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Limits to deficit accumulation in elderly people
Mechanisms of Ageing and Development 127 (2006) 494–496 www.elsevier.com/locate/mechagedev
Short communication
Limits to deficit accumulation in elderly people Kenneth Rockwood *, Arnold Mitnitski Department of Medicine, Dalhousie University, Halifax, NS, Canada B3H 1C6 Received 5 December 2005; received in revised form 3 January 2006; accepted 9 January 2006 Available online 20 February 2006
Abstract We evaluated limits to the accumulation of deficits (symptoms, diseases, disabilities) for 33,069 people aged 65+ years. We combined deficits in a frailty index (theoretical range 0–1) and found that the 99% limit varied little between samples, representing a frailty index value of about 0.65 0.05. This near-maximum shows no relationship with age. It is the same in community and institutional samples, even though the mean value is much higher in the latter. The data suggest a level of frailty beyond which, even in developed countries, further deficit accumulation is not sustainable. # 2006 Elsevier Ireland Ltd. All rights reserved. Keywords: Ageing; Frailty (deficit accumulation) index; Maximum; Limits
1. Introduction As people age, they accumulate health problems, such as symptoms, diseases and disabilities that can be considered as deficits. Not everyone accumulates deficits at the same rate, nor do they accumulate the same deficits. Deficits can be summarized in a frailty index, which counts the proportion that is present in a given individual (Mitnitski et al., 2001; Goggins et al., 2005; Woo et al., 2005). For individuals, knowing exactly which deficits have been accumulated is important, but at a population level the frailty index shows that simply knowing how many deficits have been accumulated is key. For example, the distribution of the frailty index changes characteristically with age (Rockwood et al., 2004a,b; Mitnitski and Rockwood, 2006), and the pattern of deficit accumulation is readily summarized as a Poisson distribution (Mitnitski et al., 2006). We have also observed that the mean value of the frailty index consistently increases with age (3% per year) in community-dwelling people (Mitnitski et al., 2005). By contrast, we have observed no such relationship in institutional/clinical samples. The consistent age-related accumulation of deficits in community-dwelling but not institutional/clinical samples * Corresponding author at: Division of Geriatric Medicine, Dalhousie University, 1421-5955 Veterans Memorial Lane, Halifax, NS, Canada B3H 1C6. Tel.: +1 902 473 8687; fax: +1 902 473 1050. E-mail address: [email protected] (K. Rockwood). 0047-6374/$ – see front matter # 2006 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.mad.2006.01.002
appears to be related to differences in the groups’ mean frailty index values. People in institutions are frailer than those in the community, for all save the highest ages, and mortality is exponentially related to the frailty index score (Mitnitski et al., 2005). Together, these observations suggest a pragmatic maximum to the number of deficits that can be accumulated before death supervenes. To better understand limits to deficit accumulation, we investigated how the upper limit of the frailty index in community samples relates to the mean and highest frailty index values in institutional samples.
2. Methods We conducted these secondary analyses in studies of people aged 65+. Community-based databases were the Australian Longitudinal Study of Aging (ALSA (Jorm et al., 1997) www.immi.gov.au/research/Isia), the Canadian Study of Health and Aging screening (CSHA-screen), and community clinical examination (CSHA-exam) samples (CSHA, 1994; Graham et al., 1996), the Canadian National Population Health Survey (Swain et al., 1999) (accessed via grant NPHS-671-1193-24888), the United States National Health and Nutrition Examination Survey, NHANES (Idler and Angel, 1990) (http://www.cdc.gov/ nchs/nhanes.htm). We compared these community samples with the combined institutional samples from the first two phases of the CSHA (CSHA-inst). The frailty index in each sample was constructed from about 40 variables, using only cases with complete data. Although each sample included similar items (e.g. disabilities, vascular risk factors) the variables were not the same. Variables are coded so that ‘‘1’’ indicates that the deficit is present, and ‘‘0’’ that it is absent; similarly, multi-level variables are mapped into the 0–1 interval. Higher scores thus indicate greater frailty. A list of variables is available at http://myweb.dal.ca/amitnits/Variables.htm.
