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Projecting prevalence of frailty and dementia and the economic cost of care in Japan from 2016 to 2043: a microsimulation modelling study

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 this situation 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 and higher), 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 women and from 18·7 years to 19·9 years in men. Years spent with dementia will decrease from 4·7 to 3·9 years in women and 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 a sizable population of older people with frailty and dementia, and a widening disparity in the burden of those conditions 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.


Literature review results for Research in Context
As of January 2022, we reviewed existing computer simulation models that forecast future prevalence of dementia and/or frailty, and studies projecting their expected healthcare and social costs. We searched PubMed using the following search strategy, and complemented this search using Google to include published reports and grey papers. We also manually searched existing literature and gray papers published in Japanese. For dementia conditions, we identified ten streams of simulation studies that forecast future prevalence/incidence of dementia in the UK, the USA, Spain, Canada, Australia, and Ireland (Appendix Table 1). [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] The available worldwide reports and grey papers relied on macro projection with statistical assumption using the estimated future population component and currently estimated strata-specific prevalence of the target condition, which are excluded from the survey.
There were two streams of studies (PACSim 1,2 and FEM-based 5,6 ) that included multi-comorbidity conditions, including activities of daily life and/or dependency in daily life, in the forecasting simulation. Two other studies (IMPACT-BAM 3,4 and a Spanish study 11 ) included cardiovascular risk factors in the dementia prevalence forecast. Baseline estimation of dementia conditions was based on empirical cognitive function assessment in social surveys in three study streams (PACSim, IMPACT-BAM, and FEM). These studies assess the population impact of dementia on life expectancy. 1,3,6 A recent GBD-based study estimated the trend of dementia prevalence regressed on the summary score of risk factors as well as educational attainment trend by country levels. 15 We did not identify any studies that explicitly forecast the future frailty trend, presumably because of the current lack of consensus on the measurement of frailty, and limited prevalence data on the condition. PACSim forecast the prevalence of elderly people with dependency needs, 1 and FEM forecast those having difficulties with daily life activities. 5,6 5 Appendix ( ) .
Our model assumed Granger causality because our aim was to predict future health states, not to identify causal pathways. All chronic conditions were assumed to be absorbing states (no recovery). Our model limited the population at risk for a disease-specific death to those who had that disease. Age-sex-condition-specific case fatalities for corresponding years were estimated using vital statistics microdata under additive assumptions (i.e., case fatality of a comorbidity condition is assumed to be additive when calculating probabilities of corresponding comorbidity death rates). We estimated simulation parameters using Python 3.7.7.
In this study, age-sex-disease-specific case fatalities and all-cause mortality were calculated for three educational levels (lower than high school, high school, and university including junior college and vocational school) using the census-mortality linkage method (Kasajima and Hashimoto 2020). We required this process because death records in Japan do not contain educational attainments or personal identification numbers. We calculated the incidence rate ratio of mortality relative to primary education using Poisson regression between 2000 and 2010 for each birth year cohort in 3-year increments, and then extended the ratios to 2015. We estimated the sex, birth year cohort, and education-specific baseline population as of 2016 based on the 2000 census population because the 2010 census had a lower response rate and a higher proportion of missing educational information. A population aged 80 years or older was estimated using the survival ratio method and extinct cohort method, following the Human Mortality Database protocol (available at https://www.mortality.org/).
We applied a census-mortality linkage approach to generate a future population of incoming cohorts aged 60-62 years every 3 years. For each educational group, we generated the number of death exits from the 2000 census population based on age-sex-education-specific all-cause mortality rates until the cohorts reached an age of 60 years, at which point they reached the age for microsimulation cohort entry. We started our simulation with a population aged 60 years or older and their health conditions as of June 2016 as the baseline. Owing to deterministic linkage allowing 1:m matching, the education-related mortality risk ratio may be underestimated in urban settings for the younger population, in which a larger number of "m" were matched to one death record, which may have led us to underestimate the education-related disparity in future health projection.
For future projections, the estimated condition-specific incidence and case fatalities in the most recent year (2015) were adopted in an estimated transition probability matrix for the first-order Markov model. We used a half-year cycle rather than a 1-year cycle in the Markov model because some disease conditions, especially cancer, have a turnover shorter than one year. This precludes the estimation of transition probability owing to uncounted death exits that may have exceeded new incidences in the middle of the cycle length if we had used a 1-year cycle.
Therefore, we used a half-year cycle to obtain a balance between exit and entry.
As described above, our modelling of state transition was deterministic rather than stochastic, in that we deterministically obtained state-transition probabilities for projection estimation of future prevalence of states  insurance claims *The "other" category included circulatory diseases other than coronary heart disease (e.g., heart failure), gastric diseases, and non-cancer prostatic conditions (e.g., hyperplasia).

