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Journal of Aging Research
Volume 2011, Article ID 912680, 7 pages
SuccessfulAging andLongevity inOlderOldWomen:TheRoleof
1Department of Psychology and Institute of Gerontology, Wayne State University, Detroit, MI 48202-3801, USA
2Veterans Health Administration, HSR&D/RR&D Center of Excellence, Tampa, FL 33637-1022, USA
Correspondence should be addressed to Daniel Paulson, firstname.lastname@example.org
Received 15 March 2011; Revised 2 May 2011; Accepted 17 May 2011
Academic Editor: B. A. Hagberg
Copyright © 2011 Daniel Paulson et al.ThisisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Based in successful aging theory and terminal cognitive drop research, this paper investigates cerebrovascular burden (CVB), de-
in the Health and Retirement Survey (years 2000–2008). Mortality at 2, 6, and 8 year intervals was predicted using CVB (diabetes,
heart disease, hypertension), depressive symptoms (Center for Epidemiological Studies Depression Scale), and cognitive decline
(decline of 1 standard deviation or more on the 35-point Telephone Interview for Cognitive Status over 2 years). At most waves
(2002, 2004, and 2006) mortality was predicted by CVB, depressive symptoms, and cognitive drop measured 2 years prior. CVB
and depressive symptoms at the 2000 wave predicted mortality at 6 and 8 years. Older women with the greatest longevity had
low CVB, robust cognitive functioning, and few depression symptoms, supporting successful aging theory and terminal cognitive
Rowe and Kahn  proposed criteria for successful aging
comprised of avoidance of disease, maintenance of high cog-
nitive and physical function, and sustained engagement in
prolific MacArthur Foundation Study of Successful Aging,
a $10 million, 10-year research effort led by Rowe and Kahn.
The objectives of this study, and the theoretical framework
that grew from it, are to better understand risk factors for
decline and to inform prevention efforts. For instance, work
drawn from this initiative concluded that pulmonary health
relates to both gross motor and cognitive functioning in
late life, suggesting this as an area for primary intervention
in preserving late-life independence . Drawing on the
MacArthur Study data, Others have investigated modifiable
risk factors for dementia, concluding that late-life depression
may be a precursor of cognitive decline . Still other work
useful to others had lower rates of disability and mortality
than less-engaged elders, emphasizing the importance of
social engagement and productive activities . While sup-
port for this theory of successful aging has been mixed [5, 6],
it provides a useful framework for examining longevity. This
paper will examine whether the Rowe and Kahn successful
aging variables are each independently related to longevity.
In the current paper we chose to examine this theory in older
old women over 80 years. Women constitute a majority of
all older adults over 80 but more importantly, they are more
likely than men to experience disability and to live more
years with disability than men . Geriatric syndromes such
as cardiovascular disease, cognitive decline, and depression
compromise functional independence more with increasing
age . Age 80 represents a point when medical treatment
risks associated with these comorbidities.
Behavioral factors as defined in this paper include psy-
chological aspects of functioning that can be measured
through self-report or performance-based assessment, such
as mood and cognitive functioning (i.e., depression and cog-
nition). Important behavioral factors such as cognition and
depression represent major risk factors for disability, often
preceding disability  and possibly reducing longevity.
