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Variation in Thresholds for Reporting Mobility Disability Between National Population Subgroups and Studies

¹ Department of Public Health and Primary Care, University of Cambridge, U.K.
² MRC Biostatistics Unit, Institute of Public Health, Cambridge, U.K.
³ Department of Psychiatry and Institute for Research in Extramural Medicine, Vrije Universiteit, Amsterdam, The Netherlands.
⁴ Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, Maryland.

Address correspondence to Dr. David Melzer, Department of Public Health and Primary Care, University of Cambridge, Forvie Site, Robinson Way, Cambridge CB2 2SR, U.K. E-mail: dm214{at}medschl.cam.ac.uk

	Abstract

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 Background. Disability questions require older people to report difficulties with everyday activities, using broad categorical responses. Relatively little is known about population group differences in the thresholds for reporting difficulty or inability with medium-distance mobility against tested mobility-related performance. We aimed to estimate the thresholds on tested performance at which self-reports change from one category to another, across a range of sociodemographic subgroups. We also aimed to compare reported and tested performance across two national population studies.

 Methods. The samples were from the third U.S. National Health and Nutrition Examination Study (NHANES III) and the Longitudinal Aging Study Amsterdam (LASA). Measures of gait speed, chair stands, and peak expiratory flow rate in both studies yielded the validated index of mobility-related physical limitations (MOBLI). Latent probit models were used to estimate cutpoints (thresholds) on the index for reporting difficulty or inability to walk a medium distance.

 Results. Thresholds for reporting difficulty or inability were studied by age, sex, race, educational level, and income in NHANES III. In models adjusting for the other factors, performance thresholds for reporting disability categories varied by age and income. The younger elderly persons in NHANES III on average reported difficulties or inabilities only when they reached a more severe level of tested limitation compared with older old persons. A similar pattern exists for those on higher incomes. For race, differences in threshold were present only for reporting inability, but not difficulty. Significant differences in thresholds were not present between groups defined by sex or for years of education. Comparisons between the NHANES and LASA studies show that lower reported mobility difficulty or inability prevalence in the Dutch sample is attributable both to reporting at higher levels of limitation and to better functioning.

 Conclusions. There is evidence of differences in thresholds for reporting mobility disability, especially across age and income groups in older Americans. Further work is needed to understand the perceptual, attitudinal, or environmental factors that cause these reporting differences.

Questions on disability require respondents to report levels of difficulty (e.g., none, some, much, or unable) without indicating when these terms should be used. The categorical responses identify groups with functional decline due, most commonly, to arthritis, cardiovascular disease, and cognitive impairment (2). However, the environment also influences difficulty with everyday tasks: Older people faced with high ambient temperatures or upward slopes, for example, may report difficulty walking a quarter of a mile at relatively less severe levels of physical health problems than those facing normal temperatures or flat ground. In addition, attitudes toward reporting difficulty might also influence responses, with differing thresholds for admitting difficulty. The net result of these factors is so-called "response category cutpoint shifts" for self-reported health status (3).

One way of conceptualizing the response categories to disability questions is to see them as resulting from a mapping process onto a continuous measure of relevant aspects of physical health. The transition from reporting "no difficulty" to "difficulty" occurs at a cutpoint or threshold on the underlying physical health status measure. If cutpoints differ systematically across populations, or even across sociodemographic groups within a population, then the disability reports are not comparable. A striking example of shifted thresholds is from Kerala State in India, which has the lowest mortality and illiteracy rates but consistently has the highest self-reported morbidity rates (4). For disability studies, recent analyses by the World Health Organization have also confirmed that different subpopulations have significantly different attitudes toward reporting disabilities, including mobility disability (5,6).

