ArticlePDF AvailableLiterature Review

Abstract and Figures

This review article summarizes how frailty can be considered in relation to deficit accumulation. Recalling that frailty is an age-associated, nonspecific vulnerability, we consider symptoms, signs, diseases, and disabilities as deficits, which are combined in a frailty index. An individual's frailty index score reflects the proportion of potential deficits present in that person, and indicates the likelihood that frailty is present. Although based on a simple count, the frailty index shows several interesting properties, including a characteristic rate of accumulation, a submaximal limit, and characteristic changes with age in its distribution. The frailty index, as a state variable, is able to quantitatively summarize vulnerability. Future studies include the application of network analyses and stochastic analytical techniques to the evaluation of the frailty index and the description of other state variables in relation to frailty.
Content may be subject to copyright.
Frailty in relation to the accumulation of deficits
Kenneth Rockwood
1,2
, Arnold Mitnitski
2
As accepted
The Journal of Gerontology – Medical Sciences
September 1, 2006
1
Division of Geriatric Medicine,
2
Department of Medicine, Dalhousie University,
Halifax, Nova Scotia, Canada
Address for correspondence: Kenneth Rockwood, Centre for Health Care of the Elderly,
1421-5955 Veterans’ Memorial Lane, Halifax, Nova Scotia, Canada, B3H 2E1.
Telephone: 902-473-8687. Fax: 902-473-1050. Kenneth.Rockwood@Dal.ca
Word count:
Total word count: 4309
Text word count: 2175
Running Head: Frailty and deficit accumulation
1
Abstract
This paper summarizes how frailty can be considered in relation to deficit accumulation.
Recalling that frailty is an age-associated, non-specific vulnerability, we consider
symptoms, signs, diseases and disabilities as deficits, which are combined in a frailty
index. An individual’s frailty index score reflects the proportion of potential deficits
present in that person, and indicates the likelihood that frailty is present. Although based
on a simple count, the frailty index shows several interesting properties, including a
characteristic rate of accumulation, a sub-maximal limit and characteristic changes with
age in its distribution. The frailty index, as a state variable, is able to quantitatively
summarize vulnerability. Future studies include the application of network analyses and
stochastic analytical techniques to the evaluation of the frailty index, and the description
of other state variables in relation to frailty.
Key words: frailty, index variables, stochastic process, ageing
2
Frailty is a non-specific state of increasing risk, which reflects multi-system
physiological change. It is highly age-associated. The physiological changes that underlie
frailty do not always achieve disease status, so that some people, usually very elderly, are
frail without having life-threatening illness. These statements about frailty are relatively
non-controversial. More controversial is how to operationalize frailty in clinical practice
and for research (1-6). We and others have done so by considering frailty in relation to
the accumulation of deficits (7-12).
Here, we review how studying deficit accumulation
can help elucidate frailty, its relation to aging, and its mechanisms. We focus on
mathematical and clinical aspects.
Background
The frailty index score is calculated as the proportion of potential deficits that are
present in a given individual, as elaborated below. The frailty index recognizes that
frailty is multi-factorial and dynamic (13, 14). We first tried to define frailty by
combining integrated items – and traditional foci of gerontologists – such as cognition,
mobility, continence and function (14). While this gave good construct (15)
and
predictive validity (14,16)
it left much variance unexplained, and did not consider relative
fitness. We aimed for a measure that could evaluate impairments in many systems,
accommodate change, was graded and was conceptually simple. By combining items in
a single index , we can consider frailty in absolute and relative terms, according to this
probabilistic consideration: the more things individuals have wrong with them, the higher
the likelihood that they will be frail. We now consider each part of that statement.
What should be counted as “things that individuals have wrong with them”? We
consider symptoms, signs, disabilities, diseases and laboratory measurements, which we
term deficits. The frailty index uses a range of deficits that are readily available in survey
or clinical data. [Examples are available: http://myweb.dal.ca/amitnits/STable.htm] A
standard Comprehensive Geriatric Assessment (CGA),(17) for example, records about 40
items, some of which are self-reported (e.g. ‘how would you rate your health’ ) others
ascertained by tests (e.g. Mini-Mental State Examination (18)) and still others by clinical
evaluation (e.g. congestive heart failure) or laboratory measurement (diabetes mellitus).
These can be combined by simply adding them – for example, a ‘1’ for each deficit that is
present, a ‘0’ when they are absent, and a fraction when they are present to a limited
extent (e.g. health as ‘good’=0, ‘fair’=0.5, ‘poor’=1) (19,,20). Obviously, there are many
ways to count, say, 10 deficits from a total of 40, but as illustrated below, the resulting
index score (10/40 = 0.25) has many characteristic features, even if the composition is not
the same between individuals. While it is understandable to be concerned about the
specific nature of the variables that might be included in the frailty index, our experience
suggests that, when some sufficiently large number (roughly, about 40) variables are
considered, the variables can be selected at random, and still yield comparable results of
the risks of adverse outcomes (21).
What we mean by “the higher the likelihood that they will be frail” is a greater
risk of adverse outcomes (e.g. death, institutionalization, health services use, further
deficit accumulation). Still, we note that frailty is neither necessary for death (even very
fit people can die unexpectedly, as in an accident) nor is it sufficient (even at the highest
level of the frailty index, the median survival time is more than one year). Moreover,
3
while death is individual, the mortality rate is a group statistic, so our inquiries are
necessarily probabilistic. Further, we are not concerned about mortality prediction per se
– instead, mortality prediction has served as a means of validating the concept. Were our
focus mortality prediction, we would have given heavy weight to chronological age, or
diseases of known lethality, such as late-life cancer (22). Rather, we view frailty such
that chronological age can be understood as a contextual factor – for example, as
providing an expected value for deficit accumulation. Our model suggests that the effect
of chronological age on adverse outcomes can be negligible when deficits are taken into
account (20, 21, 23, 24).
Still, apparent intuitiveness would be no advantage if it gave unintelligible or
trivial results. But the self-evident statement that people with more things wrong are
more likely to suffer an adverse is quantifiable with the frailty index, and manipulating
the resulting data gives rise to insights (including hints of mechanisms) that are not self-
evident. Before considering these mathematical aspects, we first summarize some
essential features. On average, deficits accrue at a characteristic rate. In elderly people
from four developed countries, the mean rate of deficit accumulation across ages was
close to 0.03 (observed range 0.02-0.04) per year on a log scale (Figure 1) (24).
