Article

Complex Multimorbidity and Working beyond Retirement Age
in Japan: A Prospective Propensity-Matched Analysis
Daisuke Kato 1,2,*, Ichiro Kawachi 3 and Naoki Kondo 4
Department of Family Medicine, Mie University Graduate School of Medicine, Tsu 514-8507, Japan
Department of Gerontological Evaluation, Center for Gerontology and Social Science,
National Center for Geriatrics and Gerontology, Obu 474-8511, Japan
3 Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health,Boston, MA 02215, USA; ikawachi@hsph.harvard.edu
4 Department of Social Epidemiology, Graduate School of Medicine and Public Health, Kyoto University,
Kyoto 606-8315, Japan; kondo.naoki.0s@kyoto-u.ac.jp* Correspondence: d.kato0417@gmail.com
1
2

Citation: Kato, D.; Kawachi, I.;
Kondo, N. Complex Multimorbidity
and Working beyond Retirement
Age in Japan: A Prospective
Propensity-Matched Analysis. Int. J.
Environ. Res. Public Health 2022, 19,6553. https://doi.org/10.3390/
ijerph19116553
Academic Editor:

Abstract: Background: With the aging of populations worldwide, the extension of people’s working
lives has become a crucial policy issue. The aim of this study is to assess the impact of complex
multimorbidity (CMM) as a predictor of working status among retirement-aged adults in Japan.
Methods: Using a nationwide longitudinal cohort study of people aged over 65 who were free of
documented disability at baseline, we matched individuals with respect to their propensity to develop CMM. The primary outcome of the study was working status after the six-year follow-up.
Results: Among 5613 older adults (mean age: 74.2 years) included in the study, 726 had CMM and2211 were still working at the end of the follow-up. In propensity-matched analyses, the employment rate was 6.4% higher in the CMM-free group at the end of the six-year follow-up compared to
the CMM group (725 pairs; 29.5% vs. 35.9%; p = 0.012). Logistic regression analysis showed that
CMM prevented older people from continuing to work beyond retirement age and was a more important factor than socioeconomic factors (income or educational attainment) or psychological factors (depressive symptoms or purpose in life). Conclusions: Our study found that CMM has anadverse impact on the employment rate of older adults in Japan. This finding suggests that providing appropriate support to CMM patients may extend their working lives.
Keywords: multimorbidity; complex multimorbidity; employment

Fidel Hita-Contreras
Received: 6 April 2022
Accepted: 23 May 2022
Published: 27 May 2022
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Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

1. Introduction
Due to medical and public health advances throughout the world, people are not
only living longer but also suffering from the simultaneous occurrence of multiple diseases (multimorbidity: MM). The combination of longer lives and a higher prevalence of
MM will pose a serious challenge to healthcare and to social security system solvency [1].
Combined with the declining fertility rate, these trends are projected to lead to higher
dependency ratios and a social security budgetary crisis in the future [2]. This means thatgovernments will be forced to make unpopular choices, such as raising taxes, reducing
social security benefits, or increasing the retirement age.
Increasing the retirement age is dependent on having a fit workforce. Because mortality is declining faster than disease prevalence, the number of people living with comorbidities is increasing throughout the world [3]. People are currently expected to live approximately 20 years after retirement age, many of them with MM. This makes it criticalto understand the relationship between MM and the ability of older people to extend theirworking lives.

Int. J. Environ. Res. Public Health 2022, 19, 6553. https://doi.org/10.3390/ijerph19116553

