Articles

Projecting prevalence of frailty and dementia and theeconomic cost of care in Japan from 2016 to 2043:
a microsimulation modelling study
Megumi Kasajima, Karen Eggleston, Shoki Kusaka, Hiroki Matsui, Tomoki Tanaka, Bo-Kyung Son, Katsuya Iijima, Kazuo Goda,
Masaru Kitsuregawa, Jay Bhattacharya, Hideki Hashimoto

Summary

Background Dementia and frailty often accompany one another in older age, requiring complex care and resources.
Available projections provide little information on their joint impact on future health-care need from different
segments of society and the associated costs. Using a newly developed microsimulation model, we forecast thissituation in Japan as its population ages and decreases in size.
Methods In this microsimulation modelling study, we built a model that simulates an individual’s status transition
across 11 chronic diseases (including diabetes, coronary heart disease, and stroke) as well as depression, functional
status, and self-reported health, by age, sex, and educational strata (less than high school, high school, and college andhigher), on the basis of nationally representative health surveys and existing cohort studies. Using the simulation
results, we projected the prevalence of dementia and frailty, life expectancy with these conditions, and the economic
cost for formal and informal care over the period 2016–43 in the population of Japan aged 60 years and older.
Findings Between 2016 and 2043, life expectancy at age 65 years will increase from 23·7 years to 24·9 years in womenand from 18·7 years to 19·9 years in men. Years spent with dementia will decrease from 4·7 to 3·9 years in womenand 2·2 to 1·4 years in men. By contrast, years spent with frailty will increase from 3·7 to 4·0 years for women and
1·9 to 2·1 for men, and across all educational groups. By 2043, approximately 29% of women aged 75 years and older
with a less than high school education are estimated to have both dementia and frailty, and so will require complex
care. The expected need for health care and formal long-term care is anticipated to reach costs of US$125 billion for
dementia and $97 billion for frailty per annum in 2043 for the country.
Interpretation Japan’s Government and policy makers should consider the potential social challenges in caring for asizable population of older people with frailty and dementia, and a widening disparity in the burden of thoseconditions by sex and by educational status. The future burden of dementia and frailty should be countered not only
by curative and preventive technology innovation, but also by social policies to mitigate the health gap.
Funding Japan Society for the Promotion of Science, Hitachi – the University of Tokyo Laboratory for a sustainable
society, and the National Institute of Ageing.

Lancet Public Health 2022;
7: e458–68
School of Public Health(M Kasajima MS, H Matsui MPH,
Prof H Hashimoto DPH),
Graduate School of Economics(S Kusaka MEcon), Institute ofGerontology (T Tanaka PhD,
B-K Son PhD, Prof K Iijima MD),
Institute for Future Initiatives(B-K Son, Prof K Iijima), andInstitute of Industrial Science(K Goda PhD,Prof M Kitsuregawa PhD)
University of Tokyo, Tokyo,
Japan; Walter H Shorenstein
Asia-Pacific Research Center,
Freeman Spogli Institute forInternational Studies,
Stanford University, CA, USA(K Eggleston PhD); Center forPrimary Care and Outcomes
Research, Stanford School of
Medicine, CA, USA(Prof J Bhattacharya MD)
Correspondence to:Prof Hideki Hashimoto,Department of Health and Social
Behavior, School of Public
Health, University of Tokyo,
Tokyo 113-0033, Japanhidehashim@m.u-tokyo.ac.jp

Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.

Introduction
Population ageing brings with it the challenges of agerelated conditions, such as dementia and physical frailty,
for which spending on health care and caregiving is
expected to increase. In 2016, approximately 44 million
people worldwide had dementia, almost double thenumber in 1990.1 The prevalence of frailty is rapidly
increasing globally along with population ageing and will
increase health-care demand.2
Among member countries of the Organisation for
Economic Co-operation and Development, Japan standson the front-line of population ageing. As of 2020, theJapanese population aged 65 years and older was morethan 36·0 million (approximately 29·2% of the popu­
lation).3 An estimated 3·5 million people had dementia(approximately 8% of the global burden3) and an estimated
3 million people had frailty in 2012.4
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Japan has had universal public insurance coverage forhealth care that allows affordable access to outpatient,
inpatient, and pharmaceutical care since 1961.5 In 2000,
the Japanese Government launched a public, mandatory
long-term care insurance (LTCI) scheme to support
formal provision of personal and social care by trained
support workers (hereafter referred to as formal care).
Long-term care might be provided in care homes andchronic-care hospitals, in community centres, or athome.6 After the scheme’s launch, the number of total
long-term care beneficiaries and associated expenditure
increased substantially, despite cost control policies,
from US$33 billion in 2000 to $94 billion in 2018.7
What will future demand for health care and long-termcare be in Japan? The burden of ageing-related conditions
is expected to increase in Japan in the near future, andalso in other countries in Asia and in several othere458

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Research in context
Evidence before this study
We searched PubMed and Google on January 1–15, 2022,
for academic papers and institutional reports published in
English since database inception that forecast future disabilities
related to cognitive impairment and physical frailty and theireconomic impact. Publications in Japanese were additionallysearched. The search strategy included the terms “dementia”,“frailty”, “prevalence”, “cost of illness”, “burden of disease”,“forecasting”, and “computer simulation”. We identified
ten simulation models developed for forecasting prevalence of
dementia in England, the USA, Canada, Australia, Spain, Ireland,
and globally. We did not find any studies explicitly including
frailty, although two models considered the status of
dependency or difficulties engaging in activities of daily life.
Three models assessed the population impact of dementia on
disability-free life expectancy, and five models assessed theestimated health-care cost. Three models involved
microsimulations that considered multi-morbidity conditionsof individual older people in the forecasting estimation,
three models incorporated cardiovascular risk factors,
and three models adopted state transition for the natural courseof dementia progression. A study based on the Global Burden of
Diseases, Injuries, and Risk Factors Study estimated the trend ofprevalence of dementia regressed on the summary score of risk
factors and educational attainment trend at the country level.