65 70 75 80 85 90 95
ALSA (pb)
CSHA-comm(pb)
CSHA-clin(pb)
NHANES (pb)
NPHS (pb)
SOPS (pb)
Breast cancer
CSHA-inst
MyocInfarct
US-LTHS
H70-75 (pb)
0.1
0.2
0.3
0.5
1.0
0.05
65 70 75 80 85 90 95
ALSA (pb)
CSHA-comm(pb)
CSHA-clin(pb)
NHANES (pb)
NPHS (pb)
SOPS (pb)
Breast cancer
CSHA-inst
MyocInfarct
US-LTHS
H70-75 (pb)
0.1
0.2
0.3
0.5
1.0
0.05
65 70 75 80 85 90 95
ALSA (pb)
CSHA-comm(pb)
CSHA-clin(pb)
NHANES (pb)
NPHS (pb)
SOPS (pb)
Breast cancer
CSHA-inst
MyocInfarct
US-LTHS
H70-75 (pb)
Frailty Index
0.1
0.2
0.3
0.5
1.0
0.05
Age (years)
65 70 75 80 85 90 95
ALSA (pb)
CSHA-comm(pb)
CSHA-clin(pb)
NHANES (pb)
NPHS (pb)
SOPS (pb)
Breast cancer
CSHA-inst
MyocInfarct
US-LTHS
H70-75 (pb)
0.1
0.2
0.3
0.5
1.0
0.05
65 70 75 80 85 90 95
ALSA (pb)
CSHA-comm(pb)
CSHA-clin(pb)
NHANES (pb)
NPHS (pb)
SOPS (pb)
Breast cancer
CSHA-inst
MyocInfarct
US-LTHS
H70-75 (pb)
0.1
0.2
0.3
0.5
1.0
0.05
65 70 75 80 85 90 95
ALSA (pb)
CSHA-comm(pb)
CSHA-clin(pb)
NHANES (pb)
NPHS (pb)
SOPS (pb)
Breast cancer
CSHA-inst
MyocInfarct
US-LTHS
H70-75 (pb)
0.1
0.2
0.3
0.5
1.0
0.05
65 70 75 80 85 90 95
ALSA (pb)
CSHA-comm(pb)
CSHA-clin(pb)
NHANES (pb)
NPHS (pb)
SOPS (pb)
Breast cancer
CSHA-inst
MyocInfarct
US-LTHS
H70-75 (pb)
Frailty Index
0.1
0.2
0.3
0.5
1.0
0.05
Age (years)
65 70 75 80 85 90 95
ALSA (pb)
CSHA-comm(pb)
CSHA-clin(pb)
NHANES (pb)
NPHS (pb)
SOPS (pb)
Breast cancer
CSHA-inst
MyocInfarct
US-LTHS
H70-75 (pb)
0.1
0.2
0.3
0.5
1.0
0.05
0.1
0.2
0.3
0.5
1.0
0.05
0.1
0.2
0.3
0.5
1.0
0.05
Figure 1. (From Mitnitski et al., 2005 J Am Geriatr Soc) Relationship between the frailty index and
chronological age for 7 population-based,(pb) community-dwelling samples,(n=33,581) denoted as (pb)
(ALSA (pb) -Australian Longitudinal Study of Aging; CSHA-screen (pb) Canadian Study of Health and
Aging screening sample; CSHA-exam (pb) clinical examination sample; H-70 (pb), Gothenburg study,
Sweden; NPHS – National Population Health Survey (pb) Canada; NHANES (pb) National Health and
Nutrition Examination Survey, United States; SOPS (pb) Sydney Old Persons Study, Australia), and; 2,573
people from 2 institutional (CSHA-inst CSHA wave 1 institutionalized sample; US-LYHS-inst National
Long Term Care Survey, USA) and 2 clinical studies (Breast cancer -cohort of metastatic breast cancer
survivors, Canada; MyocInfarct -Improving Cardiovascular Outcomes of Nova Scotians (ICONS),
Canada). The lines show the regression of mean frailty index with age. For community-dwelling people,
4
the line parameters are: slope=0.029 (95% confidence interval=0.0267, 0.0301) and intercept = -4.012 (-
3.872, -4.142).
Note that the samples differed not just by country, but were collected up to 20
years apart and employed different variables (e.g. self-report, clinically assessed,
laboratory measures). Moreover, the frailty indices used different numbers of variables
(from <30-70). The only restrictions on variables we used were that they reflected
deficits (cf. attributes - e.g. such as eye colour) accumulated across ages, and had < 5%
missing values. While a recent Chinese estimate put the rate of deficit lower (at about
1.4%) (8) this appears to reflect a survivor effect after age 87. By contrast, the frailty
index has otherwise correlated very highly (typically >0.96) with age (12,24,25). In
addition, women accumulate more deficits than men, even though, for any given level of
deficits, men have the higher mortality rates
(8-12, 24). In contrast to community
dwelling people, in the institutional and clinical cohorts, the frailty index was high at all
ages, and thus showed no relationship to age, consistent with high levels of frailty in
those settings (24).
Mathematical explorations in relation to mechanisms
The frailty index approach has a certain similarity with other quantitative
approaches. Vaupel et al., proposed that a largely undefined ‘frailty’ could account for
heterogeneity in health status to explain mortality outcomes (26). The origin of this frailty
was hypothesized to come from genetic differences and this was addressed in a
mathematical model incorporating pleiotropy of several genes (27).
Our approach of quantifying deficit accumulation yields some understanding of
the vulnerability of both individuals and groups. For example the distribution of the
index shows both characteristic changes with age (28) and a limit that does not depend on
age (29). Here, however, we focus on two findings that hint at biological mechanisms:
changes in the heterogeneity of health with age, and transitions between health states.
The average rate of deficit accumulation increases monotonically with age, as does the
standard deviation, underlying the generally accepted contention that, with age, health
status becomes more variable. Importantly, however only absolute heterogeneity in
health status (e.g. as measured by the variance) increases with age. Relative
heterogeneity decreases with age, as illustrated by the coefficient of variation, which is
the ratio of the standard deviation to the mean. We have found that the coefficient of
variation of the frailty index consistently decreases with age (Figure 2) (28, 30).
The decrease with age in the coefficient of variation has theoretical implications.
Ashby’s theory of 'requisite variety' (31,32) suggests that if the number of insults faced
by an organism overwhelms the number of responses that it can mount, the system will
fail. Therefore it is reasonable to expect that, as systems age, they lose variety in their
response repertoires, here captured, at the group level, by the coefficient of variation.
Further, a simple stochastic process of deficit accumulation yields a power-law
relationship between the mean frailty index m and its coefficient of variation, v ~ m
-1/2
(30). The same exponent ½ has also been found in the relationship between the average
flux in complex networks, and its fluctuations as measured by its standard deviation (33).