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Conventionally, MM has been defined by the co-occurrence of two or more diseases[4], which sometimes highlights its limitations as an index due to its higher prevalence
ratio (55–98%) [5,6]. In this study, we adopted a potentially more sensitive concept of MM,
namely, complex multimorbidity (CMM) [7], which is defined as the involvement of threeor more body system disorders (as opposed to a simple count of comorbid diseases).
CMM has been established as a risk factor for death and long-term care needs [8–11].
Furthermore, while employment status is strongly influenced by socioeconomic status(SES), older adults with CMM in particular tend to have lower SES than those with MM[8,12]. These findings demonstrate the significance of focusing on body systems andCMM, especially in MM studies focusing on SES.
The purpose of this study is to clarify the impact of CMM on the working status of
retirement-aged adults in Japan.
2. Materials and Methods
2.1. Study Participants
We used panel data from three survey waves (2010, 2013, and 2016) of the Japan Gerontological Evaluation Study (JAGES), a nationwide cohort of older adults in Japan [13].
The compulsory Japanese residential registry system was used as the sampling frame.
Self-administered questionnaires were mailed to all eligible older people in the case ofmunicipalities with fewer than 5000 residents or by random sampling in the case of municipalities with more than 5000 residents. JAGES focused on community-dwelling individuals aged 65 years or older who were free of disability at baseline. The baseline survey
in 2010 included 95,827 older adults from 16 municipalities.
In total, 62,418 individuals answered the questionnaire (response rate: 65.1%). We
excluded individuals who were not functionally independent, receiving any nursing careor home care assistance, or out of work at baseline. After excluding individuals with incomplete data on the history of present illnesses or missing information on employment
status at follow-up, a cohort of 5613 individuals was identified for analysis (Figure 1). The
most common reason for being excluded in our analysis was that the participant was already retired or not working at baseline (n = 32,230).

Figure 1. Participant flow diagram.

Int. J. Environ. Res. Public Health 2022, 19, 6553

3 of 112.2. Measurements
2.2.1. Complex Multimorbidity
At baseline, JAGES inquired about the presence of 19 diseases/health conditions.
From this list, two symptoms, “difficulty swallowing” and “difficulty with bowel movement”, were excluded because they did not refer to specific diagnoses. Thus, the study
included 17 diseases in total (Table 1), which were then classified into 9 categories according to the affected body system. CMM was defined as the coexistence of three or morebody system disorders.
Table 1. Definition of CMM.
Body System Disorders
Circulation disorder

Endocrine-metabolic disorder (General system)
Eye disorder
Gastrointestinal disorder
Hearing disorder
Mental and behavioral disorder
Musculoskeletal and connective disorder
Neoplasm
Respiratory disorder
Abbreviation: CMM, Complex multimorbidity.

Surveyed Diseases
Heart disease (including arrhythmia)
StrokeHigh blood pressure
Diabetes (including mild type)Obesity
Dyslipidemia
Impaired vision
Gastrointestinal disease
Liver disease
Impaired hearing
Mental disease
Sleep problem
Osteoporosis
Joint disease/neuralgia
lnjury/fracture
CancerRespiratory disease

2.2.2. Outcome
The outcome of the study was the employment status of older people after a six-year
follow-up period. A questionnaire was administered, and participants replied whether
they were in employment at that time. The type of employment (i.e., full-time, part-time,etc.) was not surveyed.
2.2.3. Equivalized Income
Equivalized income was calculated by dividing household income (Japanese yen;
JPY) by the square root of the number of people in the household and categorizing it intoquartiles (first quartile: JPY 1.25 million per year and lower; second quartile: JPY 1.251–
1.944 million per year; third quartile: JPY 1.945–3.061 million per year; and fourth quartile:
JPY 3.062 million per year and higher). The first quartile is roughly equal to the Japaneserelative poverty line during the JAGES survey period (JPY 1.22 million per year and lower)[14]. JPY was nearly equal to USD 0.01 during the JAGES survey period.
2.2.4. Psychological Factors
To consider the psychological aspects of workers, two variables, ‘ikigai’ and the Geriatric Depression Scale (GDS), were introduced. “Ikigai” refers to the Japanese concept of
purpose in life [15,16], which is a facet of positive psychology. It was asked about on the
JAGES survey in 2013. Many Japanese workers find their purpose in life through work[17], and ‘ikigai’ has been linked with social participation and reduced mortality amongolder people [18,19].