K. Rockwood, A. Mitnitski / Mechanisms of Ageing and Development 127 (2006) 494–496
495
Fig. 1. Accumulation of deficits at the 99% limit (Panel A) and 95% limit (Panel B) of the observed maximal values of the frailty index. In each panel, the lower points show the previously observed 3% average accumulation of deficits with age; their average is the fitted, straight solid line. In Panel A, the next highest line (solid, curved, joining the points) shows that in the institutional sample there is no relationship between deficit accumulation and age. The next line (straight, dashed) is the fitted mean of 99% of the maximal observed values for the community samples. It too, shows no relationship with age, and neither does the highest line (curved, grey) which joins the points of 99% of the maximal values at each age for the institutional sample. In Panel B, the lines represent these respective points for 95% of the maximal observed values. In contrast to the 99% lines, the 95% lines change with age, but their slope is less than the 3% average accumulation of deficits in the community samples. For a given individual, the value of the frailty index, using n variables, is defined as the fraction f = x/n, where x is the number of deficits recorded for that person. Linear regression was used to fit the relationships between the largest values of each index and age, and the difference between the slopes of these lines was assessed using confidence intervals. Data were analyzed using Matlab. In deciding what to count as the limit, we recognized that any absolute maximal value is an outlier. As it seemed likely that single outliers occur variably, we used a 99% estimate as being more stable. For comparison, we used 97.5, 95 and 92.5% estimates of the observed local maxima. (Note that these are not confidence limits, which assume a normal distribution.) We hypothesized that at near-maximal values, the frailty index should show little age-related increase, as these were near-lethal levels of frailty.
3. Results The mean, but not the maximal values, of the institutional sample are notably higher than in the community samples (Fig. 1). Moreover, at the 99% limit (Panel A) but not at the 95% limit (Panel B) the maximal values for community samples well overlap the maximal values for the combined institutional sample. Only the means and 95% limits for the community sample show a relationship with age. The slope of the line that relates deficit accumulation in each communitybased sample to age diminishes as the near-maximal value of the frailty index is approached. This varies a little between the samples, but occurs at 97.5% of the maximum observed values; i.e., at 97.5% of the observed limit – and higher – the accumulation of deficits is indistinguishable from zero. 4. Discussion We have demonstrated limits to the proportion of deficits that can be accumulated in elderly people. These limits are consistent in samples from four developed countries. The samples used different items in their deficit counts, suggesting that the finding is robust. In addition, their internal consistency – e.g. as the proportion of deficits approached the maximum values the slope of the relationship with age becomes statistically indistinguishable from zero, or that the near-
maximal values overlap between the institutional and community samples, even though the mean values are distinct – also suggests validity. Moreover, as demonstrated previously (Mitnitski et al., 2005) mortality is exponentially related to the value of the frailty index. In this context, we were interested to observe that although the maximum of deficit accumulation by definition is 1.0, the 99% limit is consistently less than 0.7 in the samples that we have studied. This estimate corresponds with what would be inferred from earlier work relating mortality to frailty index (Mitnitski et al., 2004, 2005). Our findings must be interpreted with caution. Although we report data on 33,069 people, they were all elderly, so that we cannot comment on the age at which this trend emerges. That there are limits to deficit accumulation in population studies offers the possibility of testing whether such limits are also observed in acute clinical settings, where active treatment might reverse some deficits. Whether reversing deficits changes prognosis, or whether, in samples of people who present with acute illness, there are also pragmatic limits to deficit accumulation are interesting questions that are motivating further inquiries by our group. Acknowledgements This analysis was funded by the Canadian Institutes of Health Research (CIHR) grant MOP 64-169. Kenneth Rockwood is supported by an Investigator Award from the CIHR and by the Dalhousie Medical Research Foundation as the Kathryn Allen Weldon Professor of Alzheimer Research. References Canadian Study of Health and Aging Working Group, 1994. The Canadian Study of Health and Aging: study methods and prevalence of dementia. CMAJ 150, 899–913. Goggins, W.B., Woo, J., Sham, A., Ho, S.C., 2005. Frailty index as a measure of biological age in a Chinese population. J Gerontol A Biol Sci Med Sci 60, 1046–1051.