Appendix Technical Note 2. Estimation of cognitive impairment and frailty prevalence by probabilistic extrapolation
We focused on frailty and dementia as a policy target for integrated formal medical and personal care, which is likely to require substantial resources given the aged population. We used mild cognitive impairment (MCI) as a precursor stage of dementia.
For dementia and MCI predictions, we created three binary outcome variables, defined as poor performance at immediate word recall ( 1 ), delayed word recall ( 2 ), and serial-7 examinations ( 3 ), accompanied by iADL limitations.
In the JSTAR battery for cognitive functional measurement we relied on in this estimation, the orientation to date and place, immediate and late recall of 10 nouns, and serial-7 examinations were used to enable comparison with its sister survey, the Health and Retirement Study (HRS error. Because word recall and serial-7 examination performance failure were differently predicted by these regression variables, we chose to run multivariate seemingly related probit models to simultaneously regress three test outcomes on the same set of above-mentioned explanatory variables, while allowing intercorrelation of error terms in simultaneous equations (see Supplementary Table 2 for regression results). We obtained the probability of non-failure of all three tests based on the estimated joint distribution of failure probabilities of these tests. We used this obtained probability as an indicator of cognitive function; higher probability was associated with better cognitive function.
The predicted test failure probabilities were extrapolated on an individual basis in a virtual cohort of the future older Japanese population prepared on the simulation system, according to age, sex, educational attainment, comorbidities, psychological distress, and limitations in ADL. Finally, we compared our estimated age-strata-specific prevalence with the reported numbers in Ninomiya and Asada, to see if our estimation model could reproduce the age-sex-specific distribution of dementia prevalence in the real world (Appendix Table 2). We took this reproduced distribution as support data for the validity of our estimation of dementia prevalence. [Formulation] We calculated the joint probability corresponding to a cognitively normal condition with ( 1 = 0, 2 = 0, 3 = 0) using the multivariate probit model: The common predictor X contained age, educational attainment, comorbidity conditions, self-rated health, ADL disability, and psychological distress conditions. Supplementary Table 1 shows the coefficients of multivariate probit regression ( = 1,2,3) and the ρ matrix, which describe the correlation across the error terms, 1 , 2 , and 3 . The probability of observing ( 1 = 0, 2 = 0, 3 = 0) is approximated by a product of three independent standard normal random variables and Cholesky decomposition of a ρ matrix, called the Geweke-Hajivassiliou-Keane (GHK) simulator. We ran 100 iterations of the GHK simulator to obtain a numerical approximation. For 50 iterations of the JFEM simulation, 5,000 iterations in the GHK simulator were implemented.
We sequentially indicated the probability thresholds of dementia and MCI conditions from the lowest value of the joint probability to meet the previously reported prevalence of dementia and MCI in Japan [Asada (2013) 25 and Ninomiya (2014) 26 ]. The dementia probability thresholds were 0·6606126 for men and 0·7162467 for women. The MCI probability thresholds were 0·7738026 for men and 0·8303307 for women.
We used logistic regression to predict frailty conditions for comparison with previous studies on frailty risk factors.

