DOI: 10.1111/ggi.13802

ORIGINAL ARTICLE
EPIDEMIOLOGY, CLINICAL PRACTICE AND HEALTH

Association of trajectories of cognitive function with causespeciﬁc mortality and medical and long-term care costs
Yu Taniguchi,1 Akihiko Kitamura,1 Tatsuro Ishizaki,2 Yoshinori Fujiwara,1 Tomohiro Shinozaki,3Satoshi Seino,1 Seigo Mitsutake,2 Hiroyuki Suzuki,1 Yuri Yokoyama,1 Takumi Abe,1 Tomoko Ikeuchi,1 Isao Yokota,4 Yutaka Matsuyama3 and Shoji Shinkai1
1Research Team for Social
Participation and Community
Health, Tokyo Metropolitan
Institute of Gerontology, Tokyo,
Japan
2
Research Team for Human Care,
Tokyo Metropolitan Institute ofGerontology, Tokyo, Japan3
Department of Biostatistics,
School of Public Health, The
University of Tokyo, Tokyo, Japan
4
Department of Biostatistics,
Graduate School of Medical
Science, Kyoto Prefectural
University of Medicine, Kyoto,
Japan
Correspondence
Yu Taniguchi PhD, Research
Team for Social Participation andCommunity Health, Tokyo
Metropolitan Institute ofGerontology, 35-2 Sakae-cho,Itabashi-ku, Tokyo 173-0015,
Japan.
Email: yu0717@tmig.or.jp

Aim: Cognitive decline increases mortality risk through dementia-related pathways andmight be associated with increased healthcare costs. Using up to 12 years of repeated measures data, we identiﬁed trajectories in cognitive function among community-dwelling older
Japanese adults. We then examined whether these trajectories were associated with all-cause
and cause-speciﬁc mortality, and differences in healthcare costs.
Methods: A total of 1736 adults aged ≥65 years who were free of disabling dementia completed annual assessments during 2002–2014. Cognitive function was assessed with the MiniMental State Examination. The average number of follow-up assessments was 3.9, and thetotal number of observations was 6824 during the follow-up period.
Results: We identiﬁed ﬁve trajectory patterns in cognitive function (high, second, third,
fourth, and low) during the 12-year follow-up period. The low (2.0%) and fourth (2.2%) trajectory groups had higher hazard ratios for cardiovascular disease mortality, and hazard ratios
for other cause mortality were signiﬁcantly higher for the third (16.8%) and second (38.8%)
trajectory groups than for the high trajectory group (40.3%). Until 5 years of follow up, participants in the two lower-trajectory groups had higher mean combined monthly medical andlong-term care costs. After 8 years of follow up, mean costs were highest for the third
trajectory.
Conclusions: The risk of death from cardiovascular disease was higher in the two lowertrajectory groups in cognitive function, and they showed higher healthcare costs during theﬁrst 5 years of follow up. After 8 years of follow up, the third trajectory had the highest
healthcare costs, perhaps because of hospitalizations attributable to gradual cognitive decline.
Geriatr Gerontol Int 2019; ••: ••–••.
Keywords: cognition, health service, successful aging.

Received: 8 January 2019
Revised: 19 September 2019
Accepted: 30 September 2019

Introduction
Many people experience age-related functional decline. Cognitive
function, too, typically deteriorates in later life.1 Memory performance at age 80 years is approximately 1–1.5 standard deviations
below that of young adults,2 whereas global cognition is relatively
stable, before declining moderately in the last 3–4 years of life.3
Using repeated measures analysis, we previously identiﬁed three distinct trajectory patterns in cognitive aging among communitydwelling older Japanese adults.4 Approximately half the participants
had normal cognitive aging, 43% began to decline after age 80 years
and the remaining 5% showed a rapid decline after age 65 years.
Cognitive impairment, might result from the process of cerebrovascular disease 5 (i.e. cerebral infarction and cerebral hemorrhage),© 2019 Japan Geriatrics Society

and is a risk factor for cerebrovascular dementia6 and prodromal
Alzheimer’s disease. Cognitive decline might increase the mortality
risk through dementia-related pathways, and several studies havereported an association between cognitive status and mortality.
Declines in memory and executive function were reported to be signiﬁcant predictors of mortality in a younger-old group, and decline
in processing was a strong mortality predictor in an older-old
group.7 The main causes of death among persons with low cognitive scores were cancer and cardiovascular diseases.8 In addition,
cognition was inversely associated with death from cancer, cardiovascular disease, death from other causes and respiratory illness.9
Previous ﬁndings suggest that trajectories of cognitive function
might predict mortality and cause-speciﬁc mortality; however, thepresent evidence is insufﬁcient to conﬁrm this hypothesis.7–9|