We have recently reported the development of the index of mobility-related limitations (MOBLI) (7) for epidemiologic use comparing the physical component of mobility disability across populations or over time. In logistic regression models, three measures were identified as being most strongly associated with reported difficulty or inability in walking a medium distance (a quarter of a mile) in the third U.S. National Health and Nutrition Examination Study (NHANES III): gait speed, time to complete five chair stands, and peak expiratory flow rate. We have since shown that the MOBLI is predictive of mortality over 4 years in the U.S. Established Populations for the Epidemiologic Studies of the Elderly Study (8). MOBLI also had good sensitivity and specificity in the Longitudinal Aging Study Amsterdam (LASA) and was responsive to change over two 3-year periods of follow-up (9).

The MOBLI is thus well suited as a measure of "mobility-related physical health" for studying response shifts. In this analysis, we aimed to estimate the thresholds on tested performance at which self-reports change from one category to another, across a range of sociodemographic subgroups. We also aimed to compare reported and tested performance across two national population studies.

	METHODS

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Study Sample
The NHANES III was a cross-sectional study of the U.S. population, undertaken between 1988 and 1994 (10,11). Complex clustered samples of the civilian, noninstitutionalized populations were used. Of 6596 persons aged 60 or over interviewed in their homes, a total of 5724 took the further examinations (5302 attended the mobile examination center, and 422 were examined in their own houses because they were unable to attend the center). One hundred ninety-three persons whose self-reported walking information was not available were excluded, and the remaining 5531 were included in this analysis. Data sets used in this analysis were from the Public Use Data File (12).

LASA includes a representative sample of The Netherlands (13–15). In brief, it is a longitudinal study of predictors and consequences of changes in well-being and autonomy in the older population. Thus far, the LASA study has conducted three cycles of interviews and performance tests, approximately 3 years apart. From September 1992 to September 1993, 3107 persons participated in the LASA baseline interview. To be comparable with the NHANES III data set, respondents aged 60 or over, living in the community, with measured and self-reported mobility information were included in the analysis. A total of 2115 respondents were eligible for this analysis.

Measurement of Variables
MOBLI.-- The MOBLI included measured average gait speed, time to complete five chair stands, and peak expiratory flow rate (6). The MOBLI is the calculated overall (whole NHANES III population) probability of reporting difficulty (or inability) in walking a quarter mile, based on the logistic regression models including the three measures. The MOBLI score varies from 0 to 1: The higher the score, the higher the probability of mobility-related limitation and of reporting medium-distance walking difficulty or inability. (Details of the MOBLI equations and calculation of scores are available online [16].)

Self-reported mobility disability.-- In NHANES III, the question on difficulty in walking a quarter mile had four response categories: without, some, much, and unable to do. In this analysis we dichotomized responses into (1) "any difficulty" versus none or (2) "inability" versus ability.

In LASA pilot studies (17), questions on "walking 400 meters" and "walking for 5 minutes" were asked. Walking for 5 minutes at a normal pace (5 km/h) is equivalent to walking 400 meters or a quarter of a mile, and the questions gave similar results. Ultimately, the "walking for 5 minutes" question was chosen for LASA, as conveying a clearer concept to respondents.

Sociodemographic variables that separate subgroups.-- Sociodemographic variables in this analysis include sex, age, race, and income and educational level. Levels of income were classified by the poverty income ratio (on the midpoint of observed family income category as the numerator and the poverty threshold, the age of the family reference person, and the calendar year in which the family was interviewed as the denominator). Tertiles of the weighted whole poverty income ratio distribution were used to classify people's income as low (0.025–1.708), middle (1.709–3.239), or high (3.240). Levels of education were based on the number of completed years of education, coded as low (0–7 years), middle (8–11 years), or high (12 years). Those not answering questions on educational or income level were coded as missing.

