovery. The model fit statistics indicate an acceptable fit for this final model (receiver operating characteristic = 0.746; p = 0.693). DISCUSSION In these analyses, we attempted to improve understanding of the predictors of mobility difficulty by creating separate models for onset and recovery and by utilizing a theoretically guided hierarchical approach to identify indirect effects. The strongest predictors of difficulty onset were female sex, less education, lack of private health insurance, high body mass index, and frequent pain. Age, low net worth, current smoking, lack of physical activity, and fair or poor eyesight were also significant predictors of onset. Predictors of 2-year recovery from difficulty were few, and with the exception of being bothered by pain, they differed from predictors of onset in this late-middle-aged group. The strongest predictors of recovery in the final model were mild baseline difficulty and an absence of

7 Onset of and Recovery from Mobility Difficulty 69 TABLE 3. Adjusted odds ratios obtained from hierarchical logistic regression models of recovery from lower body difficulty among 1,402 self-respondents to the Health and Retirement Survey, 1992 and 1994 Independent variable Lower body difficulty at baseline Self-report of "a little" lower body difficulty on one item at baseline Model **** Model **** Model **** Model **** Model **** Sociodemographic factors Age (years) Female sex Married (vs. not married) Not born in the United States Living in the South Mexican American (vs. white) African American (vs. white) 0.96* * * Economic factors Family income below $10,000/year Net worth below $10,000 Years of education Medicaid insurance (vs. no Medicaid) Private health insurance (vs. no private insurance) Working for pay * 0.41** * 1.06* 0.40** * 0.49* * 0.54* Health behaviors Body mass indexf <90% of ideal body weighty Body mass index >140% of ideal body weight* Current smoker (vs. never smoker) Former smoker (vs. never smoker) CAGE scale score 2 High level of physical activity (vs. medium level) Low level of physical activity (vs. medium level) * Chronic diseases High blood pressure Current diabetes mellitus Cancer Lung disease Heart disease Arthritis Stroke Psychiatric problems in past year 0.70** 0.45**** **** ** ** 0,44**** **** Physical impairments Fair or poor eyesight Fair or poor hearing Bothered by pain **** Model statistics Receiver operating characteristic Hosmer-Lemeshow p value * p <, 0.05; ** p <, 0.01; *** p 0.001; **** p t Weight (kg)/heighp (m*). i Reference group: body mass index of See text (14).