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The GDS is a psychometrically validated scale used to assess depressive symptoms;
the scale consists of 0–15 points, which we then categorized into three groups: not depressed (0–4), mild depression (5–9), and severe depression (10–15) [20].
2.2.5. Covariates
As potential confounders, we selected the following variables: age (in years); sex;
marital status (married, widowed, separated, unmarried, or other); educational attainment (5 years or fewer, 6–9 years, 10–12 years, 13 years or more, or other); equivalized
income (see above); smoking status (nonsmoker, current smoker, or ex-smoker); alcohol
consumption (non-drinker, drinker, or ex-drinker); living alone (yes/no); engagement inleisure activities (yes/no); “ikigai”; depressive symptoms (not depressed, mild depression,or severe depression); and higher-level functional capacity measured by the Tokyo Metropolitan Institute of Gerontology Index of Competence (TMIG-IC), a 13-point scale [21].
2.3. Statistical Analysis
2.3.1. Missing Data
Based on the missing-at-random assumption, we conducted multiple imputations formissing data. We analyzed 20 multiply imputed datasets using a bootstrapping expectation-maximization algorithm and combined all estimators using Rubin’s rule [22,23].
2.3.2. Propensity Score Matching
We used propensity score matching to compare the employment rate among older
adults with and without CMM. To address potential confounding bias, we conducted propensity score matching within the logistic regression model. The propensity score for eachindividual (i.e., the propensity to develop CMM) was estimated from the following variables: age, sex, marital status, educational attainment, equivalized income, smoking status, alcohol consumption, living alone, engagement in leisure activities, “ikigai”, depressive symptoms, higher-level functional capacity, and municipality code. We conducted a
1:1 matching between participants with and without CMM using the nearest-neighbormethod using a caliper width of 0.2 of the standard deviation of the logit of the propensity
score [24,25]. An absolute standardized difference greater than 0.1 was considered a significant imbalance of covariates between the groups (Table 2) [26].
Table 2. Standardized mean differences before and after propensity score matching.
SMD in Multiply
Imputed Data

SMD In Matching Data

Characteristic
Age
0.328
0.018
Sex
0.068
<0.001Marriage
0.058
0.003
Single life
0.019
0.023
TMIG-IC
0.241
0.043
Alcohol consumption
0.077
0.061
Smoking status
0.005
0.047
Engagement for leisure activities
0.045
0.055
Educational attainment
0.072
0.019
Equivalized income
0.051
0.052
Ikigai
0.047
0.027
GDS score
0.358
0.069
Municipality code
0.005
0.013
Abbreviations: GDS, Geriatric Depression Scale; SMD, Standardized mean difference; TMIG-IC, Tokyo Metropolitan Institute of Gerontology Index of Competence.

Int. J. Environ. Res. Public Health 2022, 19, 6553

5 of 11Furthermore, we examined the impact of an alternative MM definition, that is, theconventional approach of counting the number of diseases [4]. While CMM signifies thatthree or more body systems are impaired by diseases, the conventional approach simply
provides a tally of diseases. Using the conventional approach, we assessed the differencein the direction and significance of the results of analysis when participants had two or
more diseases.
Lastly, we compared the effect size of CMM on working status with other predictorsof employment, including SES (income and educational attainment), by using logistic regression analysis. Previous studies have shown that low SES is a risk for retiring before
the statutory retirement age [27,28]. We adjusted for age (in years), sex, marital status(married or widowed/separated/unmarried/other), smoking status (nonsmoker or current
smoker/ex-smoker), alcohol consumption (non-drinker or drinker/ex-drinker), living
alone (yes/no), engagement in leisure activities (yes/no), “ikigai” (yes/no), depressive
symptoms (not depressed, mild depression, and severe depression), and higher-level
functional capacity by TMIG-IC [21].
All statistical analyses were conducted using the R software package (version 4.0.1).
Statistical significance was set at p < 0.05.
3. Results
3.1. Baseline Population Characteristics
Among the study participants, 12% of men and 14% of women had CMM. With age,the prevalence of CMM increased, with 13% of the total population and 18% of those aged
75 years and older suffering from CMM (Table 3). A total of 18% of participants with CMM
and 16% of those without CMM reported incomes at or below levels corresponding to therelative poverty threshold in Japan. Approximately half of the individuals with and without CMM had more than nine years of education.
Table 3. Characteristics of participants at baseline and employment rate after 6 years.
With CMM(n = 726)
Age—no. (%)

Sex—no. (%)
Marital status—no. (%)

Single life—no. (%)

65–69
70–74
75–79
80 and above
Male
FemaleMarriedWidowedSeparated
Unmarried
OthersMissing
Yes
No
Missing

Mean TMIG-IC score (range)
Alcohol consumption—no. (%)

Smoking status—no. (%)