See Online for appendix

regions of the world where population ageing outpaces
the rate seen in Japan.8
Use of microsimulation models allows future pro­jection of population health by accounting forintertwined associations between health, demographics,
and risk factors on an individual basis.9,10 However,existing microsimulation models have not articulated
the joint evolution of frailty and dementia, which are
two major challenges to the health-care system in anageing population. Both are strongly associated witheach other, alongside other comorbid conditions typicallyobserved among older people.11 We aimed to fill thisknowledge gap and estimate the economic cost of healthcare and long-term care services related to dementia andfrailty, and disparities in these conditions, which canhelp inform health-care and social policy measures tobetter meet the challenge of population ageing.

Methods

Study design and construction of a multi-statetransition model for multi-comorbidity
In this microsimulation modelling study, we used a
recently developed microsimulation model12 with fieldbased measure­ment of cognitive function and frailty
status to deter­mine the association between frailty anddementia in older people (aged ≥60 years) in Japan.
Individual-based microsimulation models have beenused to forecast the multi-comorbidity status of older
e459

Added value of this study
To our knowledge, this is the first microsimulation modellingstudy to simultaneously consider the population impacts offrailty and dementia in an ageing population. We projectedthat in the next two decades, when the older population in
Japan will be at its largest, increased life expectancy for people
aged 65 years and over will feature more years free of
dementia, but fewer years without frailty than in 2016.
A larger proportion of women were affected by bothconditions. We predict that the prevalence of dementia will begreater in those with lower compared to higher educational
attainment, and that this disparity will grow over time.
The expected cost of health care and formal long-term care forthose with dementia is expected to remain similar to that in2016, whereas the cost associated with frailty care is expected
to increase substantially.
Implications of all the available evidence
Our results highlight a double burden of dementia and frailty
among women aged 75 years and older and those with lower
education, who will require complex care services at a higher
cost per capita. Sex and educational disparities in comorbid
dementia and frailty among the older population will beespecially important to consider in the public health policy
agenda for population ageing.

people in England (aged ≥65 years) and the USA (aged≥50 years), using panel data from nationwide samples ofolder people (appendix pp 5–7).­9,10 We searched PubMed
and Google Scholar between Jan 1 and Jan 15, 2022 forpublications since database inception in English and inJapanese that forecast future disabilities related tocognitive impairment and physical frailty and theireconomic impact. Given the paucity of suitable panel
data for the older old (aged ≥75 years) age strata inJapan,13 we used repeated nationwide cross-sectional
surveys and death records to construct a simulated
cohort: a multistate transition probabilities model of the
population aged 60 years and older.12 The model originallyused age, sex, the incidence of 11 chronic diseases(diabetes, coronary heart disease, stroke, hypertension,
hyperlipidaemia, cancer of all types, respiratory diseases[including chronic obstructive pul­monary disease], joint
conditions, eye problems, chronic kidney disease, andother diagnoses), two indicators of functional status(activities of daily living and instrumental activities ofdaily living), depression, and self-reported health. We
further incorporated an education-strata-specific statetran­
sition based on education-stratified cause-specificfatalities, by using a census-death record data linkage;
this is the model we used in these analyses.14
The current revised model starts with a baseline
population aged 60 years and older and their healthconditions as of June, 2016, evolving over a half-year cycle
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in a first-order Markov process. We used conditionspecific incidence rates and case fatalities estimated forthe most recent year as of 2015 for future projections.
Younger cohorts (those entering the age 60–62 years
strata at the time of each estimation between 2016 and2043) were stochastically prepared on the basis of theeducation-stratified population (ie, less than high school
education, high school education, and college educationand higher) as of the 2010 census, processed with exitfrom the cohort due to death at the estimated age-sexeducation-specific death rate until they matured to age
60 years to enter the model. Details of the model and data
sources are in the appendix (pp 8–12).

Identification of dementia and frailty status
The identification of dementia status by self-reports ofphysician diagnoses could lead to underestimation.
Hence, previous microsimulation studies have reliedon cognitive function measurements to estimate theprevalence of dementia.15 Similarly, we derived cognitive
function measurements from the Japanese Study ofAging and Retirement (JSTAR), a sister survey to theEnglish Longitudinal Study of Ageing and Health andRetirement Study.16
We measured amnestic cognitive impairment con­ditions on the basis of failed performance on standard
cognitive tests given to JSTAR participants (namely,
immediate and delayed word recall and serial-sevens
examinations) and the reporting of difficulty performingat least one of seven instrumental activities of daily living(ie, using transportation, grocery shopping, preparing
hot meals, paying bills, making deposits and withdrawals
at the bank or using an automated teller machine (known
as an ATM), using telephones, and taking medication).
These items are commonly used in existing algorithms
to assess dementia status using social survey data, suchas the Health and Retirement Study.10,15
Using a multivariate probit model (appendix p 14), we
estimated the likelihood of failed test performance
regressed on age, sex, educational attainment, and multicomorbidity conditions to predict dementia status foreach individual in the population. We set the probability
thresholds to match the prevalence of dementia and mild
cognitive impairment previously reported in Japanese
epidemio­logical surveys.17
There is no consensus on the standardised tools toassess frailty, which complicates status identification.2
We relied on data from the Kashiwa study,18 a populationbased community survey that measured the frailty statusof 1952 people aged 65 years and older in Kashiwa City, asuburb of Tokyo. This study used a modification of the
Cardiovascular Health Study criteria—the most widelyused method for frailty assessment.19,20 We constructed alogistic regression model of frailty as a function ofage, sex, educational attainment, comorbid conditions(diabetes, heart disease, stroke, hypertension, hyper­
lipidaemia, cancer, and chronic renal failure), depression,www.thelancet.com/public-health Vol 7 May 2022