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Y Taniguchi et al.
The World Alzheimer Report estimated that the annual global
cost of dementia is $818 billion, 85% of which is related to family
and social burdens rather than to medical care.5–10 We hypothesized that declining cognitive trajectories might be associated withincreased healthcare costs, such as medical or long-term carecosts.
The present prospective study of community-dwelling older
adults used repeated measures data on cognitive function from a
13-year longitudinal study of rural Kusatsu Town, Japan. We
examined local registries to ascertain deaths from any cause andlinked these data with national vital statistics. Then, we obtained
data on medical expenses and long-term care expenditures during
the follow-up period. The three objectives were to identify trajectories in cognitive function among a general population ofcommunity-dwelling older Japanese adults, to determine whether
these trajectories were associated with all-cause and cause-speciﬁcmortality, and to examine differences in healthcare costs (medical
and long-term care costs) between trajectories. Furthermore, apreliminary analysis examined the association of hospitalization
with trajectory.

Methods
Participants
In collaboration with the government of Kusatsu Town, Gunma
Prefecture, Japan, we launched a longitudinal study of aging andhealth in 2001. In addition to an annual preventive healthcheckup for all residents aged ≥40 years, participants aged
≥70 years (≥65 years after 2006) underwent a geriatric assessment
from 2002 through 2014. All older residents were invited to participate in annual assessments, which were all carried out in thesame manner. The details of the study design have been previously reported.4,11–16 All participants undergoing geriatric assessment provided written informed consent under conditions
approved by the ethics committee at Tokyo Metropolitan Instituteof Gerontology.
To be eligible for the study among a general population ofcommunity-dwelling older Japanese adults, individuals had to be
free of disabling dementia at baseline, as assessed by the long-termcare insurance system.4,15 Brieﬂy, disabling dementia was deﬁned
as level II or higher dementia in an observer-based rating for older
adults with dementia, as this is the level at which applicants canreceive insurance beneﬁts, including institutional, home, respite
and day care, and equipment loans.17,18 The data source for thepresent study was 1736 adults aged ≥65 years living in Kusatsu
Town who completed annual assessments at least once during theperiod from June 2002 through July 2014. The average number offollow-up assessments was 3.9, and the total number of observations was 6824 during the follow-up period (Fig. S1).

Measurement of cognitive function
Cognitive function was assessed with the Mini-Mental State
Examination (MMSE), a widely used brief screening test for
dementia and a measure of global cognitive function. The MMSE
encompasses orientation, memory, concentration, language andpraxis. It comprises 11 questions, and the score ranges from 0 to
30, with lower scores indicating worse global cognitive ability.4
Well-trained personnel administered the annual examination. We
used MMSE data from annual assessments during the study
period from 2002 through 2014.

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Cause-speciﬁc mortality
The underlying cause of death was coded by using the International Classiﬁcation of Diseases, Tenth Edition (ICD-10). The relevant ICD-10 codes were I00–I99 for cardiovascular disease(CVD) and C00–C97 for cancer; all other codes were classiﬁed as“other.” Local registries recorded 401 (23.6%) incident deaths
among the 1736 participants during the period through December
2016. The median duration of follow up for incident death was
8.3 years. Among the 401 incident deaths, 391 (97.5%) werelinked with Japanese national vital statistics (121 CVD deaths,
100 cancer deaths and 170 other deaths).