Serious disability, especially mobility loss, has been linked
to reduced survival . Identifying behavioral factors that
2 Journal of Aging Research
hasten disability onset may then lead to improved models
of integrated care. Behavioral factors such as depression and
cognitive decline may best be understood when integrated
with chronic disease, especially those that enhance vascular
Though Rowe and Kahn interpreted “avoidance of dis-
ease” broadly, vascular disease is particularly significant to
healthy aging as these chronic conditions (e.g., hyperten-
sion, atrial fibrillation, and diabetes) gradually compromise
adaptive resources. Neural network functioning is broadly
compromised by the effect of vascular disease on cerebral
tissue, termed cerebrovascular burden (CVB). CVB is also
associated with higher stroke risk, cardiac disease, and sen-
sorimotor impairment. Moreover, high CVB hastens the
manifestation of clinically significant cognitive impairment,
regardless of the specific etiology of cognitive decline (e.g.,
normative aging, Alzheimer’s disease, Parkinson’s disease,
and vascular dementia) . Elders with high CVB tend to
have less prefrontal white matter volume, more white matter
hyperintensities, and comparably impaired executive func-
tioning . Considerable evidence also exists that CVB also
contributes to the development of late-life depression symp-
toms [13, 14]. Thus, in addition to being a broad measure
of physical health in the Rowe and Kahn model, inclusion
of CVB will distinguish variance in mortality risk directly
related to this variable, thereby providing a more stringent
test of how depression and cognition independently relate to
Successful aging theory identifies sustained engagement
in social and productive activities as central to healthy aging.
Clinical depression throughout the lifespan is characterized
by reduced enjoyment in activities and decreased social en-
gagement. As such, depression symptoms in late life repre-
sent a significant barrier to successful aging based on this
interpretation. Generally speaking, more depression symp-
toms translate to poorer health outcomes. For instance,
people experiencing depression are at greater risk of a first
heart attack , stroke [16, 17], cancer , worse health
outcomes after controlling for cardiovascular risks , and
higher mortality . Depression in later life was found to
be a significant risk factor for death. Mehta et al.  re-
ported that, in a large sample of community-dwelling
elders, mortality was significantly predicted by both number
of depressive symptoms and performance on a measure of
cognitive functioning. Similarly, it was reported that in a
sample of older patients with debilitating or chronic medical
diagnoses, after controlling for age, comorbidity and illness
severity, functional impairment and cognitive functioning,
years.Inalarge(N = 3065)Dutchsampleofindividuals
between the ages of 55 and 80, depression was identified as
a significant risk factor for death over four years. However,
of other variables such as chronic disease, smoking, and
physical inactivity .
The third domain of successful aging theory identifies
preservation of cognitive and physical functioning as critical
to successful aging. Cognitive impairment limits quality
of life by reducing the capacity for meaningful work and
social interaction , and rapid loss of cognitive faculties
often suggests medical decline with a heightened mortality
risk [22, 24]. Terminal cognitive drop is identified as an
accelerated loss of cognitive functioning preceding death
, by contrast to terminal decline which is a linear de-
cline function preceding death . A review from 2002
concluded that, largely because testing terminal drop theory
requires a repeated-measures design, limited data existed
supporting this theory . Since this review, several longi-
tudinal studies have been published, including a recent study
State Exam , a brief cognitive screening measure, expe-
rienced more activity of daily living (ADL) disability and
higher mortality rates than elders with more stable scores
. While estimates of the temporal relationship between
terminal drop and death vary, recent research identified
evidence of terminal drop at a mean of 42 months before
death  in a large sample of dementia-free elders. These
estimates are roughly similar to the original estimates of
about 5 years reported by K. F. Riegel and R. M. Riegel
. Similarly, past work drawing on the Health and Retire-
ment Survey (HRS) data identified a relationship between
cognitive impairment and mortality over a 2-year interval
; however, this study evaluated cognitive decline cross-
sectionally and did not include other markers of decline
such as CVB or depression. Of note, Langa et al. reported
evidence of compression of cognitive morbidity; elders with
risk of death over 2 years than those with similar levels of
impairment in 1993.
Comorbid cognitive impairment and depressive symp-
toms suggest particularly high risk of death [19, 20]; how-
ever, these studies evaluated cognitive functioning cross-
sectionally. While impairment on cognition measures sug-
concept of terminal cognitive drop specifies rapid loss of
cognitive functioning over a brief period of time. Relatively
few studies relating to cognition and longevity evaluate how
decline over brief periods relates to longevity, and even fewer
investigate whether depression and cognitive decline are
independent predictors of mortality. Because cognition and
in late life [31, 32], it is important to distinguish the indi-
vidual relationships of these variables with longevity.