Missing—no. (%)
Nondrinker
Drinker/Ex-drinker
MissingNonsmoker
Current smoker/Ex-smoker
Missing

224 (31)267 (37)282 (39)269 (37)419 (58)
307 (42)554 (76)132 (18)
15 (2)
10 (1)2 (0)
13 (2)
647 (89)
71 (10)8 (1)11.5 (1–13)
88 (12)379 (52)304 (42)43 (6)
350 (48)313 (43)
63 (9)

Without
CMM (n =4887)
2086 (43)1616 (33)749 (15)436 (9)2983 (61)1904 (39)3881 (79)710 (15)173 (4)58 (1)
25 (1)
40 (1)
4385 (90)446 (9)56 (1)
11.8 (0–13)
467 (10)2356 (48)2252 (46)279 (6)2324 (48)2156 (44)407 (8)

Int. J. Environ. Res. Public Health 2022, 19, 6553

6 of 11Engagement for leisure activities—
no. (%)

Educational attainment—no. (%)

Equivalized income a—no. (%)

Ikigai—no. (%)

Depressive symptoms (GDS score)—no. (%)

Yes

367 (51)

2644 (54)No
Missing
5 or fewer
6–9
10–12
13 or more
OthersMissing
Q1: Very Low
Q2: Low
Q3: High
Q4: Very High
Missing
Yes
No
Missing

291 (40)68 (9)
11 (2)
328 (45)249 (34)
119 (16)6 (1)
24 (3)
128 (18)132 (18)151 (21)215 (30)100 (14)94(13)
15(2)617(85)1900 (39)343 (7)37 (1)
2151 (44)1691 (35)944 (19)26 (1)
75 (2)
758 (16)937 (19)1026 (21)1618 (33)548 (11)676(14)77(2)4134(85)

Non-depressed (0–4)

392(54)3418(70)

Mild depression (5–9)
175(24)692(14)
Severe depression (10–15)52(7)150(3)
Missing107(15)627(13)
Employed at follow-up—no. (%)
Yes
214 (29)
1997 (41)No
512 (71)2890 (59)Abbreviations: CMM, Complex multimorbidity; GDS, Geriatric Depression Scale; TIMG-IC, the Tokyo Metropolitan Institute of Gerontology Index of Competence. a Income quartile calculated by allparticipants in the JAGES2010-2016 panel (‘Very Low’–1.250; ‘Low’ 1.251–1.944; ‘High’ 1.945–3.061;‘Very High’ 3.062– million yen per year).

3.2. Outcome
After 1:1 propensity score matching, 725 pairs were included in the analysis. The Cstatistics before the matching for evaluation of the discriminatory ability of the propensity
score model was 0.65 (95% confidence interval (CI): 0.63–0.67) [29]. The populations with
CMM had a 6.4% lower employment rate than those without CMM after the six-year follow-up (29.5% vs. 35.9%; p = 0.012; odds ratio (OR), 1.33 (95% CI: 1.08–1.67)). In addition,
the conventional MM definition (2+ diseases) did not influence the employment rate
(37.2% vs. 39.6%; p = 0.114; OR, 1.10 (95% CI: 0.98–1.25)).
Table 4 shows the impact of risk factors for discontinuing work beyond the retirement age and contrasts the results from logistic regression analysis and propensity-scorematched analysis. The odds ratio for CMM was 1.35 (95%CI: 1.14–1.61). Meanwhile, theOR for income was 1.07 (95% CI: 1.02–1.13) and that for educational attainment was 1.02
(95% CI: 0.94–1.10). The absence of multicollinearity was confirmed, and the interaction
term for each variable was not statistically significant.
Table 4. Risk of discontinuation of working beyond retirement age.
Unmatched Analysis with
Propensity Score
Multivariable Adjustment
Matched Analysis
Odds Ratio for Discontinuation of Working
beyond Retirement Age (95% CI)
CMM
1.35 (1.14–1.61) *1.33 (1.08–1.67) *Educational attainment a
1.02 (0.94–1.10)
Equivalized income b
1.07 (1.02–1.13) *p-values were calculated by using a generalized linear model. The reference group was (1) “5 orfewer years of education” in educational attainment and (1) “Low” in equivalized income. * p < 0.05.