Healthcare costs (medical and long-term care costs)
All Japanese citizens have access to medical care and long-termcare coverage under the universal health insurance system. The
ofﬁcial medical insurance system includes the National Health
Insurance and health insurance for older people, and these insurances cover almost all medical treatment and medical provider
fees. Payments from insured persons to medical providers are
made on a fee-for-service basis, in which the price of each service
is determined by a uniform national fee schedule.15,19,20 The Japanese Long-Term Care Insurance (LTCI) system provides longterm care services, community-based services and in-facility services. All primary insured persons aged ≥65 years are candidates
for care. When insured persons need to use LTCI, they submit arequest to the municipal government for their primary physicians
to assess, and evaluate their physical and mental status. Using
these results, a local Long-Term Care Needs Certiﬁcation Board
determines eligibility and the care level required for insured residents. Care services using allotted beneﬁts are coordinated by acare manager, in collaboration with the insured person and theirfamily.15,21
Using insurance claims data from the National Health Insurance, health insurance for older people and data on LTCI beneﬁciaries in Kusatsu Town, we calculated monthly medical costs,
and the combined monthly medical and long-term care costs.
Because the frequency of long-term care costs was low, we elected
not to analyze these costs separately. The period of these costs foreach participant corresponded with the follow-up period for
MMSE (from June 2002 through July 2014). Costs are expressed
in $US ($US1 = ¥111.02 on 11 July 2018). Other variables areshown in File S1.

Statistical analysis
First, we identiﬁed trajectory groups in cognitive function by estimating latent class group-based trajectory models for MMSE
score. The procedure was implemented in SAS Proc Traj (SASInstitute, Cary, NC, USA),22 as reported elsewhere.4,14,15,23,24 For
a ﬁxed number of latent groups, cubic trajectory models wereﬁtted, and posterior probabilities for each group membershipassignment were calculated for each individual. We chose a modelof ﬁve latent groups because of the interpretability of the trajectories and Bayesian information criterion of the models. After assigning the group with the highest posterior probability to eachparticipant, we compared cause-speciﬁc mortality (i.e. death fromcardiovascular disease, cancer or other causes) between the trajectory groups by using Fine and Gray subdistribution hazards
models that controlled for baseline sex, age, years of education,
frequency of going outdoors, self-rated health, body mass index,
grip strength, Tokyo Metropolitan Institute of Gerontology Index
of Competence score and Geriatric Depression Scale.25 The© 2019 Japan Geriatrics Society

Cognitive trajectories and mortality and healthcare costs
underlying time-scale in the hazards models starts from ﬁrst participation (from June 2002 through July 2014) until death or theend of follow up (December 2016), whichever came ﬁrst. Finally,
we ﬁtted the group-stratiﬁed Poisson generalized estimating equation (GEE) models for changes in healthcare costs and the groupstratiﬁed logistic GEE models for probability of hospitalization,
including quadratic or cubic terms for follow-up time and theﬁrst-order autoregressive correlation structure, after combining
the two smallest groups (the fourth and low trajectory groups) into
one group. Statistical analyses were carried out with SPSS (version
23.0; IBM Corporation, Armonk, NY, USA) and SAS (version 9.4;
SAS Institute).

Results
Data from the baseline survey showed that the mean age of participants was 71.5 years (SD 5.9 years), and 56.6% were women.
The average number of years of education was 10.0
(SD 2.7 years), and 82.9% of participants reported a self-rated
health of good or very good.
We identiﬁed ﬁve trajectory patterns in cognitive function:
40.3% of participants were in the high trajectory group, 38.8% in
the second group, 16.8% in the third group, 2.2% in the fourthgroup and 2.0% were in the low trajectory group (Fig. 1). The twohigher-trajectory groups (high and second groups) had consistently higher scores during the entire follow-up period. Meanscores in the third group remained relatively constant during theﬁrst few years, gradually decreased after 3 years and had an
MMSE score <23 points within 10 years. The two lower-trajectorygroups (fourth and low trajectory groups) had an MMSE score<23 points at baseline and showed gradual declines during thefollow-up period. The average posterior probability of allocating each participant into the four groups was 0.65–0.95, indicating a good ﬁt of the model of group trajectories to individual
trajectories.
Table 1 shows the baseline demographic and health characteristics of the participants in relation to cognitive trajectory. At baseline, participants in the high trajectory group were younger, hadmore years of education, were likely to have hyperlipidemia, went
outdoors more frequently, and had greater grip strength, higher