2.Objectivesof the PresentStudy
This investigation seeks to examine whether all three do-
mains of Rowe and Kahn’s successful aging paradigm inde-
pendently predict longevity in a large sample of stroke-free
women over the age of 80. In particular, this study empha-
sizes two behavioral domains that are of particular relevance
in clinical settings—depression and cognitive decline. This
theoretical orientation, based on a broad reading of the
literature, posits that elders with few depression symptoms
and preserved cognitive functioning will experience greater
longevity. In addition to CVB, other health variables such
as self-rated health and Body Mass Index (BMI) will be
Journal of Aging Research3
included in order to control for general health. The model
proposed predicts that both depression and cognitive decline
will predict mortality in this sample. Hypothesis 1 looks at
survival across the entire study period of 8 years, whereas
Hypothesis 2 examines proximate predictors of death across
nitive decline at baseline (2000 wave) will all be independent
predictors of longevity conceptualized as survival across the
entire 8-year study period.
Hypothesis 2. Incidence of death at each wave (2002, 2004,
2006, and 2008) will be predicted by CVB, cognitive decline,
and depressive symptoms at the previous wave.
3.1. Sample. The Health and Retirement Survey (HRS) is
a prospective cohort study conducted by the University of
The first wave of the HRS occurred in 1992 with a 51-to-
61 year-old cohort and was merged with the older (70 years
and older) Asset and Health Dynamics of the Oldest Old
added in 1998 to fill in the gaps between these two groups.
Briefly, the HRS is a multistage probability cohort sample
of US households. Further details on the HRS design and
methods have been previously published .
The present study utilized the Health and Retirement
Survey (HRS) that was prepared by the RAND Center for
the Study of Aging (RAND HRS). The selected portion of
this publically available, longitudinal dataset includes five
waves at two-year intervals from 2000 through 2008 (waves
5 through 9). Inclusion criteria included age over 80 years
at the first wave and female sex. This study made use of the
1998 TICS score to identify incidence of cognitive decline
from 1998 to 2000. Because stroke is associated with highly
variable cognitive performance, participants with history
of stroke prior to 1998 were excluded. Respondents who
were unable to complete survey materials at the 1998 data
collection were also excluded as missing data would have
precluded calculation of 1998–2000 cognitive change scores.
This data set is demographically representative of the female
US population over age 80.
3.2.1. Medical Data. Medical data (hypertension, diabetes,
history of heart disease, height, and weight) and lifetime his-
tory of smoking were collected by self-report. CVB was iden-
tified as the number of cerebrovascular risk factors (hyper-
tension, diabetes, and history of heart disease) reported
creating a score ranging from 0 to 3.
3.2.2. Depressive Symptoms. A shortened, 8-item form of the
original Center for Epidemiological Studies Depression Scale
(CESD) was used to evaluate depression . Six of the
eight items are negatively worded, and two are positively
worded. Participants are asked to respond “yes” or “no”
to each item (“was depressed,” “everything was an effort,”
“sleep was restless,” “was happy,” “felt lonely,” “enjoyed life,”
“felt sad,” “could not get going”) that occurred within the
preceding week. Scores ranged from 0 to 8 with higher scores
indicating greater depressive symptoms. Using HRS data, the
reliability of the 8-item CESD measure was adequate, with
Cronbach’s alpha ranging from .81 to .83 between waves
in the longer 20-item CESD have been demonstrated using
these 8 items [35, 36]. The CESD is broadly used in the
epidemiological study of late-life depression . While the
CESD literature describes a clinical cutoff that can be used to
distinguish respondents with probable depression, this
measure was used as a semicontinuous variable representing
the full range depressive symptoms in this population sam-
3.2.3. Cognitive Functioning. The HRS data includes a brief
standardized 35-point measure of cognitive functioning that
was developed for remote screening of cognitive disorders
based on the Telephone Interview for Cognitive Status .