and impaired mobility in daily living, similar to previous
studies on frailty risk assessment.21 We extrapolated theprobability of frailty in the simulated individuals with athreshold to match the real-world prevalence reported
in a previous Japanese epidemio­
logical survey.4 The
methodological details, regression results, and estimatedage-specific and sex-specific prevalence of dementia andfrailty are in the appendix (pp 13–16, 26–27).

Estimation of economic cost for health care andlong-term care
On the basis of projections of the future prevalence ofmulti-morbidity, we estimated the economic cost ofhealth care and formal long-term care. We defined formalcare as being provided by paid professionals, andinformal care as being provided by mainly unpaid nonprofessionals (eg, family members). We relied onelectronic claim data from the National Health Insuranceand LTCI databases, which contain monthly information
on types of services used and volume of use.5,21
Japanese public health-care insurance universallycovers inpatient, outpatient, and pharmaceutical services
for acute and chronic conditions.5 Public LTCI covers
nearly 90% of formal long-term care in Japan, including
for home-based and community-based care and care incare homes and chronic-care hospital beds.6 Taking
advantage of a unique reimbursement system by which
fee-for-service payments are strictly based on an item-byitem price list set by the government, we applied thereference price list to administrative data to generate an
estimate of the cost of health care and formal long-termcare, regardless of the actual use of services.
Using nationwide administrative data on health care,
we regressed monthly use by service type (inpatient vsoutpatient and prescription) on age, sex, the 11 prespecified
comorbidities, and the number of comorbid conditions.
Then, we extrapolated the expected annual use for eachindividual in the simulated cohort population. Japan’s
LTCI defines seven eligibility criteria levels according to
the severity of care needs. We categorised the lower
three levels as mild dependency (eg, needing to beaccompanied on outings), and the higher four levels as
high dependency (requiring support in activities of daily
life—eg, bathing, toileting, or eating meals). We estimatedmean monthly use by age-specific and sex-specificdependency strata and by service type (home-based andcommunity-based care vs care in care homes) using
nationwide LTCI administrative data, then extra­polated
these estimates to annual use for each individual in thesimulated cohort population (appendix pp 19–25).
Forecasting the economic costs of informal careprovision (ie, by non-paid non-professional caregivers) isvery complex because the intensity of care provision andits hourly unit price differ according to recipients’ needlevels and the caregiver’s age, gender, occupational
status, and relationship to the recipient (eg, spouse,child, other family member, or a friend).22 By 2040, ae460

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predicted 40% of households in Japan will have a single
occupant, and 44% of households will be headed bysomeone aged 65 years or older, and most often an older
couple.23 Given the uncertainty around who will provide
informal care in the future, we provide estimates byassuming that the resource use pattern between formal
and informal care will remain constant over time, with asimple sensitivity analysis. Existing literature on informal
long-term care indicates that, in addition to the formal
services covered by LTCI, family caregivers devote an
average of 25 h per week to informal care for highly
dependent people, and approximately 10 h per week forpeople with mild dependency.24,25 We multiplied thesenumbers by the hourly wage rate of $11 (the average forformal caregivers in 2013) and by the size of the
population projected to require home-based care.

Validation of estimated prevalence and cost of care
We did validation checks against external data for
estimated prevalence and use costs. We also validated
our population projections using official government
forecasts.26 We checked health transition probabilities via
backward validation, by comparing projected prevalence
as of 2016 using a 2010 baseline, with actual prevalence
numbers observed in the 2016 national health survey.
Projections of educational attainment were validated bycomparing with Barro-Lee data.27 We used data reported
by Toshiharu Ninomiya28 and Takashi Asada17 for the agespecific and sex-specific prevalence of dementia, and of
Murayama et al4 for those of frailty. We compared an
estimated life expectancy at age 65 years as of 2016 withlife expectancy using an abridged life table published by
the Japanese Ministry of Health, Labour and Welfare.29
For formal care use, we validated the estimated resultsfor 2016 with annual use for the same year as publicly
announced by the relevant government agency (appendix
pp 21, 25).

Model outputs
The nationally representative survey we analysed—
Comprehensive Survey of People’s Living Conditions—isundertaken every 3 years (appendix p 8); therefore, we
adopted a transition interval of 3 years for our model. We
present the prevalence of dementia and frailty for 2016,
2025, 2034, and 2043 (9 year intervals). We present
estimated life expectancy free of dementia or frailty atage 65 years using the Sullivan method in thecorresponding years.30 We also prepared the estimation
by educational attain­ment strata and by sex, to assess
socioeconomic disparity in the effect of dementia andfrailty. We stochastically prepared the baseline population
at age 60 years, and obtained the Monte Carlo error and5th to 95th percentile ranges as uncertainty intervals byimplementing 50 iterations of bootstrap simulation.9
We used Python (version 3.7.7) for parameterisationof multi-state transition probabilities and Stata SE(version 14.0) for regression analyses and simulations.
e461

Role of the funding source
The funders of the study had no role in study design,
data collection, data analysis, data interpretation, or
writing of the report.