Consistent with the exponent representing influences external to the network, we interpret
this to mean that large environmental effects - for example cohort effects - become less
important closer to the end of life, where more proximate effects dominate. In
5
6
consequence, at extreme old age, people become more susceptible to smaller
perturbations. That the coefficient of variation itself can be summarized in network
analyses is of considerable interest, and is motivating further inquiries by our group.
Figure 2. (From Rockwood et al., 2004 Mech Ageing Dev) Changes with age in the frailty index. Panel A.
Change in the shape of the distribution, both sexes. Panel B. Change in the coefficient of variation
separately in men (triangles) and women (circles).
Aging involves many interacting processes, in which stochastic components play a key
role – even in genetically identical twins raised in a constant environment (34). With
aging, damage accumulates in cells and tissues, whether by random (35) or genetic (36)
mechanisms, involving sub-cellular and organ-specific pathways (37). Each results in
declines in functional capacity (38-39) and redundancy exhaustion (40). Our modeling
(23) reveals stochastic mechanisms and opens the prospect of using powerful analytical
techniques derived from the theory of stochastic processes (41). A modified Poisson
model with two nontrivial parameters gives a unified description of transitions to worse
health states, health improvements and mortality. The probability of transitions between n
(at baseline) and k deficits can be expressed as following:
),1)(exp(
!
ndn
k
n
nk
P
k
P =
ρ
ρ
where P
nd
is the probability to die during time between two consecutive assessment,
ρ
n
=
ρ
0
+b
1
n and P
nd
= P
0d
exp(b
2
n),
ρ
0
and P
0d
are the baseline characteristics. The two
parameters b
1
and b
2
describe respectively, given the current number of deficits, the
increments of their expected change, and in the risk of death (23). The very high model
fit (R=0.99) (Figure 3) with so few parameters has encouraged additional inquiries. The
simple stochastic multistage model shows not only deficits accumulation but also its flip
side. It shows that improvement is also possible and not so rare (roughly third of the
sample showed some degree of improvement in 5 years). Still the likelihood of death
increases exponentially with the number of deficits and therefore, in the long run, the
negatives outweigh the positives.
20 30 40 50 60 70 80 90
0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
1.4
1.5
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
76-80
80-85
71-75
61-65
-60
46-50
56
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
76-80
-
71-75
61-65
-60
46-50
A
B
56
70
0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
1.4
1.5
Frailty Index
tionDensity Distribu
0
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
76-80
-
71-75
61-65
-60
46-50
56
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
76-80
-
-
-
-
-
Age (years)
Coefficient of variation
A
B
20 30 40 50 60 70 80 90
0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
1.4
1.5
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
76-80
80-85
71-75
61-65
-60
46-50
56
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
76-80
-
71-75
61-65
-60
46-50
A
B
56
70
0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
1.4
1.5
Frailty Index
tionDensity Distribu
0
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
76-80
-
71-75
61-65
-60
46-50
56
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
76-80
-
-
-
-
-
Age (years)
Coefficient of variation
A
B
Number of deficits, k
The transition probabilities
0 5 10
0
0.1
0.2
0 5 10
0
0.1
0.2
0 5 10
0
0.1
0.2
0 5 10
0
0.1
0.2
0 5 10
0
0.1
0.2
0.3
0 5 10
0
0.1
0.2
0.3
0 5 10
0
0.1
0.2
0.3
0 5 10
0
0.1
0.2
0.3
0 5 10
0
0.1
0.2
0.3
0 5 10
0
0.1
0.2
0.3
0 5 10
0
0.1
0.2
0.3
0 5 10 15
0.2
0.4
0.6
0.8
n=0 n=1 n=2 n=3
n=4 n=5 n=6 n=7
n=8 n=9 n=10 mortality
Number of deficits, k
The transition probabilities
0 5 10
0
0.1
0.2
0 5 10
0
0.1
0.2
0 5 10
0
0.1
0.2
0 5 10
0
0.1
0.2
0 5 10
0
0.1
0.2
0.3
0 5 10
0
0.1
0.2
0.3
0 5 10
0
0.1
0.2
0.3
0 5 10
0
0.1
0.2
0.3
0 5 10
0
0.1
0.2
0.3
0 5 10
0
0.1
0.2
0.3
0 5 10
0
0.1
0.2
0.3
0 5 10 15
0.2
0.4
0.6
0.8
n=0 n=1 n=2 n=3
n=4 n=5 n=6 n=7
n=8 n=9 n=10 mortality
Figure 3. (From Mitnitski et al., 2006, Mach Ageing Dev) The probability of transition from n to k deficits,
and to death (right lower corner) in relation to the starting n deficits. Circles represent observational data of
transitions from CSHA-1 to CSHA-2 (filled circles), and from CSHA-2 to CSHA-3 (empty circles).
Estimates are presented for the model that combines transitions from CSHA-1 to CSHA-2, and from
CSHA-2 to CSHA-3.
Clinical utility of frailty index measures
Few clinicians would doubt that the more things that people have wrong with
them, the frailer they will be, but few too would embrace a 70-item scale. For now, we
have itemized the elements of a standard CGA to produce an ‘FI-CGA’ (20,21). The FI-
CGA has the usual properties of other versions – i.e. it is highly correlated with age,
shows a gamma distribution, is higher in women, and correlates with several adverse
outcomes, including institutionalization and health care use (Figure 4). If further coross-
validated, an FI-CGA could aid clinical decision-making by indicating the degree of
frailty, and thus the likelihood of an adverse outcome.
The FI-CGA, like other versions of the frailty index (including the widely used 5-
item phenotype definition (42)) largely weights items equally. It might seem obvious to
apply differential weights to the variables, so that cancer, for example, would be
weighted more heavily than skin disease. While in individual samples the performance
of the index (e.g. in predicting death) can be improved by weighting (43), in general,
weighting limits generalizability. For now, generalizability appears to have the greatest
value, and therefore we have pursued studies without weighting. Still, studies that might
aid clinical decision-making (for example, by demonstrating how closely an individual
7
has approached the theoretical limit of frailty) will require scrupulous attention to
whether the price paid in precision is too high for the rewards in generalizability.
Alternately, other groups might cross-validate an un-weighted frailty index, but use
weighting for local use. Whether there are demonstrable levels or severity classes also
need careful investigation (16,44).