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K. Rockwood, A. Mitnitski / Mechanisms of Ageing and Development 127 (2006) 494–496
Graham, J.E., Rockwood, K., Beattie, B.L., McDowell, I., Eastwood, R., Gauthier, S., 1996. Standardization of the diagnosis of dementia in the Canadian Study of Health and Aging. Neuroepidemiology 15, 246– 256. Idler, E.L., Angel, R.J., 1990. Self-rated health and mortality in the NHANES-I Epidemiologic Follow-up Study. Am J Public Health 80, 446–452. Jorm, A.F., Christensen, H., Korten, A.E., Henderson, A.S., Jacomb, P.A., Mackinnon, A., 1997. Do cognitive complaints either predict future cognitive decline or reflect past cognitive decline? A longitudinal study of an elderly community sample. Psychol Med 27, 91–98. Mitnitski, A.B., Bao, L., Rockwood, K., 2006. Going from bad to worse: a stochastic model of transitions in deficit accumulation, in relation to mortality. Mech. Ageing Dev. 127, 490–493. Mitnitski, A.B., Mogilner, A.J., Rockwood, K., 2001. Accumulation of deficits as a proxy measure of aging. ScientificWorldJournal 1, 323–336. Mitnitski, A., Rockwood, K., 2006. Decrease in the relative heterogeneity of health with age: a cross-national comparison. Mech Ageing Dev 127, 70–72.
Mitnitski, A.B., Song, X., Rockwood, K., 2004. The estimation of relative fitness and frailty in community dwelling older adults using self-report data. J Gerontol Med Sci 59, M627–M632. Mitnitski, A.B., Song, X., Skoog, I., Broe, A.G., Cox, J.L., Grunfeld, E., Rockwood, K., 2005. Relative fitness and frailty of elderly men and women in developed countries, in relation to mortality. J Am Geriatr Soc 53, 2184– 2189. Rockwood, K., Howlett, S.E., MacKnight, C., Beattie, B.L., Bergman, H., Hebert, R., Hogan, D.B., Wolfson, C., McDowell, I., 2004b. Prevalence, attributes, and outcomes of fitness and frailty in community-dwelling older adults: report from the Canadian study of health and aging. J Gerontol A Biol Sci Med Sci 59, 1310–1317. Rockwood, K., Mogilner, A.J., Mitnitski, A., 2004a. Changes with age in the distribution of a frailty index. Mech Ageing Dev 125, 517–519. Swain, L., Catlin, G., Beaudet, M.P., 1999. The National Population Health Survey: its longitudinal nature. Health Reports 10, 69–82. Woo, J., Goggins, W., Sham, A., Ho, S.C., 2005. Social determinants of frailty. Gerontology 51, 402–408.
Short communication
Limits to deficit accumulation in elderly people Kenneth Rockwood *, Arnold Mitnitski Department of Medicine, Dalhousie University, Halifax, NS, Canada B3H 1C6 Received 5 December 2005; received in revised form 3 January 2006; accepted 9 January 2006 Available online 20 February 2006
Abstract We evaluated limits to the accumulation of deficits (symptoms, diseases, disabilities) for 33,069 people aged 65+ years. We combined deficits in a frailty index (theoretical range 0–1) and found that the 99% limit varied little between samples, representing a frailty index value of about 0.65 0.05. This near-maximum shows no relationship with age. It is the same in community and institutional samples, even though the mean value is much higher in the latter. The data suggest a level of frailty beyond which, even in developed countries, further deficit accumulation is not sustainable. # 2006 Elsevier Ireland Ltd. All rights reserved. Keywords: Ageing; Frailty (deficit accumulation) index; Maximum; Limits
1. Introduction As people age, they accumulate health problems, such as symptoms, diseases and disabilities that can be considered as deficits. Not everyone accumulates deficits at the same rate, nor do they accumulate the same deficits. Deficits can be summarized in a frailty index, which counts the proportion that is present in a given individual (Mitnitski et al., 2001; Goggins et al., 2005; Woo et al., 2005). For individuals, knowing exactly which deficits have been accumulated is important, but at a population level the frailty index shows that simply knowing how many deficits have been accumulated is key. For example, the distribution of the frailty index changes characteristically with age (Rockwood et al., 2004a,b; Mitnitski and Rockwood, 2006), and the pattern of deficit accumulation is readily summarized as a Poisson distribution (Mitnitski et al., 2006). We have also observed that the mean value of the frailty index consistently increases with age (3% per year) in community-dwelling people (Mitnitski et al., 2005). By contrast, we have observed no such relationship in institutional/clinical samples. The consistent age-related accumulation of deficits in community-dwelling but not institutional/clinical samples * Corresponding author at: Division of Geriatric Medicine, Dalhousie University, 1421-5955 Veterans Memorial Lane, Halifax, NS, Canada B3H 1C6. Tel.: +1 902 473 8687; fax: +1 902 473 1050. E-mail address: [email protected] (K. Rockwood). 0047-6374/$ – see front matter # 2006 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.mad.2006.01.002
appears to be related to differences in the groups’ mean frailty index values. People in institutions are frailer than those in the community, for all save the highest ages, and mortality is exponentially related to the frailty index score (Mitnitski et al., 2005). Together, these observations suggest a pragmatic maximum to the number of deficits that can be accumulated before death supervenes. To better understand limits to deficit accumulation, we investigated how the upper limit of the frailty index in community samples relates to the mean and highest frailty index values in institutional samples.