Results
The estimated age-specific prevalence of dementia is set
to decrease between 2016 and 2043 for women younger
than 95 years and for men younger than 100 years(figure 1A). For 2043 versus 2016, the prevalence peak ofmild cognitive impairment is shifted into older age
groups (figure 1B), indicating that in 2043 older people
will have more years with cognitively normal functions.
By contrast, the prevalence of frailty in this population isestimated to increase between 2016 and 2043, specificallyamong those aged 90 years and older of both sexes,
reflecting improved longevity and the age-dependency of
the incidence of frailty (figure 1C).
We estimated the baseline prevalence of dementia
among people aged 60 years and older to be approximately
5·10 million in 2016 (3·53 million women and
1·57 million men), and this prevalence is projected tobe 5·03 million in 2025 (3·33 million women and
1·70 million men; table 1). In 2034, when the populationof people aged 65 years and older is projected to peak, thenumber who have dementia is projected to remain
approximately the same, at 4·90 million, followed by agradual decrease to 4·65 million in 2043.
The projected prevalence of dementia among people
aged 60–74 years shows a decreasing trend for both sexes
until 2034, at which point it increases slightly. However,the projected prevalence among people aged 75 years andolder differs by sex, with the prevalence among men
peaking in 2025 and decreasing thereafter; whereas, aconsistent increase in prevalence is seen among women,
most likely because of longer life expectancy than men
and an increased prevalence among people in their late
90s and older (table 1, figure 1).
The baseline prevalence of frailty in 2016 was
4·13 million (2·74 million women and 1·40 millionmen), and this number is expected to reach 5·24 million
in 2043 (3·51 million women and 1·72 million men;table 1). The prevalence of frailty among those aged
75 years and older is projected to increase byapproximately 1·3 times between 2016 and 2043 acrossboth sexes. Notably, 1·48 million women aged 75 years
and older are projected to have both dementia andfrailty in 2043.
We present education-stratified prevalence data in the
appendix (pp 29–31). The education-related gap in theprevalence of dementia and frailty is most notable among
those aged 75 years and older, and will increase from2016 to 2043. Approximately 29% of women aged 75 years
and older with less than high school educational
attainment (368 456 of 1 285 013 women) are projected tohave both dementia and frailty in 2043, whereas among
women in the same age group with college-level education
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A
100
90

Men in 2016
Men in 2043
Women in 2016
Women in 2043

Age-sex-specific prevalence (%)

80
70
60
50
40
30
20
10
0

B
100
35

Age-sex-specific prevalence (%)

30
25
20
15
10
5
0

C
100
90

Age-sex-specific prevalence (%)

80
70
60
50
40
30
20
10
0
60–62

63–65

66–68

69–71

72–74

75–77

78–80

81–83

84–86

Age (years)

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87–89

90–92

93–95

96–98

99–101 102–104≥105

Figure 1: Age-sex-specificprevalence of dementia (A),mild cognitive impairment(B), and frailty (C), estimatedas of 2016 and 2043, in men
and women aged 60 years
and oldere462

Articlesor higher the projected prevalence is approximately 7%(293 985 of 4 553 651).
Our simulation indicates that life expectancy willcontinue to increase and the compression of years
affected by dementia (figure 2A, B). Overall, between
2016 and 2043, life expectancy at age 65 years will
increase from 23·7 years to 24·9 years in women andfrom 18·7 years to 19·9 years in men. Life expectancy
with dementia at age 65 years was 4·7 years for womenand 2·2 years for men in 2016, and decreased to
3·9 years in women and 1·4 years in men in 2043.
Dementia-free life expectancy increases with higher
educational attainment and this discrepancy is mostnotable in men.
We observed extension of life expectancy with frailty inboth sexes between 2016 and 2043, more prominently
2016
Number of cases

2025
Prevalence
proportion

Number of cases

among women than among men, with years spent withfrailty increasing from 3·7 to 4·0 years for women and
1·9 to 2·1 for men (figure 2C, D).
The estimated annual cost of health care and formal
long-term care for people aged 60 years and older is
projected to reach $361 billion in 2043, a 12% increase
from the $323 billion in 2016 (figure 3). The annual costof health care is projected to peak in 2034, when the sizeof the older population also peaks, whereas the annual
cost of long-term care (ie, home-based and communitybased care and care in care homes) is expected tocontinue increasing. Health care and formal long-termcare costs for people aged 60 years and older withdementia amounted to $123 billion in 2016, and is
projected to increase to approximately $125 billion in
2043. Health care and long-term care costs for those with

2034
Prevalence
proportion

Number of cases

2043
Prevalence
proportion

Number of cases

Prevalence
proportionAge ≥60 yearsMen

19 388 180

Mild cognitive
impairment

2 099 367
(2 059 812–2 141 539)

Dementia··

19 780 650··

20 501 800··

19 896 410··

1 951 986
9·87%
(1 905 139–1 996 513) (9·63–10·09)

1 712 136
(1 679 667–1 740 805)

8·35%
(8·19–8·49)

1 512 801
(1 484 225–1 542 434)

7·60%
(7·46–7·75)

1 574 037
8·12%
(1 558 002–1 589 644) (8·04–8·20)