It includes indices of orientation, concentration, short-term
memory, mathematical skills, praxis and language and has
a maximum score of 35 points (observed range: 0–35) with
higher scores reflecting better functioning. This instrument
has a Cronbach’s alpha of.69 and past work has identified
factors reflecting mental status and memory . The TICS
has demonstrated high test-retest reliability and is generally
sensitive to cognitive impairment [38, 40–42]. For all of
the following models cognitive decline was identified as
a decrease in TICS score from one wave to the next wave
of more than one standard deviation (6.1 points) based on
3.2.4. Self-Rated Health. Self-rated health change was mea-
sured with a single question assessing the respondents’
perception of change in health since the last data collection
2 years prior. Change in self-rated health was assessed with
the question, “Compared to your health when we talked
with you in (last wave) would you say that your health is
better now, about the same, or worse?” Response options
included “much better,” “somewhat better,” “same,” “some-
what worse,” “much worse,” comprising a 5-point scale. This
variable was included as a semicontinuous measure.
Binary logistic regression was performed to model the likeli-
hood of death between baseline (2000) and either 2006 or
2008 as a function of demographics, cognitive function,
CVB, and depression. While the study spans the years 2000–
2008, the logistic regression using 2006 as an end point was
included as closer to half of the sample had died at this wave,
thereby optimizing the statistical power of the model. Vari-
ables were entered blockwise with age, years of education,
and body mass index (BMI) entered in the first block. The
second block included CVB as reported in the 2000 wave.
4 Journal of Aging Research
Table 1: Sample description at the 2000 wave (baseline).
Self-rated health change
% high CVB
Note. CVB: number of symptoms comprising cerebrovascular burden (heart
disease, diabetes, hypertension scored 0–3). CESD: Center for Epidemio-
logical Studies Depression Scale, TICS: Telephone Interview for Cognitive
Status, BMI: Body Mass Index.
at the 2000 wave. The fourth block included CESD score at
the 2000 wave and a variable reflecting incidence of decline
greater than 1 standard deviation (>6 points) on the TICS
between the 1998 and 2000 waves.
To better track the relationship between TICS score
decline, mood, and longevity over the course of the study,
four additional logistic regression models were computed.
In the first, incidence of death in 2002 was predicted using
2000 CVB, the 2000 CESD score, and the index reflecting
a drop in TICS score between 1998 and 2000 exceeding
1 standard deviation. The second model predicted death
in 2004 based on CVB in 2002, CESD score in 2002, and
incidence of decline in TICS score of more than 1 standard
deviation between 2000 and 2002. The third and fourth
models predicted death in 2006 and 2008 using similar
Of the 1368 respondents who met criteria at the 1998 wave,
the 2008 wave representing a 64.8% mortality rate over this
8-year period. The sample at the 2000 wave is described in
Table1.Themeanagewas85.6years(SD = 3.8).Onaverage,
respondents had 11 years of formal education (SD = 3.4).
The mean BMI at baseline was 24.4 (SD = 4.9). The average
number of cerebrovascular risk factors reported was 1 (SD =
0.8). The mean CESD score was 2.1 (SD = 2) suggesting
a low rate of depressive symptoms in this population. The
mean score on the 35 TICS measure was 18.1 (SD = 5.9).
As can be seen in Table 2, CVB and depressive symptoms
were significant predictors of mortality between 2000 and
2006. In addition, age, and BMI were also significant predic-
tors. Cognitive decline and self-rated health showed a trend
toward being significant predictors. A slightly different pic-
ture emerged with respect to 2008 outcomes. CVB was
a significant predictor of mortality, and again BMI and
age also predicted mortality. Depressive symptoms showed
a trend toward significance, while self-rated health was not
a significant predictor. Cognitive decline between 1998 and
2000 was not a significant predictor of mortality at the 2008
wave. Overall, the logistic regressions over 6 and 8 years pro-
vided partial support for applying Rowe and Kahn’s success-
ful aging model to longevity.