8 70 Clark et al. diabetes, lung disease, and frequent pain. The presence of some unique predictors and the apparently considerable differences in the effects of the various economic status indicators suggest that separate theoretical frameworks of onset and recovery may be necessary. At the very least, further investigation of the unique effects of the various economic indicators seems warranted. Few indirect mechanisms for onset and recovery were identified. Arthritis operated via pain, and Mexican- and African-American ethnicity operated via economic status. The considerable and persistent direct effects of economic indicators on recovery and onset are perplexing. With baseline sociodemographic characteristics, health behaviors, chronic disease, and physical impairments controlled, respondents with low educational attainment, low net worth, or no private insurance were at substantially greater risk of difficulty onset. Persons with Medicaid insurance were nearly one half as likely to recover as persons without Medicaid. While health insurance status may change frequently, net worth and education are relatively stable and are likely to capture a person's economic status over the course of adulthood. This supports the view that poor economic status represents a cumulative health risk that accrues over many years. Females were more likely than males to have mobility difficulty at baseline and to experience onset of mobility difficulty, but they were no more likely to recover. This is consistent with the findings of other studies (24-26) which have shown that females have poorer functional status scores at baseline and experience greater average rates of decline over periods ranging from 2 years to 10 years. What is not fully consistent with existing literature is our finding that the effects of female sex remained essentially unchanged after the incorporation of economic, health behavior, chronic disease, and physical impairment measures. Other investigators have shown that these greater rates of difficulty among women are in large part determined by economic indicators. Maddox and Clark (24) found that income and education accounted for a majority of the greater mean rates of and 10-year declines in functional status among women relative to men aged years. Verbrugge (25) and Ross and Bird (26) had access to data on a wide range of social and behavioral predictors, and they found that among adults aged 18 years and over, female sex disadvantages in difficulty, morbidity, and perceived health were statistically accounted for by these predictors. There are three potential explanations for the differences in the findings associated with female sex in this report and those of other researchers (24-26). First, each study used different measures of mobility difficulty, and direct and indirect predictors for difficulty are likely to depend on the particular measures employed. In fact, Wolinsky et al. (6) used a mobility difficulty measure very similar to that used here, and their results showed that females aged S70 years were more likely than males to experience decline even after data were controlled for sociodemographic, economic, and disease indicators. Second, the study by Verbrugge (25) and Ross and Bird (26) used crosssectional data; the predictors may account for sex differences at a particular point in time but not account for differences in prospective risk. This hypothesis, however, is not consistent with the panel data results reported by Maddox and Clark (24). Third, Verbrugge (25) and Ross and Bird (26) had access to information on psychosocial measures such as stress, sense of control, and health attitudes, and these psychosocial factors may be the primary source of sex differences in difficulty and/or perceived health. Again, however, this hypothesis is not fully consistent with the findings of other studies. Thus, the hypothesis that would seem most consistent with the findings of each of these studies is that different measures of physical function and health status have very different predictors. The primary limitation of these analyses is the modest change in wording between the baseline and follow-up questions on mobility difficulty. It has been shown that differences in the wording of questions can produce quite different rates of difficulty (27). Thus, we were concerned that the modest change in instrumentation might have biased the results. Fortunately, several questionnaire modules were tested in baseline subsamples, and one of those modules (n = 684) included a "difficulty" question with the same stem and response set as those used at follow-up (i.e., "Do you have any difficulty...?"/["yes," "no," "don't do"]). However, "Do you have any difficulty walking?" was the only module question that was similar to the lower body items included in our scale. Comparison of the distribution of responses to this module question with the distribution of responses on the lower body scale item, "How much difficulty do you have walking several blocks?", yielded an expected pattern. Ninety-two percent of those who responded affirmatively to the module question also reported having at least "a little" difficulty walking several blocks. Eight-five percent of those who responded negatively to the module question also reported no difficulty in walking several blocks. Nonetheless, as an added safeguard, we also implemented the models with alternative coding schemes. Persons who reported having "a little" difficulty at baseline, for example, were coded zero along with those who reported having no difficulty. Results from analyses based on

9 Onset of and Recovery from Mobility Difficulty 71 this and other alternative coding schemes did not differ significantly from those shown. Therefore, it is unlikely that the modest change in instrumentation affected these findings. The identification of predictors of difficulty through survey data is a necessary but insufficient step toward the identification of modifiable risk factors. Predictors were categorized into blocks, and we incorporated these blocks into the models one at a time in hopes of isolating the impact of modifiable predictors. This approach had limited success. Female sex is clearly immutable, and net worth and education are also essentially immutable. Whether or not the remaining significant predictors are modifiable depends in large part on the social and cultural context in which the predictors exist (28). The perception of pain, for example, might be modifiable through pain management techniques, but these techniques may not be acceptable in some subcultures. Improvements in insurance coverage do not appear possible in the current political context of the United States. Similarly, rates of obesity, smoking, and physical activity, and thus of lung disease, diabetes, and several other chronic diseases, will be more modifiable in some contexts than in others. Mobility difficulty appears to play a very large role in health and disability in later life, and the older adult population is projected to increase substantially in the coming decades. Identification of modifiable predictors and their social contexts will play an important role in reducing the dependency rates and care costs associated with such disability in future cohorts of older adults. ACKNOWLEDGMENTS This work was supported in part by National Institutes of Health grants R29-AG to Dr. Daniel Clark and R37- AG to Dr. Fredric Wolinsky, and an American Association of Retired Persons Andrus Foundation Grant to Drs. Clark and Wolinsky. REFERENCES 1. 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