1 701 501
(1 688 133–1 721 208)8·60%
(8·53–8·70)

1 512 435
(1 501 547–1 525 162)7·38%
(7·32–7·44)

1 176 225
(1 167 746–1 187 434)

5·91%
(5·87–5·97)

Frailty

1 397 898
(1 396 632–1 400 103)7·21%
(7·20–7·22)

1 610 2148·14%
(1 608 468–1 612 455) (8·13–8·15)

1 743 082
(1 741 000–1 744 713)

8·50%
(8·49–8·51)

1 721 806
(1 719 608–1 725 415)

8·65%
(8·64–8·67)

Dementia andfrailty

419 282(415 982–422 267)2·16%
(2·15–2·18)494 973(491 168–498 058)

483 458(481 124–486 785)

2·36%
(2·35–2·37)402 453(400 113–406 704)

2·02%
(2·01–2·04)

Women

24 105 400

10·83%(10·62–11·05)··

24 762 430

2·50%(2·48–2·52)··

25 492 570··

24 599 120··

Mild cognitive
impairment

2 510 807
(2 460 312–2 560 955)

10·42%(10·21–10·62)

2 719 972
10·98%(2 663 206–2 780 339) (10·76–11·23)

2 714 563
(2 672 261–2 762 508)

10·65%(10·48–10·83)

2 353 775
(2 327 819–2 376 789)

9·57%
(9·46–9·66)

Dementia

3 528 476
(3 517 009–3 542 491)

14·64%(14·59–14·70)

3 328 257
(3 316 729–3 343 257)

13·44%(13·39–13·50)

3 389 978(3 376 754–3 402 069)

13·30%(13·25–13·34)

3 469 545
(3 459 747–3 482 466)

14·11%(14·06–14·15)

Frailty

2 736 493
(2 735 046–2 738 454)

11·35%(11·35–11·36)

3 159 041
(3 157 211–3 163 165)

12·76%(12·75–12·77)

3 462 453
13·58%(3 459 182–3 464 991) (13·57–13·59)

3 514 460
(3 511 671–3 516 837)

14·29%(14·28–14·30)

Dementia andfrailty

1 326 273
(1 322 800–1 329 978)5·50%
(5·49–5·52)

1 385 076
(1 381 675–1 387 750)

5·59%
(5·58–5·60)

1 412 591
5·54%
(1 410 049–1 415 646) (5·53–5·55)

1 508 757
(1 505 482–1 512 162)6·13%
(6·12–6·15)

11 737 240

11 556 310

Age 60–74 yearsMen

12 495 730··

10 983 780

··

··

··

Mild cognitive
impairment

647 838(623 913–669 322)

5·18%
(4·99–5·36)379 758(365 668–393 103)3·46%
(3·33–3·58)

304 765(294 038–318 124)

2·60%
(2·51–2·71)323 306(312 044–335 989)

2·80%
(2·70–2·91)

Dementia

241 230(234 223–251 722)

1·93%
(1·87–2·01)

120 409(117 612–128 125)1·10%
(1·07–1·17)73 529(71 206–76 722)

0·63%
(0·61–0·65)

80 786(77 952–84 854)0·70%
(0·67–0·73)

Frailty

253 188(252 590–254 024)

2·03%
(2·02–2·03)

209 534(208 508–210 371)

1·91%
(1·90–1·92)

201 042(200 269–202 084)

1·71%
(1·71–1·72)

223 752(222 768–224 374)

1·94%
(1·93–1·94)

Dementia andfrailty

22 988(22 363–23 514)

0·18%
(0·18–0·19)

12 625(12 309–13 056)

0·11%
(0·11–0·12)

7 443(7 208–7 766)

0·06%
(0·06–0·07)

8 511(8 208–8 835)

0·07%
(0·07–0·08)

Women

13 374 790··

11 580 100··

12 156 230··

11 999 820··

Mild cognitive
impairment

652 112(627 822–671 562)

4·88%
(4·69–5·02)

575 006(562 610–586 196)

4·97%
(4·86–5·06)

576 853(564 258–586 455)

4·75%
(4·64–4·82)588 701(578 383–599 312)

4·91%
(4·82–4·99)

Dementia

540 643(533 481–550 283)

4·04%
(3·99–4·11)

401 484(394 366–407 183)

3·47%
(3·41–3·52)

357 065(351 389–364 288)2·94%
(2·89–2·100)

370 194(363 351–378 297)

3·09%
(3·03–3·15)

Frailty

256 670(255 959–257 469)

1·92%
(1·91–1·93)

225 264(224 424–225 787)

1·95%
(1·94–1·95)

202 606(202 108–203 363)

1·67%
(1·66–1·67)219 516(218 784–220 270)

1·83%
(1·82–1·84)

Dementia andfrailty

56 820(55 923–57 626)0·42%
(0·42–0·43)

37 988(37 091–38 776)

0·33%
(0·32–0·33)

29 316(28 786–29 932)

0·24%
(0·24–0·25)

31 299(30 685–31 974)0·26%
(0·26–0·27)(Table 1 continues on next page)e463

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2016
Number of cases

2025
Prevalence
proportion

2034

Number of cases

Prevalence
proportion

Number of cases

2043
Prevalence
proportion

Number of cases

Prevalence
proportion(Continued from previous page)Age ≥75 yearsMen

6 892 449··

8 797 099··

8 764 496··

8 340 157··

Mild cognitive
impairment

1 452 816
(1 421 541–1 485 732)

21·08%(20·62–21·56)