2. Methods We conducted these secondary analyses in studies of people aged 65+. Community-based databases were the Australian Longitudinal Study of Aging (ALSA (Jorm et al., 1997) www.immi.gov.au/research/Isia), the Canadian Study of Health and Aging screening (CSHA-screen), and community clinical examination (CSHA-exam) samples (CSHA, 1994; Graham et al., 1996), the Canadian National Population Health Survey (Swain et al., 1999) (accessed via grant NPHS-671-1193-24888), the United States National Health and Nutrition Examination Survey, NHANES (Idler and Angel, 1990) (http://www.cdc.gov/ nchs/nhanes.htm). We compared these community samples with the combined institutional samples from the first two phases of the CSHA (CSHA-inst). The frailty index in each sample was constructed from about 40 variables, using only cases with complete data. Although each sample included similar items (e.g. disabilities, vascular risk factors) the variables were not the same. Variables are coded so that ‘‘1’’ indicates that the deficit is present, and ‘‘0’’ that it is absent; similarly, multi-level variables are mapped into the 0–1 interval. Higher scores thus indicate greater frailty. A list of variables is available at http://myweb.dal.ca/amitnits/Variables.htm.
K. Rockwood, A. Mitnitski / Mechanisms of Ageing and Development 127 (2006) 494–496
495
Fig. 1. Accumulation of deficits at the 99% limit (Panel A) and 95% limit (Panel B) of the observed maximal values of the frailty index. In each panel, the lower points show the previously observed 3% average accumulation of deficits with age; their average is the fitted, straight solid line. In Panel A, the next highest line (solid, curved, joining the points) shows that in the institutional sample there is no relationship between deficit accumulation and age. The next line (straight, dashed) is the fitted mean of 99% of the maximal observed values for the community samples. It too, shows no relationship with age, and neither does the highest line (curved, grey) which joins the points of 99% of the maximal values at each age for the institutional sample. In Panel B, the lines represent these respective points for 95% of the maximal observed values. In contrast to the 99% lines, the 95% lines change with age, but their slope is less than the 3% average accumulation of deficits in the community samples. For a given individual, the value of the frailty index, using n variables, is defined as the fraction f = x/n, where x is the number of deficits recorded for that person. Linear regression was used to fit the relationships between the largest values of each index and age, and the difference between the slopes of these lines was assessed using confidence intervals. Data were analyzed using Matlab. In deciding what to count as the limit, we recognized that any absolute maximal value is an outlier. As it seemed likely that single outliers occur variably, we used a 99% estimate as being more stable. For comparison, we used 97.5, 95 and 92.5% estimates of the observed local maxima. (Note that these are not confidence limits, which assume a normal distribution.) We hypothesized that at near-maximal values, the frailty index should show little age-related increase, as these were near-lethal levels of frailty.