Four additional logistic regressions predicting death in
2002, 2004, 2006, and 2008 were completed to better under-
stand the relationship between CVB, mood, cognitive
change, and longevity over brief periods of time. CESD and
TICS scores are unavailable for many participants, primarily
for reasons of incapacity. As a result of listwise deletion
caused by absent data on these predictor variables, these
four logistic regression analyses included 69% (2002), 61%
(2004), 54% (2006), and 43% (2008) of respondents who
died, respectively. As described in Table 3, CVB significantly
predictedmortalityin the2002, 2004,and2008 waves.CESD
score was a significant predictor of death in the 2002 and
standard deviation significantly predicted incidence of death
in 2004 and 2008 and showed a trend toward significance in
2002 (P = .088).
The primary findings of the present research are that, among
women over the age of 80, CVB, depressive symptoms, and
rapid cognitive decline (terminal drop) predict incidence of
mortality over brief periods (2 years). CVB significantly pre-
dicted 2-year mortality at 3 of 4 waves, and depressive symp-
toms and cognitive decline significantly predicted mortality
significantly predicted by age, BMI, and CVB. Additionally,
depressive symptoms significantly predicted mortality over 6
years and showed a trend toward significance in predicting
mortality over 8 years. Incidence of rapid cognitive decline
was not a significant predictor of death over 6 or 8 years.
As demonstrated in the analyses predicting death over 2-
year periods, rapid cognitive decline is a robust predictor
of proximal death. Together, these findings suggest that, in
addition to undermining quality of life and independence
as described by Rowe and Kahn, sharp cognitive decline
suggests high risk of imminent death. Cognitive drop, even
in the older old is a significant indicator of declining health
and potentially shortened life. While it is well documented
that cognitive abilities decline in those over 80 , it is
when declines are significantly above the norm when life
expectancy is affected among the older old. By contrast,
many of those who died toward the end of this study
had robust cognitive functioning at this baseline interval.
These results are consistent with past work describing the
relationship between CVB, mood, cognitive decline, and
longevity [19, 20, 22, 24, 28, 29].
The present findings support and extend Rowe and
Kahn’s  successful aging theory by relating to low CVB,
relatively few depressive symptoms, and preserved cognitive
functioning, representing the three domains of successful
Journal of Aging Research5
Table 2: Results of logistic regression predicting incidence of mortality between 2000 and 2006.
Predicting death between 2000 and 2006
Predicting death between 2000 and 2008
0.15 0.10 2.37
2000 CESD score
1998–2000 TICS Drop
Exp(B) 95% CI
Exp(B) 95% CI
1.82 43.441.97 32.87
Note. BMI: Body Mass Index. CVB: cerebrovascular burden, self-rated health change: change in self-rated health from previous wave, CESD: Center for
Epidemiological Studies Depression Scale, TICS: Telephone Interview for Cognitive Status. TICS Drop reflects incidence of decline on TICS score greater than
6 points between 1998 and 2000.
§P between .05 and .10,∗P < .05,ψP < .001, df = 1 for all comparisons.
Table 3: Results of logistic regression analyses predicting death at 2002, 2004, 2006, and 2008 waves based on depressive symptoms and
incidence of cognitive decline.
Predicting death in 2002
Predicting death in 2006
Predicting death in 2004
Predicting death in 2008
Exp(B) 95% CI
Exp(B) 95% CI
1998–2000 TICS >1SD
2000–2002 TICS >1SD
Exp(B) 95% CI
Exp(B) 95% CI
2002–2004 TICS > 1SD
2004–2006 TICS > 1SD
Note. CVB: cerebrovascular burden. CESD: Center for Epidemiological Studies Depression Scale. TICS: Telephone Interview for Cognitive Status. TICS Drop
reflects incidence of decline on TICS score greater than 6 points between waves as indicated.