0 10 20 30 40 50 60 70
-
0 10 20 30 40 50 60 70
Probability of institutionalization
CFS=1-3
CFS=4
CFS=5
CFS=6-7
0 10 20 30 40 50 60 70
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
-
0 10 20 30 40 50 60 70
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Probability of survival
CFS=1-3
CFS=4
CFS=5
CFS=6-7
Time (month)
AB
0 10 20 30 40 50 60 70
-
0 10 20 30 40 50 60 70
Probability of institutionalization
CFS=1-3
CFS=4
CFS=5
CFS=6-7
0 10 20 30 40 50 60 70
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
-
0 10 20 30 40 50 60 70
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Probability of survival
CFS=1-3
CFS=4
CFS=5
CFS=6-7
Time (month)
AB
Figure 4. (From Rockwood et al., 2005, CMAJ) Panel A. Kaplan-Meier medium-term survival curves
(adjusted for age and sex) for subjects with different values of the CSHA Clinical Frailty Scale (CFS). The
number of people at the start of each group: n=952 for CFS=1-3; n=349 for CFS=4; n=305 for CFS=5;
n=691 for CFS=6-7. Panel B. Kaplan-Meier medium-term institutionalization curves for subjects (adjusted
for age and sex) with different values of the Clinical Global Frailty Scale. The number of people at the start
of each group: n=828 for CFS=1-3; n=256 for CFS=4; n=136 for CFS=5; n=66 for CFS=6-7.
Future directions
In addition to studies that further explore the frailty index’s mathematical
properties, evaluate the limit to frailty, locally cross-validate weighted and un-weighted
clinical versions, and investigate grades, we see other uses of the frailty index approach.
We are keen that insights on frailty can translate into pragmatic techniques for
geriatricians (45). Clearly, the frailty index does not define a syndrome, which is a
collection of specific symptoms and signs. Instead, the frailty index can be considered as
a state variable, in that it characterize the whole health of individuals and validly
classifies risk across a wide range of people (7,46). This does not contradict the idea of a
syndrome; indeed it should be the case that people classified as frail syndromically will
have higher frailty index values than those who do not.
If the frailty index can be considered as state variable, perhaps there are others. In
our view, attention and concentration, function and mobility and balance each seem to be
logical candidates, as they are evolutionarily high order, and integrate many pathways.
Mobility and balance especially seems to have merit in the acute care setting, where they
fluctuate with changes in an individual’s overall state of health, can readily be tracked,
have plain language descriptors, and are susceptible to quantification (47). Perhaps the
8
most ambitious application of the frailty index is as a means of summarizing the
commonly invoked concept – but less commonly quantified – notion of “biological age”
(7,8,12,48-50,). Such studies might best be situated within the idea of biomarkers, and
could thereby benefit from the considerable experience of those inquiries (51-53). For
now, the evaluation of deficit accumulation index points out how we can embrace the
complexity of frailty.
9
Conflict of interest
We declare no conflict of interest.
Acknowledgments
Kenneth Rockwood receives career support from the Canadian Institutes of Health
Research (CIHR) through an Investigator Award, and from the Dalhousie Medical
Research Foundation as the Kathryn Allen Weldon Professor of Alzheimer Research.
Some of the analyses included in this review were conducted with CIHR support through
grants MOP-62823 (PI:KR) and MOP-64169 (PI:AM). The authors assert no proprietary
interest in this work.
10
References
1. Hogan DB, MacKnight C, Bergman H; Steering Committee, Canadian Initiative on
Frailty and Aging. Models, definitions, and criteria of frailty. Aging Clin Exp Res. 2003;
15 (3 Suppl): 1-29.
2. Ferrucci L, Guralnik JM, Studenski S, Fried LP, Cutler GB Jr, Walston JD;
Interventions on Frailty Working Group. Designing randomized, controlled trials aimed
at preventing or delaying functional decline and disability in frail, older persons: a
consensus report. J Am Geriatr Soc. 2004; 52: 625-634.
3. Fried L, Ferrucci L, Darer J, Williamson J, Anderson G. Untangling the concepts of
disability, frailty, and comorbidity: implications for improved targeting and care. J
Gerontol Med Sci. 2004: 59: 255-263.
4. Fisher AL. Just what defines frailty? J Am Geriatr Soc. 2005; 53: 2229-22230.
5. Rockwood K. What would make a definition of frailty successful? Age Ageing. 2005;
34: 432-434.
6. Rockwood K. Frailty and its definition: a worthy challenge. J Am Geriatr Soc. 2005;
53: 1069-1070.
7. Mitnitski A, Mogilner A, Rockwood K. Accumulation of deficits as a proxy measure of
aging. The Scientific World. 2001; 1: 323-336.
8. Goggins WB, Woo J, Sham A, Ho SC.. Frailty index as a measure of personal
biological age in a Chinese population. J Gerontol A Biol Sci Med Sc 2005; 60: 1046-
1051.
9. Woo J, Goggins W, Sham A, Ho SC. Social determinants of frailty.
Gerontology.
2005; 51: 402-408.
10. Woo J Goggins W, Sham A, Ho SC. Public health significance of the frailty index.
Disabil Rehabil. 2006; 28: 515-521.
11. Kulminski A, Yashin A, Akushevich I, Ukraintseva S, Land K, Arbeev K, Manton K.
Frailty index as a major indicator of aging processes and mortality in elderly: results from
analyses of the national long term care survey data. arXiv.org > q-bio >2005;
http://xxx.lanl.gov/abs/q-bio.PE/0509038
12. Kulminski A, Yashin A, Ukraintseva S, Akushevich I, Land K, Arbeev K, Manton K.
Age-associated disorders as a proxy measure of biological age: findings from the NLTCS
Data. arXiv.org > q-bio >2005; http://xxx.lanl.gov/abs/q-bio.PE/0509034
11
13. Rockwood K, Fox RA, Stolee P, Robertson D, Beattie. Frailty in elderly people: An
evolving concept. CMAJ. 1994; 150: 489-495.
14. Rockwood K, Stolee P, McDowell I. Factors associated with institutionalization of
older people in Canada: testing a multifactorial definition of frailty. J Am Geriatr Soc.
1996; 44: 578-582.
15. Rockwood K, Stadnyk K, MacKnight C, McDowell I, Hébert R, Hogan DB. A brief
clinical measure of frailty, Lancet. 1999; 353: 205-206.
16. Rockwood K, Song X, MacKnight C, Bergman H, Hogan DB, McDowell I, Mitnitski
A. A global clinical measure of fitness and frailty in elderly people. CMAJ. 2005; 173:
489-495.
17. Rockwood K, Silvius JL, Fox RA. A standard Comprehensive Geriatric Assessment:
Helping your elderly patients maintain functional well-being. Postgrad Med. 1998;
103:247-264.