3. Results The mean, but not the maximal values, of the institutional sample are notably higher than in the community samples (Fig. 1). Moreover, at the 99% limit (Panel A) but not at the 95% limit (Panel B) the maximal values for community samples well overlap the maximal values for the combined institutional sample. Only the means and 95% limits for the community sample show a relationship with age. The slope of the line that relates deficit accumulation in each communitybased sample to age diminishes as the near-maximal value of the frailty index is approached. This varies a little between the samples, but occurs at 97.5% of the maximum observed values; i.e., at 97.5% of the observed limit – and higher – the accumulation of deficits is indistinguishable from zero. 4. Discussion We have demonstrated limits to the proportion of deficits that can be accumulated in elderly people. These limits are consistent in samples from four developed countries. The samples used different items in their deficit counts, suggesting that the finding is robust. In addition, their internal consistency – e.g. as the proportion of deficits approached the maximum values the slope of the relationship with age becomes statistically indistinguishable from zero, or that the near-
maximal values overlap between the institutional and community samples, even though the mean values are distinct – also suggests validity. Moreover, as demonstrated previously (Mitnitski et al., 2005) mortality is exponentially related to the value of the frailty index. In this context, we were interested to observe that although the maximum of deficit accumulation by definition is 1.0, the 99% limit is consistently less than 0.7 in the samples that we have studied. This estimate corresponds with what would be inferred from earlier work relating mortality to frailty index (Mitnitski et al., 2004, 2005). Our findings must be interpreted with caution. Although we report data on 33,069 people, they were all elderly, so that we cannot comment on the age at which this trend emerges. That there are limits to deficit accumulation in population studies offers the possibility of testing whether such limits are also observed in acute clinical settings, where active treatment might reverse some deficits. Whether reversing deficits changes prognosis, or whether, in samples of people who present with acute illness, there are also pragmatic limits to deficit accumulation are interesting questions that are motivating further inquiries by our group. Acknowledgements This analysis was funded by the Canadian Institutes of Health Research (CIHR) grant MOP 64-169. Kenneth Rockwood is supported by an Investigator Award from the CIHR and by the Dalhousie Medical Research Foundation as the Kathryn Allen Weldon Professor of Alzheimer Research. References Canadian Study of Health and Aging Working Group, 1994. The Canadian Study of Health and Aging: study methods and prevalence of dementia. CMAJ 150, 899–913. Goggins, W.B., Woo, J., Sham, A., Ho, S.C., 2005. Frailty index as a measure of biological age in a Chinese population. J Gerontol A Biol Sci Med Sci 60, 1046–1051.
496
K. Rockwood, A. Mitnitski / Mechanisms of Ageing and Development 127 (2006) 494–496
Graham, J.E., Rockwood, K., Beattie, B.L., McDowell, I., Eastwood, R., Gauthier, S., 1996. Standardization of the diagnosis of dementia in the Canadian Study of Health and Aging. Neuroepidemiology 15, 246– 256. Idler, E.L., Angel, R.J., 1990. Self-rated health and mortality in the NHANES-I Epidemiologic Follow-up Study. Am J Public Health 80, 446–452. Jorm, A.F., Christensen, H., Korten, A.E., Henderson, A.S., Jacomb, P.A., Mackinnon, A., 1997. Do cognitive complaints either predict future cognitive decline or reflect past cognitive decline? A longitudinal study of an elderly community sample. Psychol Med 27, 91–98. Mitnitski, A.B., Bao, L., Rockwood, K., 2006. Going from bad to worse: a stochastic model of transitions in deficit accumulation, in relation to mortality. Mech. Ageing Dev. 127, 490–493. Mitnitski, A.B., Mogilner, A.J., Rockwood, K., 2001. Accumulation of deficits as a proxy measure of aging. ScientificWorldJournal 1, 323–336. Mitnitski, A., Rockwood, K., 2006. Decrease in the relative heterogeneity of health with age: a cross-national comparison. Mech Ageing Dev 127, 70–72.
Mitnitski, A.B., Song, X., Rockwood, K., 2004. The estimation of relative fitness and frailty in community dwelling older adults using self-report data. J Gerontol Med Sci 59, M627–M632. Mitnitski, A.B., Song, X., Skoog, I., Broe, A.G., Cox, J.L., Grunfeld, E., Rockwood, K., 2005. Relative fitness and frailty of elderly men and women in developed countries, in relation to mortality. J Am Geriatr Soc 53, 2184– 2189. Rockwood, K., Howlett, S.E., MacKnight, C., Beattie, B.L., Bergman, H., Hebert, R., Hogan, D.B., Wolfson, C., McDowell, I., 2004b. Prevalence, attributes, and outcomes of fitness and frailty in community-dwelling older adults: report from the Canadian study of health and aging. J Gerontol A Biol Sci Med Sci 59, 1310–1317. Rockwood, K., Mogilner, A.J., Mitnitski, A., 2004a. Changes with age in the distribution of a frailty index. Mech Ageing Dev 125, 517–519. Swain, L., Catlin, G., Beaudet, M.P., 1999. The National Population Health Survey: its longitudinal nature. Health Reports 10, 69–82. Woo, J., Goggins, W., Sham, A., Ho, S.C., 2005. Social determinants of frailty. Gerontology 51, 402–408.