Int. J. Environ. Res. Public Health 2022, 19, 6553

7 of 11Abbreviations: CI, Confidence interval; CMM, Complex multimorbidity; GDS, Geriatric Depression
Scale; a Educational attainment was classified into five categories: (1) 5 years or fewer, (2) 6–9 years,(3) 10–12 years, (4) 13 years or more, and (5) other. b Income quartile calculated by all participantsin the JAGES2010-2016 panel (‘Very Low’ –1.250; ‘Low’ 1.251–1.944; ‘High’ 1.945–3.061; ‘Very High’
3.062– million yen per year).

4. Discussion
Although the employment of older people has been shown to contribute to improved
health outcomes, such as lower mortality [30] and dementia [31], the present study sought
to test the reverse hypothesis, namely, that MM in older people truncates their working
lives. To the best of our knowledge, this is the first study to show that CMM reduces theemployment rate of older people. Our finding implies that providing appropriate support
to patients with CMM may extend their working lives.
In addition, we examined the effect of lower educational attainment and low incomeon the continued employment of older people and found that low income at baseline wasa risk factor for continuing to work after retirement age, although it was not as influential
as CMM (most (Q4) vs. least (Q1) income; OR: 1.20; 95% CI: 1.01–1.43). As for the educational background, there was no statistically significant association with working beyond
retirement age, although this needs to be tested with a larger sample size. Economic disparities among older adults are likely to be amplified by disparities in employment status,
given the incomes earned from work. That is, providing appropriate support to patientswith CMM may be effective in not only extending their working life but also in reducing
economic disparities among older people.
In contrast with previous reports from European countries, depressive symptoms
among Japanese older people tended to be exacerbated by retirement [17], while employment among older people was associated with “ikigai” (higher sense of purpose in life).
[15] However, even after controlling for both “ikigai” and depressive symptoms, CMM
continued to be an independent predictor of working beyond the retirement age.
Further research is needed to examine factors that present barriers for older people
from continuing to work. The impact of MM on working beyond retirement age will vary
according to national differences in social policies (e.g., access to healthcare, legal protections for people with disabilities, and income security in retirement), as well as culturalvariations in the level of social support available to individuals as they approach retirement age. For example, given the same level of MM, an individual living in a society with
low-income security in retirement may be forced to continue working beyond retirement
age compared to someone living in a society with more generous levels of social security.
In Japan, approximately 40% of older people hope to work throughout their lives,and approximately 80% of them hope to work until they are 70 or older [32]. In addition,
the physical fitness test results of older people in their early 70s today are comparable tothose of older people in their late 60s two decades ago [33]. As a result, the image of aging
is changing throughout society. These data may suggest that extending the retirement age
is a realistic possibility. Currently, the retirement age in Japan is 65, which means that theratio of the working-age population (18–64) to the elderly (65+) is 2:1, meaning that 2:1
working-aged people support one older person. In contrast, if the retirement age is set at
75, the ratio will be 2:4 (if not extended, 1:3) in 2065 [34]. Therefore, if the retirement age
can be extended by 10 years, the burden placed on the working-age population by older
people can be reduced, even compared to the current situation.
Work is also beneficial to older people in other ways besides promoting good health.
First, an occupation provides a social role and a social identity, such that the inability tocontinue working has a negative impact on people’s social lives that is distinct from theeconomic impact [35]. Second, increased income from continued employment may contribute to material health investments, which may further promote health among the elderly. Among the G7 countries, Japan has the highest employment rate among the elderly
and the lowest “future replacement rate”, which refers to pension benefits divided by