Tokyo Metropolitan Institute of Gerontology Index of Competence scores, higher MMSE scores and lower Geriatric Depression
Scale Short Form scores.
As compared with the high trajectory group, the second,
third, fourth and low trajectory groups had hazard ratios(HR) of 1.31 (95% conﬁdence interval [CI] 0.99–1.74), 1.38
(95% CI 0.99–1.92), 1.10 (95% CI 0.55–2.20) and 2.01 (95%
CI 1.04–3.88), respectively, for all-cause mortality, after adjustment for several covariates (Table 2). For cause-speciﬁc death,the low trajectory group had a signiﬁcantly higher HR for CVD
mortality. CVD mortality risk was slightly higher for the fourth
trajectory group than for the high trajectory group, but the difference was not signiﬁcant. The HR for other cause mortality(non-CVD death and non-cancer death) were signiﬁcantly
higher for the second and third trajectory groups than for thehigh trajectory group.
Among the 1736 participants, 1095 (63.1%) incurred monthly
medical costs and 89 (5.1%) incurred monthly long-term carecosts during follow up. The GEE models showed trajectoryspeciﬁc changes in monthly medical costs, and combined medical
and long-term care costs (Figs 2, 3). Mean monthly medical costsin the fourth and low trajectory groups were higher than in thethree higher-trajectory groups until 5 years of follow up, but
decreased linearly thereafter. In contrast, the three highertrajectory groups showed increases in monthly medical costs after
the baseline survey; in particular, the third trajectory group had adramatic increase during the second half of follow up, and mean
monthly medical costs were estimated at $619.6 after 12 years offollow up. Combined monthly medical and long-term care costs
were slightly higher than monthly medical costs alone. Interestingly, mean costs were highest in the third trajectory group, which
had the highest monthly medical and long-term care costs after
8 years of follow up (the combined costs were estimated at $884.8
after 12 years of follow up).
Fitted logistic GEE models for the probability of hospitalizationin the ﬁve trajectory groups showed several distinct patterns(Fig. S2). The probability during follow up was relatively constantfor the two higher-trajectory groups, but gradually decreased forthe two lower-trajectory group. In contrast, the probability rapidly
increased in the third trajectory group during the last 4 years offollow up.

Figure 1 Trajectory patterns
for cognitive function. The solid),lines are estimated values. ()
High group (40.3%); (Second group (38.8%); ())
Third group (16.8%); () Low
Fourth group (2.2%); (group (2.0%). MMSE, MiniMental State Examination.
© 2019 Japan Geriatrics Society|

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Y Taniguchi et al.
Table 1 Associations of baseline demographic and health characteristics with Mini-Mental State Examination trajectory groups incommunity-dwelling older adults
Variable

MMSE trajectory
High
SecondThird
FourthLow
P-value(n = 700, 40.3%) (n = 673, 38.8%) (n = 291, 16.8%) (n = 38, 2.2%) (n = 34, 2.0%)

Sex, women (%)
Age (years)
Years of education
Frequency of going outdoors,at least once a day (%)
Self-rated health, very good or good (%)
Chronic disease (%)
Hypertension
Hyperlipidemia
Stroke (cerebral hemorrhage,
subarachnoid hemorrhage)
Heart disease (angina, myocardial
infarction, arrhythmia e.g.
ventricular
extrasystole, others)
Diabetes mellitus
BMI (kg/m2)
Handgrip strength (kg)Instrumental Activities of daily living(score)Effectance (score)Social role (score)
TMIG-IC (score)
GDS Short Form (score)
MMSE (score)