§P = .088,∗P < .05.+P < .01,ψP < .001, df = 1 for all comparisons.
aging theory, to greater longevity in this sample of older
women. In the present study, Rowe and Kahn’s formulation
of successful aging is expanded from quality of life to
length of life. Heightened depression and rapid loss of
cognition were significantly related to timing of death, and
conversely robust functioning was related to longevity. These
findings underscore the significance of behavioral factors to
the discussion of longevity. Behavioral factors such as mood
and gross cognitive functioning are basic characteristics
of the individual patient, and these findings highlight the
importance of subjective reports or clinician perceptions of
decline in these areas, especially when working with older
patients. The finding that CVB predicts longevity broadly
supports the large medical and epidemiological literature
citing conditions such as heart disease, hypertension, and
diabetes as risk factors for death (discussed in ). Our
finding that depressive symptoms predict longevity corrob-
orate significant past work on this subject [19–22].
These findings are also consistent with the concept of
terminal cognitive decline effect over longer periods of time
[24, 28, 29], these findings support other research [19, 20]
that terminal cognitive drop theory can be applied over
brief periods of time. Additionally, most research supporting
terminal cognitive decline relates mortality to impaired per-
formance on cognitive measures, suggesting decline from an
idiographic baseline and consequently discounting the time
period over which decline occurs. By contrast, the present
study contributes to the existing literature by testing this
theory among older old women using longitudinal data.
The primary limitation of the present study is that cog-
nitive and mood data are not available for many respon-
dents approaching death. This data is absent largely for
reasons of incapacity. Consequently, it is likely that the rela-
tionship between depressive symptoms, cognitive decline,
and longevity is underrepresented by the present research.
data. However, use of such data is common in population-
based samples and reasonable concordance values between
self-reports of disease and medical chart reviews have
been reported [44, 45]. Future research should build on
the present finding by identifying relationships between
6 Journal of Aging Research
markers of cerebrovascular health, depression, and cognitive
The development of this research was generously supported
by the Blue Cross Blue Shield of Michigan Foundation and
by the T32 grant-supported NIA/NIH Pre-Doctoral Training
Program in Aging and Urban Health at the Institute of
Gerontology. 2 T-32 AG00275-06.
 J. W. Rowe and R. L. Kahn, “Successful aging,” The Gerontolo-
gist, vol. 37, no. 4, pp. 433–440, 1997.
 N. R. Cook, M. S. Albert, L. F. Berkman, D. Blazer, J. O.
Taylor, and C. H. Hennekens, “Interrelationships of peak
elderly: MacArthur Foundation Studies of Aging,” Journals of
Gerontology—Series A, vol. 50, no. 6, pp. M317–M323, 1995.
 J. Chodosh, D. M. Kado, T. E. Seeman, and A. S. Karlamangla,
“Depressive symptoms as a predictor of cognitive decline:
of Geriatric Psychiatry, vol. 15, no. 5, pp. 406–415, 2007.
 T. L. Gruenewald, A. S. Karlamangla, G. A. Greendale, B. H.
Singer, and T. E. Seeman, “Feelings of usefulness to others,
disability and mortality in older adults: the MacArthur Study
no. 1, pp. P28–P37, 2007.
ful aging and well-being: self-rated compared with Rowe and
Kahn,” The Gerontologist, vol. 42, no. 6, pp. 727–733, 2002.
 M. B. Holstein and M. Minkler, “Self, society and the ‘New
Gerontology’,” The Gerontologist, vol. 43, no. 6, pp. 787–796,
 S. Arber and H. Cooper, “Gender differences in health in later
1, pp. 61–76, 1999.
 A. L. Rosso, C. B. Eaton, R. Wallace et al., “Combined impact
of geriatric syndromes and cardiometabolic diseases on mea-
sures of functional impairment,” Journals of Gerontology—
Series A, vol. 66, no. 3, pp. 349–354, 2011.
 M. L. Bruce, T. E. Seeman, S. S. Merrill, and D. G. Blazer, “The
impact of depressive symptomatology on physical disability:
of Public Health, vol. 84, no. 11, pp. 1796–1799, 1994.
culties and physical activity as predictors of mortality and loss
of independence in the community-living older population,”
Journal of the American Geriatrics Society, vol. 48, no. 5, pp.
burden and healthy brain aging,” Clinics in Geriatric Medicine,
vol. 26, no. 1, pp. 17–27, 2010.