Statistical Analysis
Latent probit model.-- Statistically, we assume there is a latent variable representing an individual's relevant physiologic status. With the use of this, we can estimate and compare difficulty or inability thresholds (cutpoints) on this scale. In this analysis, the latent variable is measured using the MOBLI index. To estimate the difficulty or inability cutpoints, we used the Generalized Linear Latent and Mixed Models (GLLAMM) Program, developed by Rabe-Hesketh and colleagues for use in STATA (18,19). In our model, the binary response y_i of person i to the self-reported mobility question is modeled using a probit model with underlying response y_i*. The latent variable can be described by the measured mobility level of person i. That is, we assume y_i* N(µ_i,1) and µ_i = x_iß, where x_i is the MOBLI score and ß is its associated coefficient. The observed responses y_i (0: no difficulty or no inability; or 1: with difficulty or with inability) are generated assuming a threshold (response category cutpoint) model with person-specific thresholds (response category cutpoint) _i. That is, y_i = 0 if y_i* < _i and y_i = 1 if y_i* _i. The ith person-specific threshold (response category cutpoint) _i is a linear combination of covariates (z_i, individual-level sociodemographic characteristics) and can be expressed as _i = z_i', where is the coefficient vector associated with z_i and is estimated by maximum likelihood, assuming responses are independent across individuals. In this cutpoint model, the covariates include age, sex, race, educational level, income level, and national study.

To understand individual effects and the overall adjusted effect of each variable on people's threshold for reporting disability, we built latent probit models for each variable separately and then together. In each model, the first subgroup of the variable was fixed as the baseline category and the estimated coefficient and standard errors for other subgroups were calculated. The level of statistical significance was set at p <.01.

In the analyses based on each variable, the underlying mobility function measure was rescaled from 0 to 1, where 0 corresponds to the lowest level of functional limitation (i.e., no functional limitation) and 1 corresponds to the highest level of functional limitation. The estimated difficulty or inability cutpoints were also appropriately rescaled to lie between 0 and 1. The 95% confidence intervals for these rescaled cutpoints were also calculated.

	RESULTS

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Comparisons of Cutpoints for Different Subgroups in the NHANES III Study
In the NHANES III sample (Table 1), the proportions of male and female are nearly equal. Most of the sample were aged 60–69 (41.7%) and were White (59%). One-third (34.4%) reported having difficulties in walking a quarter of a mile, and about one-third of them (706/1901) were unable to do it.

View larger version (31K): [in this window] [in a new window]   Figure 1. Diagram showing rescaled estimated mobility index cutpoints (with 95% confidence intervals) for the reporting of difficulty or inability, in different subgroups (Y axis: index of mobility-related limitations [MOBLI] score; 1 = most severe measured impairments)

View larger version (29K): [in this window] [in a new window]   Figure 2. Diagram showing rescaled estimated mobility index cutpoints (with 95% confidence intervals) for the reporting of difficulty or inability, in the United States and The Netherlands (Y axis: index of mobility-related limitations [MOBLI] score; 1 = most severe measured impairment)

	DISCUSSION

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In similar analyses of the NHANES III data set, Iburg and colleagues (5) compared self- and physician reports with a latent variable based on eight available physical tests, namely, shoulder external rotation—right and left, hip and knee flexion—right and left, timed 8-ft walk, timed tandem stand, and five timed chair stands. The analysis assumed that this latent variable represented an individual's true mobility level. That analysis similarly showed that thresholds for reporting difficulties occurred at more severe levels of tested impairment in men compared with women, in non-White respondents compared with White respondents, and in those on higher incomes compared with those on lower incomes. Two limitations of this earlier work have been addressed in our analyses: namely, the use of an unvalidated statistical latent variable in contrast to our use of the MOBLI and the analysis of age and educational level in addition to sex, race, and income level as grouping variables. A key difference between the Iburg latent variable and the MOBLI score is that the latter included expiratory flow rate, which was empirically found to be more closely associated with medium-distance mobility than the upper-extremity and balance tests. In addition, Iburg and colleagues (5) did not explore cross-study comparisons.

NHANES Analyses
A number of limitations in this analysis need to be considered in assessing the results. First, the NHANES III study included community-living older people only. One common hurdle in analyzing performance test data is missing data, but here we have used the MOBLI, which includes "missing" as a category in score calculation. In addition, "missing" categories were included for grouping variables. The use of the latent probit model, although less familiar, is conceptually very similar to logistic regression models: In logistic regression, group differences are measured by relative odds, whereas in probit models, thresholds are reported. The traditional probit model is widely used in calculation of doses required to achieve target effects (20,21). The use of this statistical technique should therefore not be problematic.