Int. J. Environ. Res. Public Health 2022, 19, 6553

8 of 11take-home pay before retirement (38.7%) [36]. While pension reform is necessary, it is also
worthwhile to encourage older people to continue their work at the same time. Finally,
ill-health due to disease may reduce the willingness of workers to acquire job skills. In
other words, appropriate interventions in CMM could lead to an increase in a healthy andskilled workforce, ultimately leading to higher productivity and improved human capital,
including health.
There are several limitations to the current study. First, in the current study, we did
not consider the occupational class. However, we controlled for other aspects of socioeconomic status, including income and education [37]. Furthermore, in stratified analysis,
we found that neither lower income nor education level modified the association between
CMM and working status among older people.
Second, work desire was not considered among the reasons why older individuals
chose to continue working beyond retirement age. In other words, the cohort analyzed inthis study is a heterogeneous group, with a mix of people who continued to work because
they wanted to and people who were forced to work for economic reasons. However, in
this study, we found that higher income significantly contributed to the higher employment rate, which implies that the reason for older people to continue working was notdue to financial needs.
Third, in this study, we adopted the CMM concept of impairment across body systems to define MM. For example, while the conventional approach to defining MM would
consider “stroke” and “heart attack” to be separate diseases (yielding an MM score of 2),
the CMM approach collapses them under the single entity, “disorders affecting the circulatory system.” Whilst we believe that the CMM approach better captures the common
etiological pathways underlying co-occurring diseases (as well as their impact on clinicalcare), the method is strongly constrained by the data collected by the researchers. In
JAGES, only 17 diseases were queried on the survey, and as shown in Table 1, among thenine body systems, four of them had only one disease representing that system. However,
CMM inhibited older people from continuing to work beyond retirement age more thanthe conventional definition of MM (2+ diseases).
Finally, although our analysis was based on a longitudinal study design, this study
is observational. Therefore, caution is needed when inferring causality.
The challenge of maintaining the fiscal solvency of social security systems has forced
governments throughout the world to adopt measures such as raising taxes, reducing
benefits, and raising the retirement age. Raising the age at which workers become eligible
for benefits means that workers are often also family caregivers at the same time (e.g.,taking care of sick parents or spouses) [38]. Therefore, for older people to continue towork, it is important to promote the diversification of work patterns, including part-time
as well as full-time work. In future research, it is necessary to conduct an analysis thattakes the type of work into account.
5. Conclusions
CMM had a negative impact on the employment rate of older adults in Japan. Extending the working lives of older people will require appropriate support for individuals
with CMM.
Author Contributions: Conceptualization, D.K., I.K. and N.K.; Formal analysis, D.K. and I.K.; Methodology, D.K. and I.K.; Project administration, D.K.; Supervision, I.K.; Visualization, D.K.; Writing—original draft, Daisuke Kato; Writing—review and editing, I.K. and N.K. All authors have readand agreed to the published version of the manuscript.
Funding: This study used data from JAGES (the Japan Gerontological Evaluation Study). This study
was supported by Grantin-Aid for Scientific Research (15H01972, 15H04781, 15H05059, 15K03417,
15K03982, 15K16181, 15K17232, 15K18174, 15K19241, 15K21266, 15KT0007, 15KT0097, 16H05556,
16K09122, 16K00913, 16K02025, 16K12964, 16K13443, 16K16295, 16K16595, 16K16633, 16K17256,
16K17281, 16K19247, 16K19267, 16K21461, 16K21465, 16KT0014, 17K04305, 17K04306, 25253052,