59.7
69.6  4.5
11.0  2.7
85.6

52.5
71.4  5.6
9.7  2.5
84.2

56.4
74.4  6.8
8.6  2.3
76.4

63.2
75.9  7.1
8.6  3.1
78.9

70.6
80.0  7.8
7.0  2.4
62.5

0.864
<0.001<0.001
0.036

84.3

83.1

79.7

76.3

84.4

0.384

34.4
20.1
4.2

36.3
17.5
6.8

44.6
14.1
8.3

39.5
15.8
7.9

31.3
3.1
9.1

0.712
0.002
0.110

9.7

10.5

8.7

13.2

6.1

0.196

8.5
23.2  3.1
27.4  8.8
4.9  0.4

10.1
23.4  3.1
26.3  9.1
4.9  0.6

9.7
23.2  3.5
23.7  9.3
4.7  0.9

10.5
22.6  2.8
21.6  10.1
4.7  0.7

15.6
21.9  3.5
16.8  5.6
4.0  1.5

0.182
0.787
<0.001<0.001

3.7  0.7
3.6  1.8
12.2  1.3
3.1  2.8
29.1  1.3

3.5  0.8
3.5  0.9
11.8  1.7
3.7  2.9
26.9  1.7

3.1  1.0
3.3  1.0
11.1  2.2
4.0  3.0
24.4  2.1

3.0  1.1
2.8  1.2
10.5  2.3
5.8  3.8
23.2  2.4

2.0  1.4
2.2  1.4
8.1  3.3
6.0  2.9
15.2  3.5<0.001<0.001<0.001<0.001<0.001

Data presented as the mean  SD. P-values were calculated based on the cumulative logit model for Mini-Mental State Examination (MMSE) trajectory groups as an outcome after adjusting for baseline age and sex. BMI, body mass index; GDS, Geriatric Depression Scale, TMIG-IC, Tokyo
Metropolitan Institute of Gerontology Index of Competence.

Discussion
Traditionally, an MMSE cut-off of 23 out of 24 has been used toscreen persons with suspected cognitive impairment ordementia,26 and MMSE scores <20 have been used to identify
psychosis of dementia. In the present 12-year longitudinal studyof 1736 older adults without disabling dementia at baseline,
approximately 80% were classiﬁed in the two higher-trajectorygroups (40.3% in the high trajectory and 38.8% in the second trajectory), and they retained higher cognitive function during theentire follow-up period. The cognitive function of the 16.8% ofparticipants in the third trajectory group remained relatively constant for the ﬁrst few years, gradually decreased after 3 years andwas consistent with dementia within 10 years of follow up. Boththe fourth trajectory (2.2%) and low trajectory (2.0%) groups had
cognitive impairment at baseline, and would likely have developed
severe dementia had follow up been longer. The participants inthe two lower-trajectory groups had lower physical, psychologicaland social function, fewer years of education, and were older.
These ﬁndings suggest that efforts to address cognition-associatedcontrollable factors (i.e. physical, psychological and social function) might improve individual cognitive trajectories.
Cognitive trajectories were associated with mortality and conﬁrmed the putative relationship between cognition and mortality.
As compared with the high trajectory group, participants in the
low trajectory group had an adjusted HR of 2.01 for all-cause

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mortality and a signiﬁcantly higher HR for CVD mortality; thefourth trajectory group also had a slightly higher HR, but the difference was non-signiﬁcant. We carried out a preliminary analysis
for cerebrovascular disease mortality (ICD-10 codes, I60–I69).
Competing-risk subdistribution regression showed HR of 4.97
(95% CI 1.42–17.50) in the low trajectory group and 5.51 (95%
CI 1.89–16.10) in the fourth trajectory group, as compared withthe high trajectory group. These ﬁndings show that older persons
with severe dementia are at higher risk for death from cardiovascular diseases, such as cerebrovascular disease. In contrast, as
compared with the high trajectory group, participants in the thirdand second trajectory groups had HR of 2.29 and 1.80 for othercause mortality, respectively. In a preliminary analysis of respiratory mortality (ICD-10 codes J00–J99), the HR was 5.25 (95% CI
2.34–11.77) for the third trajectory group and 2.49 (95% CI
1.12–5.56) for the second trajectory group, as compared with thehigh trajectory group. These results suggest that older persons
with gradual cognitive decline have a higher risk of non-cancer
death, such as respiratory disease.
We hypothesized that lower cognitive trajectory might be associated with increased healthcare costs. In fact, although the mean
monthly medical and long-term care costs were higher in the twolower-trajectory groups during the ﬁrst 5 years of follow up, they
decreased linearly thereafter. The period of decreasing healthcare
costs in these two groups after 5 years of follow up overlapped theperiod when their MMSE scores were <20. Speciﬁcally, the present ﬁndings suggest two potential mechanisms by which© 2019 Japan Geriatrics Society