Our analyses also have a number of strengths. The NHANES III study is a high-quality national study with a relatively large sample of older people, combining self-report and tested performance relevant to medium-distance mobility. Our use of the MOBLI score as a continuous measure of the physical component of mobility disability provides a strong foundation for the analysis, given the construct validity of the MOBLI, plus the evidence for its good test characteristics, predictive validity, and sensitivity to change.

The results of the analysis of NHANES III are clear-cut: Thresholds for reporting disability categories vary by age and income level when compared with tested performance in models adjusted for the other studied factors. Thus, younger elderly individuals on average report difficulties or inabilities only when they reach a more severe level of tested impairment compared with older old individuals. A similar pattern exists for higher income level. At the same time, significant differences were not present between groups defined by sex or for the main categories of educational level. For race, differences in threshold were present only for reporting "inability" but not difficulty.

This analysis is also relevant to understanding causes of disability. For sex, it is well documented that women have both higher disability prevalence and incidence rates than men (22,23), even after controlling for age (24), and worse measured mobility function (25). Our results have shown that after adjusting for measured mobility function (the "physiologic" component of mobility disability), women and men report disability with the same thresholds. The sex difference in disability rate appears, therefore, to reflect physiologic differences. The lack of significant threshold differences by educational level similarly suggests that the higher prevalence of mobility disability in less educated groups (26–28) reflects real physical health differences (25).

Clearly, the mechanism for reporting disability across different age or income groups is somewhat different. With advancing age, self-reported disability and measured functional limitations both become more common. In our analysis, the younger old subjects were shown to be relatively less likely to report mobility disabilities than the older old subjects with similar measured physical health status. Possible explanations for this finding include differences in attitudes toward reporting or differences in environment. How precisely attitudes toward disability reporting are formed in different age groups and cohorts needs to be further explored.

Evidence from some studies has shown that older people with higher income level have both better self-reported (27,28) and measured (25) physical function. Differences in reporting of disability across income groups evident in our analysis may perhaps arise from real differences in the help and facilities available to older people with higher incomes, or it may reflect only differences in attitudes. Again, further research is needed.

It is interesting that White Americans are more likely than non-Whites to report inability. However, results from previous studies about racial differences in self-reported disability are inconsistent (29,30). The possible directions of racial differences are still unclear, especially if socioeconomic status differences are accounted for in analyses (31).

Cross-Study Comparison
Comparing studies of older people is seldom easy, as detailed differences in design and instruments abound. Although the measures used in both the LASA and the NHANES III studies are very similar, there are some differences that could make comparison across these two studies difficult to interpret. First, the comparable question on walking a quarter of a mile in LASA related to "walking for five minutes," which was designed to cover roughly the same medium-distance walk but was found to convey a clearer concept to Dutch respondents. Reported prevalence rates in the LASA study for mobility disability were far lower than those in the U.S. study, and these differences could be dismissed as uninterpretable because of the differences in the disability question asked. However, recent evidence from another cross-country study, when all questions were standardized, also suggests that Dutch people have a lower prevalence of self-reported disability than do people in other countries (32).

By using the MOBLI score in both studies, we can assess whether part of this difference in reported disability is due to differences in reporting, and we can also assess how much of the difference in mobility-related performance remains to be explained. Our analyses show that the thresholds for reporting difficulty and inability in the LASA study were indeed substantially different from those in the U.S. study, with Dutch respondents reporting disability only at more severe levels of tested performance. Nevertheless, differences in MOBLI score and in performance on the individual tests are also present.

Clearly, much more work has to be done to understand why differences in reporting of disability exist between some groups but not others in comparison with measured physical performance. Important differences in, for example, home and outside environment may play important roles and need further exploration. In any event, the analyses presented support the case for the use of relevant and validated test-based measures in epidemiologic studies of the causes of the physical health component of disability.