18. Folstein MF, Folstein SE, McHugh PR. "Mini-mental state". A practical method for
grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12:189-198.
19. Jones DM, Song X, Rockwood K. Operationalizing a frailty index from standardized
comprehensive geriatric assessment. J Am Geriatr Soc. 2004; 52: 1929-1233.
20. Jones D, Song X, Mitnitski A, Rockwood K. Evaluation of a frailty index based on a
comprehensive geriatric assessment in a population based study of elderly Canadians.
Aging Clin Exp Res. 2005; 17: 465-471.
21. Rockwood K, Mitnitski A, Song X, Steen B, Skoog I. Long-term risks of death and
institutionalization of elderly people in relation to deficit accumulation at age 70. J Am
Geriatr Soc. 2006; 54:975-979.
22. Lee SJ, Lindquist K, Segal MR, Covinsky KE. Development and validation of a
prognostic index for 4-year mortality in older adults. JAMA. 2006; 295: 801-808.
23. Mitnitski AB, Bao L, Rockwood K. Going from bad to worse: A stochastic model of
transitions in deficit accumulation, in relation to mortality. Mech Ageing Dev. 2006;127:
490-493.
24. Mitnitski A, Song X, Skoog I, Broe AG, Cox JL, Grunfeld E, Rockwood K. Relative
fitness and frailty of elderly men and women in developed countries, in relation to
mortality. J Am Geriatr Soc. 2005;
53: 2184-2189.
25. Mitnitski AB, Mogilner AJ, MacKnight C, Rockwood K. The mortality rate as a
function of accumulated deficits in a frailty index. Mech Ageing Dev. 2002; 123: 1459-
1462.
12
26. Vaupel JW, Manton KG, Stallard E. The impact of heterogeneity in individual frailty
on the dynamics of mortality. Demography. 1979; 9: 439-454.
27. Yashin AI, Ukraintseva SV, DeBenedictis G, et al. Have the oldest old adults ever
been frail in the past? A hyposthesis that explains modern trends in survival. J Gerontol
Med Sci. 2001;56A: B432-B442.
28. Rockwood K, Mogilner AJ., Mitnitski A. Changes with age in the distribution of a
frailty index. Mech Ageing Dev. 2004; 125: 517-519.
29 . Rockwood K, Mitnitski A Limit to deficit accumulation in elderly people Mech
Ageing Dev. 2006; 127; 494-496.
30. Mitnitski A, Rockwood K. Decrease in the relative heterogeneity of health with age: a
cross-national comparison. Mech Ageing Dev 2006;127:70-72.
31. Ashby WR. An introduction to cybernetics. London: Chapman & Hall Ltd, 1952; 295.
32. Thaler DS. Design for an aging brain. Neurobiol Aging. 2002; 23: 13-15.
33. Argollo de Menezes M, Barabashi AL. Separating internal and external dynamics of
complex systems. Phys Rev Lett. 2004; 93: 068701
34. Kirkwood TB, Feder M, Finch CE, Franceschi C, Globerson A, Klingenberg CP,
LaMarco K, Omholt S, Westendorp RG. 2005. What accounts for the wide variation in
life span of genetically identical organisms reared in a constant environment? Mech
Ageing Dev. 2005; 126: 439-443.
35. Kirkwood TB. Understanding the odd science of aging. Cell. 2005; 120: 437-447.
36. Boehm M, Slack F. A developmental timing microRNA and its target regulate life
span in C. elegans. Science. 2005; 310: 1954-1957.
37. Taylor RW, Barron MJ, Borthwick GM, et al. Mitochondrial DNA mutations in
human colonic crypt stem cells. J Clin Invest. 2003; 112: 1351-1360.
38. Shock NW. Age changes in some physiological processes. Geriatrics. 1957; 12: 40-48.
39. Strehler BL, Mildvan AS. A General Theory of Mortality and Aging. Science. 1960;
132: 14-21.
40. Gavrilov LA, Gavrilova NS. The reliability theory of aging and longevity. J Theor
Biol. 2001; 213: 527-545.
13
41. Bharucha-Reid, A. T. Elements of the theory of Markov processes and their
applications., McGraw-Hill Series in Probability and Statistics McGraw-Hill Book Co.,
Inc., New York-Toronto-London, 1960.
42. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al.
Cardiovascular Health Study Collaborative Research Group. Frailty in older adults:
evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001; 56: M146-M156.
43. Song X, Mitnitski A, MacKnight C, Rockwood K. Assessment of Individual risk of
death using self-report data: an artificial neural network compared to a frailty Index. J Am
Geriatr Soc. 2004; 52: 1180-1184.
44. Bandeen-Roche K, Xue Q-L, Ferrucci L, Walstom J, Guralnik, Chaves P, Zeger AL,
Fried L. Phenotype of frailty: characteriszation in the Women’s Health and Aging Study.
J Gerontol Med Sci. 2006;61A: 262-266.
45. Ferrucci L, Mahallati A, Simonchick EM, Frailty and foolishness of Eos. J Gerontol
Med Sci. 2006;61A: 260-261.
46.Mitnitski AB, Rockwood K. Help Available--Phenomenological Models for Research
on Aging. Sci. SAGE KE 2003; vp2,
http://sageke.sciencemag.org/cgi/content/full/sageke;2003/12/vp2
47. MacKnight C, Rockwood K. Rasch analysis of the hierarchical assessment of balance
and mobility (HABAM). J Clin Epidemiol. 2000; 53: 1242-1247.
48. Mitnitski AB, Graham JE, Mogilner AJ, Rockwood K. Frailty, fitness and late-life
mortality in relation to chronological and biological age. BMC Geriatr. 2002;2:1.
49. Karasik D, Demissie S, Cupples LA, Kiel DP. Disentangling the Genetic
Determinants of Human Aging: Biological Age as an Alternative to the Use of Survival
Measures. J Gerontol A Biol Sci Med Sci.
2005; 60:574-587.
50. Warner HR. Current Status of Efforts to Measure and Modulate the Biological Rate
of Aging. J Gerontol A Biol Sci Med Sci. 2004; 59: 692-696.
51. Butler RN, Sprott R, Warner H, et al. Biomarkers of aging: from primitive organisms
to humans. J Gerontol A Biol Sci Med Sci. 2004; 59: B560-B567.
52. Weale RA. A Note on Age-Related Biomarkers. J Gerontol A Biol Sci Med Sci.
2005; 60: 35-38.