1 571 291
(1 530 316–1 614 157)

17·86%(17·40–18·35)

1 404 037
16·02%(1 381 904–1 436 634) (15·77–16·39)

1 190 512
(1 162 623–1 210 344)

14·28%(13·94–14·51)

Dementia

1 330 830
(1 321 801–1 346 559)

19·31%(19·18–19·54)

1 579 892
17·96%(1 562 960–1 595 405) (17·76–18·14)

1 438 631
16·41%(1 426 642–1 450 504) (16·28–16·55)

1 094 243
(1 086 234–1 106 091)

13·12%(13·02–13·27)

Frailty

1 144 626
(1 143 366–1 146 433)

16·61%(16·59–16·63)

1 400 766
15·92%(1 398 867–1 402 590) (15·90–15·94)

1 542 012
(1 540 086–1 543 719)

17·59%(17·58–17·61)

1 498 174
(1 496 468–1 501 385)

17·97%(17·94–17·99)

Dementia andfrailty

396 540(392 915–398 900)

5·75%
(5·70–5·79)482 238(478 802–485 397)

476 089(473 664–479 254)

5·43%
(5·40–5·47)393 916(391 690–398 080)

4·72%
(4·70–4·77)

Women

10 730 610

Mild cognitive
impairment

1 862 290
(1 813 325–1 917 555)

Dementia··
17·35%(16·90–17·87)

13 182 410

5·48%(5·44–5·52)··

13 336 360··

12 599 170··

2 146 789
16·29%(2 095 730–2 206 690) (15·90–16·74)

2 136 943
16·02%(2 097 600–2 186 698) (15·73–16·40)

1 763 372
(1 740 182–1 787 010)

2 987 831
27·84%(2 977 104–2 996 943) (27·74–27·93)

2 926 058
(2 914 632–2 941 153)

3 032 892
22·74%(3 020 980–3 047 625) (22·65–22·85)

3 099 972
24·61%(3 092 386–3 108 864) (24·55–24·67)

Frailty

2 479 944
23·11%(2 478 052–2 481 689) (23·09–23·13)

2 933 980
22·26%(2 932 228–2 937 584) (22·24–22·28)

3 259 602
(3 256 768–3 262 232)

24·44%(24·42–24·46)

3 294 875
(3 292 543–3 297 457)

26·15%(26·14–26·17)

Dementia andfrailty

1 269 639
(1 266 877–1 273 129)

1 346 920
10·22%(1 343 984–1 349 643) (10·20–10·24)

1 383 218
10·37%(1 380 498–1 386 477) (10·35–10·40)

1 477 213
(1 474 770–1 480 889)

11·73%(11·70–11·75)

11·83%(11·81–11·86)

22·20%(22·11–22·31)

14·00%(13·81–14·18)

Data in parentheses are the 5th to 95th percentile range from 50 iterations.

Table 1: Projected prevalence of dementia, mild cognitive impairment, and frailty in 2016, 2025, 2034, and 2043, by sex

frailty amounted to $77 billion in 2016, and is projected toincrease to $97 billion in 2043.
In 2016, the annual cost per capita for health care andformal long-term care among women with an educational
level of less than high school was almost twice that of
their counterparts with a college education or higher, andthe gap widened further by 2043 (table 2). Whereas, for
men the difference in annual costs between those withless than high school education and college education or
higher is not as pronounced. This gap is largely
attributable to the difference in estimated long-term carecosts. Among those with lower educational attainment,
women require more spending on formal long-term careand health care than do men, and this sex difference is
projected to increase between 2016 and 2043 (table 2).
Women with a lower level of education with dementia or
frailty, or both, consistently bear the highest cost percapita.
Finally, we estimate the additional cost for informalcare provision to be $93·1 billion in 2016, 59% of which
was related to dementia care and 33% to frailty care. The
cost is expected to increase to $103·3 billion in 2043,
52% of which is related to dementia care and 36% tofrailty care. Because household size and the related
capacity for informal care are projected to decrease byapproximately 20% between 2016 and 2043, we did asensitivity analysis assuming a substitutional increaseof 13% in community-based home care, on the basis of
the existing literature (appendix p 22).31 The results ofthis analysis suggest that the total formal and informal
www.thelancet.com/public-health Vol 7 May 2022

care costs will remain at approximately $460 billion perannum in all those aged 60 years and older after
substitution (appendix p 22).

Discussion
To our knowledge, this is the first microsimulation-based
forecast of the joint distribution of dementia and frailty,
which are major health-care challenges for the ageing
population.
Despite rapid population ageing in Japan across the
projected period (2016–43), the time spent with dementia
is expected to decrease among those aged 60–74 years,
especially among men, primarily attributable to pro­
jected improved educational attainment and reduced
cardiovascular risks among that subpopulation. Previous
forecasts of the prevalence of dementia, which relied on
static assumptions without considering the anticipatedimprovement in educational attainment and cardio­
vascular risks among the future older population,
estimated that the population with dementia would
reach 9·0 million, or 25% of the population aged 65 years
and older in Japan by 2040,28,32 a figure that exceeds ourestimates. Recent estimation with con­sideration of risk
factor improvement over time by the GBD 2019
Dementia Forecasting Collaborators projected theprevalence of dementia in Japan to be 5·2 million in
2050, which seems closer to our estimate.33
The impact of frailty is likely to increase for both men
and women, with larger increases in the prevalence offrailty predicted for women than for men. Compared withe464

Articles

A Men
30

Without dementia

Home-based and community-based care
Care at care homes
Inpatient medical service
Outpatient and prescription