Conclusion
Traditional disability questions require older people to report difficulties with everyday activities including mobility, using broad and largely undefined categories. The core component of disability is physiologic. When it is measured by a valid continuous measure of mobility-related physical impairment or limitation and is compared with disability, there is evidence of differences in thresholds for reporting mobility disability across age and income groups in older Americans. Furthermore, comparisons between the NHANES III and LASA studies suggest that both reporting thresholds and measured impairment or limitation contribute to reported differences in medium-distance mobility disability. Further work is needed to understand the causes of attitude or environmental factors that may contribute to these reporting differences.

	Acknowledgments

 
This study was conducted as a part of the Longitudinal Aging Study Amsterdam (LASA) and was supported by funding from The Netherlands Ministry of Health, Welfare, and Sports to D. J. H. Deeg and grants from the U.K. NHS Research and Development Programme to D. Melzer.

The authors thank Prof. Gary King and Dr. Sophia Rabe-Hesketh for their statistical advice.

Received April 23, 2003

Accepted July 31, 2003

	References

Top Abstract Methods Results Discussion References

 

1. Fried LP, Guralnik JM. Disability in older adults: evidence regarding significance, etiology, and risk. J Am Geriatr Soc. 1997;45:92-100.[Medline]
2. 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. Soc Sci Med. 1999;48:445-469.
3. Murray CJL, Tandon A, Salomon J, Mathers CD, Sadana R. Cross-Population Comparability of Evidence for Health Policy. Global Programme on Evidence for Health Policy Series, No. 46. Geneva: World Health Organization; 2002.
4. Sen A. Health: perception versus observation. Br Med J. 2002;324:860-861.[Free Full Text]
5. Iburg K, Salomon J, Tandon A, Murray CJL. Cross-Population Comparability of Self-Reported and Physician-Assessed Mobility Levels: Evidence from the Third National Health and Nutrition Examination Survey. Global Programme on Evidence for Health Policy Series, No. 14. Geneva: World Health Organization; 2001.
6. Murray CJL, Tandon A, Salomon J, Mathers CD. Enhancing Cross-Population Comparability of Survey Results. Global Programme on Evidence for Health Policy Series, No. 35. Geneva: World Health Organization; 2001.
7. Lan T-Y, Melzer D, Tom BD, Guralnik JM. Performance tests and disability: developing an objective index of mobility-related limitation in older populations. J Gerontol Med Sci. 2002;57A:M294-M301.
8. Melzer D, Lan T-Y, Guralnik JM. The predictive validity for mortality of the index of mobility-related limitation. Result from the EPESE Study. Age Ageing. 2003;32:(6): 619-625.[Abstract/Free Full Text]
9. Lan T-Y, Deeg DJH, Guralnik JM, Melzer D. Responsiveness of the index of mobility limitation: comparison with gait speed alone in the Longitudinal Aging Study Amsterdam. J Gerontol A Biol Sci Med Sci. 2003;58:(8): 721-727.
10. Burt VL, Harris T. The third National Health and Nutrition Examination Survey: contributing data on aging and health. The Gerontologist. 1994;34:486-490.[Abstract]
11. Marwick C. NHANES III health data relevant for aging nation. JAMA. 1997;277:100-102.[Abstract/Free Full Text]
12. National Center for Health Statistics, U.S. Department of Health and Human Services. National Health and Nutrition Examination Survey III, 1988–1994. CD-ROM Series 11, Nos. 1A & 2A; 1997.
13. Deeg DJH, Westendorp-de Serière M. Autonomy and Well-Being in the Older Population I. Report from the Longitudinal Aging Study Amsterdam, 1992–1993. Amsterdam: VU University Press; 1994.