 N. Raz, K. M. Rodrigue, and J. D. Acker, “Hypertension and
the brain: vulnerability of the prefrontal regions and executive
functions,” Behavioral Neuroscience, vol. 117, no. 6, pp. 1169–
P. Roose, “The vascular depression subtype: evidence of inter-
nal validity,” Biological Psychiatry, vol. 64, no. 6, pp. 491–497,
 C. E. Coffey, G. S. Figiel, W. T. Djang, and R. D. Weiner,
“Subcortical hyperintensity on magnetic resonance imaging:
a comparison of normal and depressed elderly subjects,” The
American Journal of Psychiatry, vol. 147, no. 2, pp. 187–189,
 A. H. Glassman and P. A. Shapiro, “Depression and the course
vol. 155, no. 1, pp. 4–11, 1998.
 M. J. Bos, T. Lind´ en, P. J. Koudstaal et al., “Depressive symp-
toms and risk of stroke: the Rotterdam Study,” Journal of
 S. L. Larson, P. L. Owens, D. Ford, and W. Eaton, “Depressive
disorder, dysthymia and risk of stroke: thirteen-year follow-
up from the Baltimore Epidemiologic Catchment Area Study,”
Stroke, vol. 32, no. 9, pp. 1979–1983, 2001.
 B. W. Penninx, J. M. Guralnik, M. Pahor et al., “Chronically
depressed mood and cancer risk in older persons,” Journal of
the National Cancer Institute, vol. 90, no. 24, pp. 1888–1893,
 C. L. Arfken, P. A. Lichtenberg, and M. E. Tancer, “Cognitive
impairment and depression predict mortality in medically ill
older adults,” Journals of Gerontology—Series A, vol. 54, no. 3,
pp. M152–M156, 1999.
 K. M. Mehta, K. Yaffe, K. M. Langa, L. Sands, M. A. Whooley,
and K. E. Covinsky, “Additive effects of cognitive function
and depressive symptoms on mortality in elderly community-
living adults,” Journals of Gerontology—Series A, vol. 58, no. 5,
pp. 461–467, 2003.
 K. E. Covinsky, E. Kahana, M. H. Chin, R. M. Palmer, R. H.
Fortinsky, and C. S. Landefeld, “Depressive symptoms and 3-
year mortality in older hospitalized medical patients,” Annals
of Internal Medicine, vol. 130, no. 7, pp. 563–569, 1999.
 B. W. Penninx, S. W. Geerlings, D. J. Deeg, J. T. van Eijk, W.
and the risk of death in older persons,” Archives of General
Psychiatry, vol. 56, no. 10, pp. 889–895, 1999.
 P. Missotten, G. Squelard, M. Ylieff et al., “Quality of life
in older Belgian people: comparison between people with
dementia, mild cognitive impairment and controls,” Interna-
tional Journal of Geriatric Psychiatry, vol. 23, no. 11, pp. 1103–
 K. F. Riegel and R. M. Riegel, “Development, drop and death,”
Developmental Psychology, vol. 6, no. 2, pp. 306–319, 1972.
 E. Palmore and W. Cleveland, “Aging, terminal decline and
terminal drop,” Journals of Gerontology, vol. 31, no. 1, pp. 76–
function: an updated review of longitudinal studies,” Experi-
mental Aging Research, vol. 28, no. 3, pp. 299–315, 2002.
state’: a practical method for grading the cognitive state of
patients for the clinician,” Journal of Psychiatric Research, vol.
12, no. 3, pp. 189–198, 1975.