With dementia

400

20

2·2

15
10

16·5

1·2

0·2

1·4

1·5

300

5·0

4·1

17·5

14·4

18·9

18·5

18·1

14·6

19·9

5

50
With dementia

Life expectancy (years)

4·7

5·6

19·0

17·9

20

0

4·0

3·7

3·9

19·7

20·2

21·0

3·7

5·9

3·9

15
10

21·1

18·2

21·2

5
0

C Men
30

Without frailty

With frailty

Life expectancy (years)

25
20

1·9

2·2

1·7

1·9

2·1

2·6

1·9

2·2

16·8

16·2

17·0

17·1

17·8

17·0

17·7

18·0

15
10
5
0

D Women30

Without frailty

With frailty

25
20

3·7

3·9

3·7

3·2

4·0

4·4

4·2

3·6

20·0

19·7

20·1

20·7

20·9

19·7

20·6

21·5

15
10
5
0

All

Less than High school College
high schooland higher
2016

All

Less than High school College
high schooland higher
2043

Figure 2: Projected life expectancy at age 65 years with and without dementia (A, B) or frailty (C, D) in 2016
and 2043, by sex and educational attainment

dementia, the incidence and progression of which isstrongly associated with demographic factors, physicalfrailty might reflect near-universal susceptibility to

89·7

89·6

87·2

116·0

114·3

103·2

110·5

65·8

67·5

71·8

76·6

74·5

74·9

79·0

82·9

2016

2025

2034

2043

150

B Women25

Life expectancy (years)

200

100

Without dementia

79·1

250

0

30

e465

350

0·3
Cost (billions of US$)Life expectancy (years)

25

Year

Figure 3: Projected costs of health care and formal long-term care for the
population aged 60 years and older
Health care includes outpatient and prescriptions and inpatient medical services.
Formal long-term care includes home-based and community-based care andcare in care homes. Annual cost estimation in billions of US$ at 2016 value.

age-related physiological decline that could result insmaller education-related disparities and a larger burden
among women than among men, given their longer life
expectancy.
Existing dynamic microsimulation models for England,
the USA, and other countries consistently indicate thatthe prevalence of dementia in the overall population will
increase, despite decreasing age-specific incidence andprevalence, because of improved longevity.­9,10,33 In Japan,
decreased prevalence among those younger than
95 years, and increased prevalence thereafter, seem tocounterbalance the increased number of older people
with dementia caused by extended life expectancy.
However, we estimate that care costs will remain high,with an increase in comorbid dementia and frailty
requiring increasing costs per capita. With an expected
decrease in the size of the working-age population,23 thesocial burden of care on the working-age population will
become more onerous.
Our dynamic microsimulation model is similar to the
PACSim project, which jointly models dementia withmultiple comorbidities and socioeconomic conditions of
the older population.8 PACSim also relies on several
nationally representative surveys with a specific functional
measurement obtained from the English Longitudinal
Study of Ageing. Our model primarily uses crosssectional, nationally representative surveys, augmented
by existing population-based survey data for measurementof cognitive functions and physical frailty.
Despite demographic and institutional differences
between England and Japan, we found importantsimilarities between the two countries. In particular, wefound that the effect of dementia on requirements for
caregiving will differentially affect subpopulations ofolder people, depending on their sex and socioeconomic
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Articles

Men

Women

Less than high school
education

College education andhigherLess than high school
education

College education andhigherFormal long-term care costs$3569 (3568–3571)$1825 (1823–1826)$5517 (5515–5519)$1974 (1972–1976)

Health-care costs$4264 (4263–4266)$4101 (4100–4102)$4175 (4175–4176)$2992 (2991–2993)

Year 2016
Total population

Mean number of comorbidities in population aged ≥60 years per capita

1·65

1·65

1·52

1·35

Without dementia or frailty
Formal long-term care costs$1407 (1392–1423)$1428 (1425–1431)$441 (438–444)$752 (741–766)

Health-care costs$3762 (3757–3768)$3982 (3981–3983)$3238 (3231–3243)$2779 (2777–2781)

Mean number of comorbidities in population aged ≥60 years per capita

1·48

1·61

1·41

1·32

With dementia or frailty, or both
Formal long-term care costs$8950 (8882–9033)$9134 (9093–9167)$15 803 (15 753–15 840)

$15 880 (15 803–15 935)

Health-care costs$5573 (5550–5599)$6325 (6308–6343)$6094 (6081–6109)$5422 (5393–5449)

Mean number of comorbidities in population aged ≥60 years per capita

2·06

2·25

1·74

1·80

Year 2043
Total population
Formal long-term care costs$4378 (4367–4385)$2240 (2236–2243)$8687 (8673–8701)$3290 (3286–3293)

Health-care costs$4337 (4333–4341)$4281 (4280–4283)$5140 (5135–5146)$3237 (3236–3239)

Mean number of comorbidities in population aged ≥60 years per capita

1·73

1·80

1·62

1·38

Without dementia or frailty
Formal long-term care costs$1724 (1701–1746)$1701 (1699–1704)$823 (813–831)$1140 (1125–1151)

Health-care costs$3584 (3578–3590)$4137 (4135–4139)$3702 (3693–3713)$2912 (2911–2914)

Mean number of comorbidities in population aged ≥60 years per capita

1·42

1·75

1·46

1·30

With dementia or frailty, or both
Formal long-term care costs$8849 (8786–8903)$7904 (7887–7926)$16 275 (16 236–16 314)

$15 717 (15 682–15 768)

Health-care costs$5872 (5846–5895)$6172 (6163–6182)$6693 (6679–6707)$5435 (5423–5447)

Mean number of comorbidities in population aged ≥60 years per capita

2·28

2·44

1·79

1·84

Data in parentheses are the 5th to 95th percentile range from 50 iterations. Annual cost estimations are in US$ at 2016 value. For mean number of comorbidities, uncertainty ranges were obtained but theranges were too narrow to present here to a sufficient level of accuracy.