5.32 (3.25–8.70)***P < 0.05. **P < 0.01. Number of all-cause deaths/number of each trajectory group. Cox’s proportional hazards model was run for all-cause mortality. For the cause-speciﬁc outcomes (cardiovasculardisease [CVD], cancer and other cause of death), Fine–Gray subdistribution hazard models were ﬁt to account for competing events. Adjusted for sex, age, years of education, frequency of going outdoors, self-rated health, history of hyperlipidemia, body mass index, grip strength, Tokyo Metropolitan Institute of Gerontology Index of Competence and Geriatric Depression Scale. §Reference group;CI, conﬁdence interval; HR, hazard ratio; MMSE, Mini-Mental State Examination.

0.75 (0.16–3.46)

0.38 (0.05–2.89)
3.09 (1.13–8.45)*
0.51 (0.06–4.60)
0.59 (0.08–4.45)
5.10 (2.18–12.44)**
8.81 (4.51–17.21)**

2.29 (1.36–3.87)*

Low (n = 19/34, 55.9%)

2.01 (1.04–3.88)*

2.04 (0.71–5.87)

3.53 (2.30–5.87)**
0.69 (0.35–1.38)-

0.96 (0.53–1.75)
1.08 (0.59–2.00)

2.31 (0.97–5.50)
3.01 (1.31–6.91)**

1.79 (1.10–2.89)*
1.38 (0.99–1.92)

1.10 (0.55–2.20)

2.23 (1.69–2.93)**

1.89 (1.04–23.45)*

Third (n = 109/291, 37.5%)

Fourth (n = 12/38, 31.6%)

1(95% CI)

Adjusted HR

1.80 (1.10–2.95)*
1.94 (1.13–8.45)*
1.20 (0.73–1.95)
1.47 (0.94–2.29)
0.84 (0.50–1.43)
1.00 (0.63–1.59)
1.31 (0.99–1.74)
1.53 (1.19–1.97)**1
1
1High§ (n = 97/700, 13.9%)(95% CI)(95% CI)(95% CI)

Crude HR
Adjusted HR
Crude HRMMSE trajectory

Second (n = 164/673, 24.4%)

1
1
1
1

(95% CI)

Crude HR
Adjusted HR(95% CI)(95% CI)

Crude HR
Adjusted HR(95% CI)

Other mortality
Cancer mortality
CVD mortality
All-cause mortality

Table 2 Independent associations of Mini-Mental State Examination trajectory groups with all-cause and cause-speciﬁc mortality in community-dwelling older adults

Cognitive trajectories and mortality and healthcare costs© 2019 Japan Geriatrics Society