53. Parentini I, Cavallini G, Donati A, Gori Z, Bergamini E. Accumulation of Dolichol in
Older Tissues Satisfies the Proposed Criteria To Be Qualified a Biomarker of Aging. J
Gerontol A Biol Sci Med Sci. 2005; 60: 39-43.
14
... Rockwood defines frailty as the accumulation of physical and psychological deficits (6). Based on this model, he developed the frailty index, which takes into account clinical signs, geriatrics syndromes, and level of disability (7). Using this index, patients can be identified as frail or robust, a helpful categorization for patient management. ...
Article
ContextThe COVID-19 pandemic has placed a tremendous stress on healthcare systems and caused reorganization. As the pandemic intensifies, identifying the profile of patients with COVID-19 was primordial in order to predict negative outcomes and organize healthcare resources. Age is associated with COVID-19’s mortality, but for obvious ethical reasons, chronological age cannot be the sole criterion for predicting negative outcomes.Objective The objective of this study was to determine the relationship between frailty index (FI) and length of hospital stay, and death in a non-COVID population of patients aged 75 years old and above.Methods and designA retrospective, analytical, single-centered observational study was performed in the geriatric short-stay accommodation unit at Guadeloupe University Hospital. For this study, 158 patients who were at least 75 years old were recruited from November 2020 to May 2021. FI was calculated as the number of deficits in a participant divided by the total number of deficits considered (the cut-off of FI is.25 in outpatient). Multivariate logistics regression analyses were conducted to assess the association between frailty and death, and length of stay.ResultsThe average age of the participants was 85.7 ± 6.74 with a range of 75–104. Twenty-four of the patients died during hospitalization. FI was only significantly associated with mortality even after adjustment for age and gender (HR 26.3, 95% CI 1.7–413.4, P = 0.021). The association was stronger in the highest tertile of the FI (age- and gender-adjusted HR 4.6, 95% CI 1.39–15.11, P = 0.01). There was no significant interaction between FI and length of stay.Conclusion Our study shows an association between FI (in terms of age-related deficit accumulation) and mortality in a non-COVID geriatric short-stay unit in Guadeloupe. The FI seems to have a lower capacity to catch events such as length of stay in this very complex population. Further research studies have to be conducted for better understanding and investigation of our findings.
... Unfortunately, only 60% of older adults admitted to SNFs are discharged home, and many are re-hospitalized, acquire new disabilities, or die (Achterberg et al., 2019;Buurman et al., 2016). Research from post-acute facilities in Europe, support that frailty, a state of vulnerability (Rockwood & Mitnitski, 2007), is a strong predictor of overall health outcomes after hospitalization, (Kerminen et al., 2020;Stuck et al., 2021) including functional recovery. However, measuring frailty during SNF has not previously been done in the complex postacute care US system. ...
Article
Full-text available
Functional status and quality of life are not routinely assessed after skilled nursing facility (SNF) discharge. We determined feasibility of measuring frailty among adults ≥65 years admitted to SNF after hospitalization, and post-discharge outcomes. We calculated a frailty index (non-frail [≤0.25], mild frailty [0.26–0.35], moderate [0.36–0.45], and severe [>0.45]). After SNF discharge, we conducted serial telephone interviews measuring ability to perform functional activities and Patient Reported Outcome Measurement Information System (PROMIS) scores. Overall of 68 screened patients, 42 were eligible, and 24 (57.1%) eligible patients were enrolled. Of these, 5 (20.8%) were admitted after elective hospitalizations, 17 (70.8%) were female, and 11 (45.8%) had moderate-to-severe frailty. Frailty was measured in all participants in a mean 32.1 minutes. At 90 days, a total of three participants died, and two were lost to follow-up. Post-discharge functional status varied by frailty, with moderate-to-severe frailty having persistent impairment and lower PROMIS scores (worse quality of life) compared to those with no or mild frailty (38.2 [13.7] vs. 47.3 [8.1] p = .04). Measuring frailty and quality of life in older patients admitted to SNF is feasible. Furthermore, measuring frailty may help identify those at particularly high risk of poor recovery and lower quality of life after discharge.
... The 31-item VA-FI 15 was developed based on the deficit accumulation conceptual framework that assumes that frailty is the result of multiple physical, functional, psychological, and social variables. 21 Unlike the frailty phenotype, the deficit accumulation approach does not rely on predetermined variables and might be more suitable to our veteran population because it focuses on multimorbidity, cognitive impairment, and disability. 22 The VA-FI has been validated in more than 2 million veterans and has been shown to be associated with mortality. ...
Article
Full-text available
Background Studies have shown that COVID-19 vaccination is effective at preventing infection and death in older populations. However, whether vaccination effectiveness is reduced in patients with frailty is unclear. We aimed to compare vaccine effectiveness against hospitalisation and death after COVID-19 during the surge of the delta (B.1.617.2) variant of SARS-CoV-2 according to patients' frailty status. Methods In this retrospective cohort study, we used data derived from the US Veterans Health Administration (VHA) facilities and the US Department of Veterans Affairs (VA) COVID-19 Shared Data Resource, which contains information from the VA National Surveillance Tool, death certificates, and National Cemetery Administration. We included veterans aged 19 years or older who tested positive for SARS-CoV-2 using RT-PCR or antigen tests between July 25 and Sept 30, 2021, with no record of a previous positive test. Deaths were identified through VHA facilities, death certificates, and National Cemetery Administration data available from VA databases. We also retrieved data including sociodemographic characteristics, medical conditions diagnosed at baseline, frailty score, and vaccination information. The primary outcomes were COVID-19-associated hospitalisations and all-cause deaths at 30 days from testing positive for SARS-CoV-2. The odds ratio (OR) for COVID-19-associated hospitalisation and hazard ratio (HR) for death of vaccinated patients compared with the unvaccinated patients were estimated according to frailty categories of robust, pre-frail, or frail. Vaccine effectiveness was estimated as 1 minus the OR for COVID-19-associated hospitalisation, and 1 minus the HR for death. Findings We identified 57 784 veterans (mean age 57·5 years [SD 16·7], 50 642 [87·6%] males, and 40 743 [70·5%] White people), of whom 28 497 (49·3%) were categorised as robust, 16 737 (29·0%) as pre-frail, and 12 550 (21·7%) as frail. There were 2577 all-cause deaths (676 [26·2%] in the vaccinated group and 1901 [73·8%] in the unvaccinated group), and 7857 COVID-19-associated hospitalisations (2749 [35·0%] in the vaccinated group and 5108 [65·0%] in the unvaccinated group) within 30 days of a positive SARS-CoV-2 test. Vaccine effectiveness against COVID-19-associated hospitalisation within 30 days of a positive SARS-CoV-2 test was 65% (95% CI 61–69) in the robust group, 54% (48–58) in the pre-frail group, and 36% (30–42) in the frail group. By 30 days of a positive SARS-CoV-2 test, all-cause death was 79% (95% CI 74–84) in the robust group, 79% (75–83) in the pre-frail group, and 68% (63–71) in the frail group. Interpretation Compared with non-frail patients (pre-frail and robust), those with frailty had lower levels of vaccination protection against COVID-19-associated hospitalisation and all-cause death. Future studies investigating COVID-19 vaccine effectiveness should incorporate frailty assessments and actively recruit older adults with frailty. Funding Miami VA Healthcare System Geriatric Research Education and Clinical Center.