 K. Yaffe, K. Lindquist, E. Vittinghoff et al., “The effect of
maintaining cognition on risk of sisability and death,” Journal
of the American Geriatrics Society, vol. 58, no. 5, pp. 889–894,
 R. S. Wilson, T. L. Beck, J. L. Bienias, and D. A. Bennett, “Ter-
years of life,” Psychosomatic Medicine, vol. 69, no. 2, pp. 131–
 K. M. Langa, E. B. Larson, J. H. Karlawish et al., “Trends in
the prevalence and mortality of cognitive impairment in the
Journal of Aging Research7 Download full-text
United States: is there evidence of a compression of cognitive
morbidity?” Alzheimer’s and Dementia, vol. 4, no. 2, pp. 134–
 B. T. Mast, B. Yochim, S. E. MacNeill, and P. A. Lichtenberg,
“Risk factors for geriatric depression: the importance of exec-
utive functioning within the vascular depression hypothesis,”
Journals of Gerontology—Series A, vol. 59, no. 12, pp. 1290–
 A. Bielak, D. Gerstorf, K. M. Kiely, K. J. Anstey, and M. Luszcz,
“Depressive symptoms predict decline in perceptual speed in
older adulthood,” Psychology and Aging. In press.
 S. G. Heeringa and J. Conner, “Technical description of the
Health and Retirement Study sample design,” HRS/AHEAD
Documentation Report DR-002, University of Michigan, Ann
Arbor, Mich, USA, 1995.
 L. Radloff, “The CES-D Scale: a self-report depression scale
for research in the general population,” Applied Psychological
Measurement, vol. 1, no. 3, pp. 385–401, 1977.
 D. E. Steffick, “Documentation of affective functioning mea-
sures in the Health and Retirement Study,” HRS Documenta-
tion Report DR-005, Survey Research Center at the Institute
for Social Research, Ann Arbor, Mich, USA, 2000.
 R. Wallace, A. R. Herzog, M. B. Ofstedal et al., “Documen-
tation of affective functioning measures in the Health and
Retirement Study,” Tech. Rep., Survey Research Center, Uni-
versity of Michigan, Ann Arbor, Mich, USA, 2000.
 A. T. F. Beekman, D. J. H. Deeg, J. Van Limbeek, A. W. Braam,
M. Z. De Vries, and W. Van Tilburg, “Criterion validity of the
Center for Epidemiologic Studies Depression scale (CES-D):
results from a community-based sample of older subjects in
the Netherlands,” Psychological Medicine, vol. 27, no. 1, pp.
 J. Brandt, M. Spencer, and M. Folstein, “The telephone inter-
view for cognitive status,” Neuropsychiatry, Neuropsychology
and Behavioral Neurology, vol. 1, no. 2, pp. 111–117, 1988.
 A. R. Herzog and R. B. Wallace, “Measures of cognitive func-
tioning in the AHEAD study,” Journals of Gerontology—Series
B, vol. 52, special issue, pp. 37–48, 1997.
 K. A. Welsh, J. C. S. Breitner, and K. M. Magruder-Habib,
“Detection of dementia in the elderly using telephone screen-
ing of cognitive status,” Neuropsychiatry, Neuropsychology and
Behavioral Neurology, vol. 6, no. 2, pp. 103–110, 1993.
 D. W. Desmond, T. K. Tatemichi, and L. Hanzawa, “The tele-
phone interview for cognitive status (TICS): reliability and
validity in a stroke sample,” International Journal of Geriatric
Psychiatry, vol. 9, no. 10, pp. 803–807, 1994.
 T.J¨ arvenp¨ a¨ a,J.O.Rinne,I.R¨ aih¨ aetal.,“Characteristicsoftwo
telephone screens for cognitive impairment,” Dementia and
Geriatric Cognitive Disorders, vol. 13, no. 3, pp. 149–155, 2002.
 C. L. Dahle, B. S. Jacobs, and N. Raz, “Aging, vascular risk
and cognition: blood glucose, pulse pressure and cognitive
performance in healthy adults,” Psychology and Aging, vol. 24,
no. 1, pp. 154–162, 2009.
 T. L. Bush, S. R. Miller, A. L. Golden, and W. E. Hale, “Self-
report and medical record report agreement of selected med-
ical conditions in the elderly,” The American Journal of Public
Health, vol. 79, no. 11, pp. 1554–1556, 1989.
 B. M. Psaty, L. H. Kuller, D. Bild et al., “Methods of assessing
prevalent cardiovascular disease in the cardiovascular health