healthcare costs decrease for older persons with progressive
dementia. First, older persons with progressive dementia might
have difﬁculty reporting their subjective symptoms, which could
lead to undertreatment or inappropriate treatment. The probability of hospitalization linearly decreased during follow up in the
two lower-trajectory groups, which suggests that reporting subjective symptoms is difﬁcult for older persons with progressive
dementia. Second, physicians might alter treatment plans inaccordance with patients’ cognitive function. For example, theInternational Diabetes Federation and Japan Diabetes Society recommend less rigorous glycated hemoglobin limits for older adults
with cognitive impairment, and the goals of glycemic control are
adjusted in relation to the cognitive status of older patients. To
better address the healthcare needs of patients in the 21st century,
medical care might evolve to a more individualized and integrated
model.27 The present third trajectory group had the highestmonthly medical and long-term care costs after 8 years of follow
up. Sharma et al. reported that one-third of people with dementia
had falls leading to hospitalization, necessitating action in clinicalpractice.28 This suggests that the high healthcare costs of the third
trajectory group were caused by fall-related hospitalizations attributable to gradual cognitive decline. A USA study reported that
healthcare expenditures were substantially higher for persons withdementia than for those with other diseases.29 Attempts to prevent
dementia and reduce social security costs for persons at high riskof dementia should target people with gradual cognitive decline,
especially those with an MMSE score <23 points.
The present study had strengths that warrant mention. First,
our large sample and repeated measures data from a general population of community-dwelling older adults allowed us to use agroup-based semiparametric mixture model, which yielded potential trajectories in cognitive function during follow up. Second,
data on medical and long-term care costs were derived from theJapanese National Health Insurance, health insurance for older
people and the LTCI database. Because Japan has a universal
health insurance system, we were able to link age trajectories incognitive function, cause of death, and medical and long-termcare costs. The present ﬁndings might aid policies for health promotion and preventive care initiatives in the many countries withgrowing social security cost burdens.
The present study also had limitations. First, the Japanese
health insurance system is completely different from the Medicare
and Medicaid programs in the USA, and programs in other Western countries.30 Thus, the estimated healthcare costs in the present study might differ from those in other countries. However,
because older persons such as those in the present study population are the people who participate in community-based healthcheckups and seek care in clinical settings, the present trajectoryspeciﬁc changes in healthcare costs will likely be applicable tothem. Second, the data source for the present study was limited to
one rural town and participants in the annual preventive healthcheckup. To conﬁrm the present ﬁndings, data from other study
sites or nationally representative data for Japan are required.
Third, we calculated monthly medical costs and combinedmonthly medical and long-term care costs. Although the detailed
costs of medical expenses, such as drug prescriptions and underlying disease, might be important in understanding the reasons forthe high costs in the third trajectory group, these data were notavailable for analysis. Future studies should examine the reasonsfor high medical and long-term care costs.
In conclusion, the present prospective study identiﬁed ﬁve
major trajectory patterns in cognitive function among a general
population of community-dwelling older adults. The two lowertrajectory groups had higher risks for death from CVD, as|5

Y Taniguchi et al.

Figure 2 Cognitive function
trajectory-speciﬁc trends inmonthly medical costs. The solid),lines are estimated values. () Second group;
High group; () Third group; () Fourth &(low group.

Figure 3 Cognitive function
trajectory-speciﬁc trends inmonthly medical and long-termcare costs. The solid lines are), Highestimated values. (group; () Second group; ()) Fourth &Third group; (low group.

compared with the high trajectory group, and the participants inthe third trajectory group had a higher risk for non-cancer death.
The mean monthly healthcare costs were higher in the two lowertrajectory groups during the ﬁrst 5 years of follow up. The third
trajectory group had the highest healthcare costs after 8 years offollow up, perhaps because of hospitalizations attributable to gradual cognitive decline.

Activity Start-up JP24890302, and Grants-In-Aid for Young Scientists (B) JP15K16539 and 18 K17409, from the Ministry of Education, Culture, Sports, Science and Technology, Japan.

Acknowledgements

References

We thank the staff members of the public health center and town
ofﬁce of Kusatsu, and our colleagues on the Research Team forSocial Participation and Community Health, Tokyo Metropolitan
Institute of Gerontology. This study was supported by Tokyo
Metropolitan Institute of Gerontology, JST/RISTEX, Grants-InAid for Scientiﬁc Research (B) JP20390190, (B) JP21390212,(B) JP24390173 and (B) JP26310111, a Grant-In-Aid for Research

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Disclosure statement
The authors declare no conﬂict of interest.

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© 2019 Japan Geriatrics Society

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Supporting information
Additional supporting information may be found in the online
version of this article at the publisher’s website:
Figure S1 Study ﬂow to be eligible for the study
Figure S2 Cognitive function trajectory-speciﬁc probability ofoccurrence for hospitalization. The solid lines are estimatedvalues.
File S1 Other variables in the study.

How to cite this article: Taniguchi Y, Kitamura A,
Ishizaki T, et al. Association of trajectories of cognitive
function with cause-speciﬁc mortality and medical andlong-term care costs. Geriatr. Gerontol. Int. 2019;1–7.
https://doi.org/10.1111/ggi.13802|

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