... It seems to improve the accuracy of the Frailty Phenotype to predict adverse events (death, hospitalization, incident frailty, and disability) 20 in older adults even better than classical frailty tools, as the two most used tool 24 : the Frailty Phenotype 1 and the Frailty Index. 25 In addition, it allows continuous assessment of frailty levels, being sensitive to small changes that have been shown to be related to the risk of different adverse events such as disability, hospitalization and mortality, potentially overcoming several of the pitfalls of previous frailty assessment. 26 It is composed by five domains [gait speed, grip strength, physical activity, body mass index (BMI), and balance]. ...
Article
Background: Frailty and sarcopenia are age-associated syndromes that have been associated with the risk of several adverse events, mainly functional decline and death, that usually coexist. However, the potential role of one of them (sarcopenia) in modulating some of those adverse events associated to the other one (frailty) has not been explored. The aim of this work is to assess the role of sarcopenia within the frailty transitions and mortality in older people. Methods: Data from the Toledo Study of Healthy Aging (TSHA) were used. TSHA is a cohort of community-dwelling older adults ≥65. Frailty was assessed according with the Frailty Phenotype (FP) and the Frailty Trait Scale-5 (FTS5) at baseline and at follow-up. Basal sarcopenia status was measured with the standardized Foundation for the National Institutes of Health criteria. Fisher's exact test and logistic regression model were used to determine if sarcopenia modified the transition of frailty states (median follow-up of 2.99 years) and Cox proportional hazard model was used for assessing mortality. Results: There were 1538 participants (74.73 ± 5.73; 45.51% men) included. Transitions from robustness to prefrailty and frailty according to FP were more frequent in sarcopenic than in non-sarcopenic participants (32.37% vs. 15.18%, P ≤ 0.001; 5.76% vs. 1.12%; P ≤ 0.001, respectively) and from prefrailty-to-frailty (12.68% vs. 4.27%; P = 0.0026). Improvement from prefrail-to-robust and remaining robust was more frequent in non-sarcopenic participants (52.56% vs. 33.80%, P ≤ 0.001; 80.18% vs 61.15%, P ≤ 0.001, respectively). When classified by FTS5, this was also the case for the transition from non-frail-to-frail (25.91% vs. 4.47%, P ≤ 0.001) and for remaining stable as non-frail (91.25% vs. 70.98%, P ≤ 0.001). Sarcopenia was associated with an increased risk of progression from robustness-to-prefrailty [odds ratio (OR) 2.34 (95% confidence interval, CI) (1.51, 3.63); P ≤ 0.001], from prefrailty-to-frailty [OR(95% CI) 2.50 (1.08, 5.79); P = 0.033] (FP), and from non-frail-to-frail [OR(95% CI) 4.73 (2.94, 7.62); P-value ≤ 0.001]. Sarcopenia does not seem to modify the risk of death associated with a poor frailty status (hazard ratios (HR, 95%) P > 0.05). Conclusions: Transitions within frailty status, but not the risk of death associated to frailty, are modulated by the presence of sarcopenia.
Article
Background: Polypharmacy and potentially inappropriate medications (PIMs) are common among older adults with blood cancers, but their association with frailty and how to manage them optimally remain unclear. Patients and methods: From 2015 to 2019, patients aged ≥75 years presenting for initial oncology consult underwent screening geriatric assessment. Patients were determined to be robust, prefrail, or frail via deficit accumulation and phenotypic approaches. We quantified each patient's total number of medications and PIMs using the Anticholinergic Risk Scale (ARS) and a scale we generated using the NCCN Medications of Concern called the Geriatric Oncology Potentially Inappropriate Medications (GO-PIM) scale. We assessed cross-sectional associations of PIMs with frailty in multivariable regression models adjusting for age, gender, and comorbidity. Results: Of 785 patients assessed, 603 (77%) were taking ≥5 medications and 421 (54%) were taking ≥8 medications; 201 (25%) were taking at least 1 PIM based on the ARS and 343 (44%) at least 1 PIM based on the GO-PIM scale. Among the 468 (60%) patients on active cancer treatment, taking ≥8 medications was associated with frailty (adjusted odds ratio [aOR], 2.82; 95% CI, 1.92-4.17). With each additional medication, the odds of being prefrail or frail increased 8% (aOR, 1.08; 95% CI, 1.04-1.12). With each 1-point increase on the ARS, the odds of being prefrail or frail increased 19% (aOR, 1.19; 95% CI, 1.03-1.39); with each additional PIM based on the GO-PIM scale, the odds increased 65% (aOR, 1.65; 95% CI, 1.34-2.04). Conclusions: Polypharmacy and PIMs are prevalent among older patients with blood cancers; taking ≥8 medications is strongly associated with frailty. These data suggest careful medication reconciliation for this population may be helpful, and deprescribing when possible is high-yield, especially for PIMs on the GO-PIM scale.