Table 2: Projected costs for health care and formal long-term care per capita for the population aged 60 years and older, by sex and educational attainment

conditions. An education-related gap in the prevalence of
dementia has also been reported in the USA.15 Our
forecast indicates that comorbid dementia and frailty will
be more prevalent among women aged 75 years andolder than other subpopulations and among those with alower level of education than with a higher level of
education, who will require greater resources for theircomplex care needs.
Our study has some limitations. First, the method weused for classification of dementia status has not beentested directly with the concurrent clinical measurementof cognitive function. Although our estimation success­fully replicated the age-specific prevalence distributionof dementia among the Japanese population, it might
misclassify status at the individual level. Moreover,the cross-country comparison and cross-validation ofdifferent classification algorithms require caution andfurther research.34 We also had to rely on urban data forfield-based measurement of frailty that might notnecessarily reflect rural situations. Because approximately
95% of the Japanese population lives in cities, the use of
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urban data might still provide a plausible projection offrailty. Second, the model does not include information
on health-related behaviours, such as smoking, exercise,and dietary habits, which are known correlates of healthin old age.35–37 In the state-transition model we adopted,
we had to assume non-reversibility of chronic conditions
for model simplicity; however, health behaviours willchange over the life course. With limited availability of
information on health behaviours in the data source,
our simulation could not incorporate behavioural
information, which might lead to an overestimation of
the future prevalence of dementia, given, for example,
the decreasing trend in smoking.33 Third, our model
assumes constant transition probabilities between multicomorbid statuses after 2015. Because some comorbid
statuses are competing risks for others, change in one
comorbid incidence will affect others in a complex way,
which might lead to overestimation or underestimation.
Our model also assumes constant effectiveness ofcurative and preventive health-care technology in thenear future; an assumption which is likely to lead to thee466Articles

overestimation of future disease prevalence. Finally, ourcurrent study assumes constant patterns of use of formal
and informal care and does not consider how health anddemographic trends might affect the demand for
informal care and the indirect costs of lost productivity.
Therefore, our results are likely to underestimate thefuture economic impact of frailty and dementia.
Our projection of reduced prevalence of dementia in
the next 20 years in a subset of the population is goodnews for a rapidly ageing but highly educated population,although this trend is unevenly distributed across thesexes and by socioeconomic status. Tertiary educationand economic participation oppor­
tunities are lessavailable to women than to men in Japan.38 Consequently,
even with high educational attainment, women in Japan
probably experience greater stress and poorer healththan do men.39 Further research and policy discussion onwhether closing the sex-related gap in education andsocial participation leads to a diminishing future burdenof dementia and frailty in society might be warranted.
Japan’s working-age population is expected to decreasein size for the foreseeable future. An increasing fractionof the population will have decreasing functional
status. Meanwhile, many promising interventions could
potentially delay the onset of frailty, such as regularexercise and psychosocial support.36 However, in addition
to individual-level interventions, Japan must invest in
developing social and physical environments that areinclusive for people with such disabilities. Although
Japanese people might have fewer years lived withdementia in the near future, a disproportionate
concentration of frailty and dementia among susceptible
subpopulations requires attention to improve healthequity beyond simply regarding population ageing as asocial burden. Both sex and educational disparities inhealth among the older population deserve concentrated
attention as part of the public health policy agenda for
population ageing. The future burden of dementia andfrailty should be countered by curative and preventive
technology innovation, and further mitigated by social
policies to close the health gap.
Contributors
MKa contributed to the study design, model building, and data analysis;
interpreted the data; and drafted and revised the manuscript. SK and
HM analysed the data for the cost analysis. TT, B-KS, and KI contributed
to the acquisition of community-based functional measurement data
and related data analysis, and revised the manuscript. KG and MKi
contributed to the construction of a high-specification computer
simulation environment for dynamic microsimulation. KE and JB
contributed to model building and critically revised the manuscript.
HH contributed to the study design and data acquisition, reviewed
the literature, interpreted the data, and drafted and critically revised themanuscript. MKa and HH accessed and verified the data used in the
study, and all authors had access to the verified data.
Declaration of interests
We declare no competing interests.
Data sharing
Government microdata are available by due application according to
the Statistics Act article 33. JSTAR data are available on request withapproval for use from the Research Institute of Economy, Trade,e467

and Industry, Japan. Availability of Kashiwa study data is restricted due
to confidentiality arrangements with the Kashiwa Municipality
authority. Model codes and summary data from the study are available
from the corresponding author upon request.
Acknowledgments
This study was funded by the Japan Society for the Promotion of Science(18H04070) and a non-restricted fund from Hitachi – the University ofTokyo Laboratory for a sustainable society. The study was partly fundedby the National Institute on Aging of the National Institutes of Health(award number 3R01AG030153-14S3, awarded to MKa and HH).
KE gratefully acknowledges research funding from a Shorenstein AsiaPacific Research Center faculty research award and a pilot award fromthe National Institute on Aging of the National Institutes of Health(award number P30AG066589).
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