Healthcare costs
We estimated healthcare costs of inpatient medical services and outpatient services, including prescriptions. We analysed a 3% sample of the national electronic claims data (approximately 200 million records of 5 million individuals) for the national health insurance from April 2013 to March 2016. The claims data included monthly utilisation of healthcare by service type, with information on patient demographics and diagnoses. We regressed the utilisation of medical services for men and women separately according to age, morbidity diagnoses of 11 diseases, and the number of comorbidities (Supplementary Table 4).
For each individual , we extrapolated the expected monthly utilisation for inpatient medical services ( _ ) and outpatient care and prescriptions (Outp_cost ) by with health conditions, , and the number of comorbidities, ( ). For outpatient services, we specified the initial consultation, , if the individual, i, was newly diagnosed in the corresponding month; otherwise, the consultation was regarded as a follow-up consultation (namely, = + − ). We fixed the age effect, α, after age 85 years to fit our cost estimation per capita to the official governmental report.
We did not estimate an individual patient's status for requiring outpatient or inpatient services or both; therefore, we estimated the propensity of receiving inpatient services ( ) by referring to the number of inpatient cases out of the total claims for each age-sex strata under the health condition, . Then, we obtained the total medical cost for the month by aggregating the expected utilisation of the simulated population: The monthly estimation was multiplied by 6 months for the estimation period cycle, and was finally summed for the annual estimation.
Reliance on diagnostic codes presented in administrative data may lead to the problem of overcoding and consequent misclassification of the resource utilisation linked to disease-specific conditions. Although we acknowledge this problem, we assigned health conditions by simply referring to recorded diagnostic codes.
As a validity check, we applied the obtained regression coefficients to the prepared virtual cohort of the older Japanese population (aged >65 years) in the simulation to extrapolate estimated individual monthly healthcare utilisation. We compared the sum of the estimated individual utilisation with the government report of national medical expenditure, as presented in Supplementary ) by age-sex-dependency strata to obtain the total cost of social care. We did not estimate the place of service reception in our simulation model; therefore, we estimated the propensity of service type use by referring to the ratio of community-based service users to care home users as of 2016 in each segment of dependency status for each sex and age category. people (40% in their 60s to 90% in their 90s) who needed care for emerging ADL/IADL dysfunctions actually applied for a formal LTC service. Thus, we present estimated formal care costs based on the as-is scenario (63% of those in need will use formal care) for comparison with the number reported in the government report. We confirmed that our as-is scenario corresponds well to the publicly announced actual use of formal LTC services (Supplementary Table 7).

22
Scenario analysis for sensitivity check As we anticipate that the household capacity for informal care provision will decline over time in Japan owing to an expected reduction in household size, it is unclear whether formal care use will increase to substitute for the decline in informal care provision, given the mixed empirical findings in the literature.
We acknowledge a study by Bonsang (2009) in J Health Econ, which used European panel data derived from the Survey of Health, Ageing, and Retirement in Europe (SHARE), and concluded that a substitutional association was observed only for mild care needs. 28 Evidence from the USA suggests a substitution of formal care for people with dementia with fewer family members availabile to supply informal care (Choi et al. 2021) 29 , and that better access to formal care support reduced coresidence with adult children (Mommaerts 2018). 30 Courbage et al. used SHARE data in a European study and found mixed results; formal care was substituted for informal care in Spain, but complemented informal care in Italy. 31 More recently, Lin (2019) reported the situation in China, and concluded that formal care is complementary rather than substitutional for informal care provision. 32 This issue remains understudied in Japan. Recently, Miyawaki et al. (2020) reported that the reduced availability of formal care for milder care needs after public policy change leads to increased hours of informal care provision. 33 This suggests a substitutional relationship in the case of mild care needs; however, the reverse pattern (that a reduction in informal care leads to a substitutional increase in formal care use) remains to be tested.
Given the available evidence on this issue, we concluded that there is no firm empirical basis on which to make assumptions about the resource utilisation patterns for formal and informal care. We adopted a conservative position and decided to assume that the pattern will remain constant over the next two decades. This assumption may underestimate the use of formal care services and overestimate the cost of informal care if the substitutional association holds in the case of mild care needs.
We conducted a sensitivity analysis in which we relied on the substitutional elasticity between formal community-based home care and informal care demonstrated in Bonsang (2009) 28 (substitutional elasticity of −0·68, or 10% increase in informal care leads to 6·8% decrease in formal care use). Bonsang found that the substitution of informal care provision for nursing care was not significant. We referred to a government projection that mean household size would decrease from 2·33 in 2015 to 2·08 in 2040, and estimated that the capacity of informal care (household size minus 1) is expected to decrease by approximately 19% (= 1−(2·08−1)/(2·33−1)). If we apply this number to our estimated cost of formal community-based home care services, a 19% decrease in informal care cost (=103·3 billion USD) will be substituted by an increase in formal care cost by 13%, or 11·9 billion USD (=83 billion USD*0·13) as of 2043.
The total cost of informal and formal care as of 2043 was estimated as 464·2 billion USD. After the substitution, the cost will be 455·5 billion USD, or 98·1% of the original estimate. Note. High is an abbreviation for high dependency (with disability conditions categorised in the higher four levels for longterm care service use); Mild is an abbreviation for mild dependency (the lower three levels). We converted JPY to USD at the exchange rate 1,000 JPY = 9·091 USD.      http://www.ipss.go.jp/pp-zenkoku/j/zenkoku2017/db_zenkoku2017/s_tables/1-9.htm http://www.ipss.go.jp/pp-zenkoku/j/zenkoku2017/db_zenkoku2017/s_tables/11-6.htm

Supplementary
The results indicate extension of life expectancy with dementia and frailty in both sexes, which leads to overestimation of future dementia and frailty prevalence compared with that produced by the current study.