Article
Background: Frailty, a state of vulnerability to stressors resulting from loss of physiological reserve due to multisystemic dysfunction, is common among hospitalized older adults. Hospital clinicians need objective and practical instruments that identify older adults with frailty. The FI-LAB is based on laboratory values and vital signs and may capture biological changes of frailty that predispose hospitalized older adults to complications. The study's aim was to assess the association of the FI-LAB versus VA-FI with hospital and post-hospital clinical outcomes in older adults. Methods: Retrospective cohort study was conducted on Veterans aged ≥60 admitted to a VA hospital. We identified acute hospitalizations January 2011-December-2014 with 1-year follow-up. A 31-item FI-LAB was created from blood laboratory tests and vital signs collected within the first 48 h of admission and scores were categorized as low (<0.25), moderate (0.25-0.40), and high (>0.40). For each FI-LAB group, we obtained odds ratio (OR) and confidence intervals (CI) for hospital and post-hospital outcomes using multivariate binomial logistic regression. Additionally, we calculated hazard ratios (HR) and CI for all-cause in-hospital mortality comparing the high and moderate FI-LAB group with the low group. Results: Patients were 1407 Veterans, mean age 72.7 (SD = 9.0), 67.8% Caucasian, 96.1% males, 47.0% (n = 661), 41.0% (n = 577), and 12.0% (n = 169) were in the low, moderate, and high FI-LAB groups, respectively. Moderate and high scores were associated with prolonged LOS, OR:1.62 (95% CI:1.29-2.03); and 3.36 (95% CI:2.27-4.99), ICU admission, OR:1.40 (95% CI:1.03-1.90); and OR:2.00 (95% CI:1.33-3.02), nursing home placement OR:2.36 (95% CI:1.26-4.44); and 5.99 (95% CI:2.83-12.70), 30-day readmissions OR:1.74 (95% CI:1.20-2.52); and 2.20 (95% CI:1.30-3.74), 30-day mortality OR: 2.51 (95% CI:1.01-6.23); and 8.97 (95% CI:3.42-23.53), 6-month mortality OR:3.00 (95% CI:1.90-4.74); and 6.16 (95% CI:3.55-10.71), and 1-year mortality OR: 2.66 (95% CI:1.87-3.79); and 4.76 (95% CI:3.00-7.54) respectively. The high FI-LAB group showed higher risk of in-hospital mortality, HR:18.17 (95% CI:4.01-80.52) with an area-under-the-curve of 0.843 (95% CI:0.75-0.93). Conclusions: High and moderate FI-LAB scores were associated with worse in-hospital and post-hospital outcomes. The FI-LAB may identify hospitalized older patients with frailty at higher risk and assist clinicians in implementing strategies to improve outcomes.
Article
Background a Frailty Index (FI) calculated by the accumulation of deficits is often used to quantify the extent of frailty in individuals in specific settings. This study aimed to derive a FI that can be applied across three standardised international Residential Assessment Instrument assessments (interRAI), used at different stages of ageing and the corresponding increase in support needs. Methods deficit items common to the interRAI Contact Assessment (CA), Home Care (HC) or Long-Term Care Facilities assessment (LTCF) were identified and recoded to form a cumulative deficit FI. The index was validated using a large dataset of needs assessments of older people in New Zealand against mortality prediction using Kaplan Meier curves and logistic regression models. The index was further validated by comparing its performance with a previously validated index in the HC cohort. Results the index comprised 15 questions across seven domains. The assessment cohort and their mean frailty (SD) were: 89,506 CA with 0.26 (0.15), 151,270 HC with 0.36 (0.15) and 83,473 LTCF with 0.41 (0.17). The index predicted 1-year mortality for each of the CA, HC and LTCF, cohorts with area under the receiver operating characteristic curves (AUCs) of 0.741 (95% confidence interval, CI: 0.718–0.762), 0.687 (95%CI: 0.684–0.690) and 0.674 (95%CI: 0.670–0.678), respectively. Conclusions the results for this multi-instrument FI are congruent with the differences in frailty expected for people in the target settings for these instruments and appropriately associated with mortality at each stage of the journey of progressive ageing.
Article
Full-text available
Objectives To investigate the prevalence of the comprehensive frailty and its associated factors among community dwelling older adults. Design A cross-sectional study. Setting Six community healthcare centres in Xi’an City, Northwest China. Participants A total of 2647 community dwelling older adults completed the study between March and August 2021. Primary and secondary outcome measures The primary outcome was the prevalence of frailty, measured with the Comprehensive Frailty Assessment Instrument. The secondary outcomes were potential factors associated with frailty, measured with a social-demographic and health-related information sheet, the Short-Form Mini-Nutritional Assessment and the Pittsburgh Sleep Quality Index. Results The participants averaged 27.77±10.13 in the total score of the Comprehensive Frailty Assessment Instrument. According to the cut-off points defining the classification of frailty, the majority of the participants were with mild (n=1478, 55.8%) or high (n=390, 14.8%) frailty. Multivariate stepwise linear regression analysis demonstrated that older age, lower educational level, empty nesters, higher level of self-perceived medical burden, abnormal body weight, physical inactivity, medication taking, increased number of clinic visit, undernutrition and poor sleep quality are associated with higher total score in the Comprehensive Frailty Assessment Instrument, indicating higher level of frailty. Multivariate multinomial logistic regression analysis exhibited similar findings but further captured female gender as a risk factor for the presence of mild and high frailty compared with no-low frailty. Conclusion The prevalence of the comprehensive frailty and frailty in the physiological, psychological, social and environmental domains is high. A variety of social-demographic, health-related and behavioural factors were associated with the comprehensive frailty. Further investigations on frailty prevalence and its associated factors based on comprehensive assessments are desirable.
Article
Hypertension is a frequent finding in elderly patients. Hypertension in older age can be both associated with frailty and represent a risk factor for frailty. Hypertension is recognized as a main risk factor for cardiovascular diseases such as heart failure, atrial fibrillation, and stroke and the occurrence of these diseases may provoke a decline in health status and/or worsen the degree of frailty. Blood pressure targets in hypertensive older and frail patients are not completely defined. However, specific evaluations of individual patients and their co-morbidities and assessment of domains and components of frailty, together with weighted consideration of drug use, may help in finding the appropriate therapy.
Article
To study the trade-offs and the macroeconomic repercussions of rising health care demand in a public health service, we develop a continuous-time overlapping generations model with a public health care sector and a realistic aging process. Health care services are provided to two groups of individuals, the healthy and the sick, free of charge at the point of service. Without a price mechanism, the government relies on a queuing rule for allocating its services. We conceptualize this mechanism as congestion that lowers the efficacy of health care. Then, we calibrate the model to match UK data from 2007-2016 and analyze the steady-state, general equilibrium response of the economy of shocks to productivity/income and medical effectiveness. Our analysis suggests that the optimal response to an increase in the demand for health care depends strongly on whether it is due to an increase in income or medical effectiveness. We also show that there is disagreement across age-groups on the preferred policy.
Article
Biomarkers of aging would be highly desirable, but so far, a definitive panel of biomarkers to predict mortality risk has not been obtained, even though many traits that vary with age have been identified. This lack hinders the search for interventions that may retard the rate of aging in mammals. The recent discovery and characterization of many longevity genes in animal model systems, such as nematodes, fruit flies, and mice, are providing new targets for research by providing insight into mechanisms of longevity regulation in these model systems. It is hoped that this will ultimately lead to interventions to delay the development of age-related pathology in humans.