14. Broese van Groenou MI, van Tilburg TG, de Leeuw ED, Liefbroer AC. Data collection. In Knipscheer CPM, de Jong Gierveld J, van Tilburg TG, Dykstra PA, eds. Living Arrangements and Social Networks of Older Adults in the Netherlands. First Results. Amsterdam: VU University Press; 1995:185–197.
15. Aartsen MJ, Smits CH, van Tilburg T, Knipscheer KC, Deeg DJH. Activity in older adults: cause or consequence of cognitive functioning? A longitudinal study on everyday activities and cognitive performance in older adults. J Gerontol Psychol Sci. 2002;57B:P153-P162.
16. Epidemiology for Policy Group, Department of Public Health and Primary Care University of Cambridge. MOBLI Index. Available at www.phpc.cam.ac.uk/epg/mobli.htm 2002.
17. Smits CH, Deeg DJ, Jonker C. Cognitive and emotional predictors of disablement in older adults. J Aging Health. 1997;9:204-221.[Abstract/Free Full Text]
18. Rabe-Hesketh S, Pickles A, Skrondal A. GLLAMM Manual. Technical Report 2001/01. London: Department of Biostatistics and Computing, Institute of Psychiatry, King's College, University of London; 2001.
19. Rabe-Hesketh S, Skrondal A. Estimating Chopit Models in GLLAMM. London: Department of Biostatistics and Computing, Institute of Psychiatry, King's College, University of London; 2002.
20. Finney JD. Probit Analysis. Cambridge, U.K.: Cambridge University Press; 1971.
21. Aldrich JH, Nelson FD. Linear Probability, Logit, and Probit Models. Quantitative Applications in the Social Sciences; No. 45. Beverly Hills: Sage Publications; 1984.
22. Strawbridge WJ, Kaplan GA, Camacho T, Cohen RD. The dynamics of disability and functional change in an elderly cohort: results from the Alameda County Study. J Am Geriatr Soc. 1992;40:799-806.[Medline]
23. Leveille SG, Penninx BW, Melzer D, Izmirlian G, Guralnik JM. Sex differences in the prevalence of mobility disability in old age: the dynamics of incidence, recovery, and mortality. J Gerontol Soc Sci. 2000;55B:S41-S50.
24. Crimmins EM, Saito Y, Reynolds SL. Further evidence on recent trends in the prevalence and incidence of disability among older Americans from two sources: the LSOA and the NHIS. J Gerontol Soc Sci. 1997;52B:S59-S71.
25. Seeman TE, Charpentier PA, Berkman LF, et al. Predicting changes in physical performance in a high-functioning elderly cohort: MacArthur Studies of Successful Aging. J Gerontol Med Sci. 1994;49A:M97-M108.
26. Snowdon DA, Ostwald SK, Kane RL. Education, survival, and independence in elderly Catholic sisters, 1936–1988. Am J Epidemiol. 1989;130:999-1012.[Abstract/Free Full Text]
27. Boult C, Kane RL, Louis TA, Boult L, McCaffrey D. Chronic conditions that lead to functional limitation in the elderly. J Gerontol Med Sci. 1994;49A:M28-M36.
28. Mor V, Murphy J, Masterson-Allen S, et al. Risk of functional decline among well elders. J Clin Epidemiol. 1989;42:895-904.[Medline]
29. Mendes de Leon CF, Beckett LA, Fillenbaum GG, et al. Black–white differences in risk of becoming disabled and recovering from disability in old age: a longitudinal analysis of two EPESE populations. Am J Epidemiol. 1997;145:488-497.[Abstract/Free Full Text]
30. Mendes de Leon CF, Fillenbaum GG, Williams CS, Brock DB, Beckett LA, Berkman LF. Functional disability among elderly blacks and whites in two diverse areas: the New Haven and North Carolina EPESE. Established Populations for the Epidemiologic Studies of the Elderly. Am J Public Health. 1995;85:994-998.[Abstract/Free Full Text]
31. Guralnik JM, Leveille SG. Race, ethnicity, and health outcomes—unraveling the mediating role of socioeconomic status. Am J Public Health. 1997;87:728-730.[Free Full Text]
32. Pluijm S, Deeg DJ. Harmonizing a measure of ADL across six countries. Presented at the Symposium: European Longitudinal Studies of Health in Older Age, annual meeting of the Gerontological Society of America, Chicago, IL, 2002.

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