Int. J. Environ. Res. Public Health 2022, 19, 6553

9 of 1125713027, 26285138, 26460828, 26780328, 18H03018, 18H04071, 18H03047, 18H00953, 18H00955,
18KK0057, 19H03901, 19H03915, 19H03860, 19K04785, 19K10641, 19K11657, 19K19818, 19K19455,
19K24060, 19K20909, 20H00557) from JSPS (Japan Society for the Promotion of Science); Health Labour Sciences Research Grants (H26-Choju-Ippan-006, H27-Ninchisyou-Ippan-001 H28- Choju-Ippan-002, H28- Ninchisyou-Ippan-002, H30-Kenki-Ippan-006,H29-Chikyukibo-Ippan-001, H30Jyunkankinado-Ippan-004, 19FA1012, 19FA2001, 21FA1012, 21K19635), Research project on healthand welfare promotion for the elderly from the Ministry of Health, Labour and Welfare, Japan; theResearch and Development Grants for Longevity Science from Japan Agency for Medical Researchand development (AMED) (JP18dk0110027, JP18ls0110002, JP18le0110009, JP20dk0110034,
JP21lk0310073, JP21dk0110037), the Research Funding for Longevity Sciences from National Center
for Geriatrics and Gerontology (24-17, 24-23, 29-42, 30-30, 30-22, 20-19, 21-20); Open Innovation Platform with Enterprises, Research Institute and Academia (OPERA, JPMJOP1831) from the Japan Science and Technology (JST); a grant from the Japan Foundation For Aging And Health (J09KF00804),
a grant from Innovative Research Program on Suicide Countermeasures (1-4), a grant from Sasakawa Sports Foundation, a grant from Japan Health Promotion & Fitness Foundation, a grant from
Chiba Foundation for Health Promotion & Disease Prevention, the 8020 Research Grant for fiscal
2019 from the 8020 Promotion Foundation (adopted number: 19-2-06), grants from Meiji Yasuda
Life Foundation of Health and Welfare. The views and opinions expressed in this article are thoseof the authors and do not necessarily reflect the 42 official policy or position of the respective funding organizations.
Institutional Review Board Statement: The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Chiba University Faculty of Medicine(No. 2493) and the National Center for Geriatrics and Gerontology (No. 992).
Informed Consent Statement: The JAGES participants were informed that their participation in
the survey was voluntary and their consent to participate was shown by returning the questionnaireby post.
Data Availability Statement: The datasets of the Japan Gerontological Evaluation Study (JAGES),
which were used in this research, are available from the corresponding author upon reasonable
request. All inquiries should be addressed to the data management committee via e-mail:dataadmin.ml@jages.net. All JAGES datasets have ethical or legal restrictions for public deposition
due to the inclusion of sensitive information from the human participants. Following the regulationsof local governments which cooperated in the survey, the JAGES data management committee has
imposed restrictions upon the data.
Acknowledgments: The authors appreciate all the staff who conducted the surveys, all survey participants, and JAGES group members.
Conflicts of Interest: The authors declare no conflicts of interest.

References
1.
2.
3.

4.
5.
6.
7.
8.

Hung, W.W.; Ross, J.S.; Boockvar, K.S.; Siu, A.L. Recent trends in chronic disease, impairment and disability among older adults
in the United States. BMC Geriatr. 2011, 11, 47. https://doi.org/10.1186/1471-2318-11-47.
Harper, S.; American Association for the Advancement of Science. Economic and social implications of aging societies. Science
2014, 346, 587–591.
Vos, T.; Barber, R.M.; Bell, B.; Bertozzi-Villa, A.; Biryukov, S.; Bolliger, I.; Charlson, F.; Davis, A.; Degenhardt, L.; Dicker, D.; etal. Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases andinjuries in 188 countries, 1990–2013: A systematic analysis for the Global Burden of Disease Study 2013. Lancet 2015, 386, 743–
800. https://doi.org/10.1016/s0140-6736(15)60692-4.
Xu, X.; Mishra, G.D.; Jones, M. Evidence on multimorbidity from definition to intervention: An overview of systematic reviews.
Ageing Res. Rev. 2017, 37, 53–68. https://doi.org/10.1016/j.arr.2017.05.003.
Marengoni, A.; Angleman, S.; Melis, R.; Mangialasche, F.; Karp, A.; Garmen, A.; Meinow, B.; Fratiglioni, L. Aging withmultimorbidity: A systematic review of the literature. Ageing Res. Rev. 2011, 10, 430–439. https://doi.org/10.1016/j.arr.2011.03.003.
Pathirana, T.I.; Jackson, C.A. Socioeconomic status and multimorbidity: A systematic review and meta-analysis. Aust. N. Z. J.
Public Health 2018, 42, 186–194. https://doi.org/10.1111/1753-6405.12762.
Harrison, C.; Britt, H.; Miller, G.; Henderson, J.; Examining different measures of multimorbidity, using a large prospective
cross-sectional study in Australian general practice. BMJ Open 2014, 4, 4694. https://doi.org/10.1136/bmjopen-2013-004694.
Kato, D.; Kawachi, I.; Saito, J.; Kondo, N.; Complex multimorbidity and mortality in Japan: A prospective propensity-matchedcohort study. BMJ Open 2021, 11, e046749. https://doi.org/10.1136/bmjopen-2020-046749.

Int. J. Environ. Res. Public Health 2022, 19, 6553

9.
10.

11.
12.
13.
14.
15.
16.
17.
18.

19.

20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.

34.

10 of 11

Storeng, S.H.; Vinjerui, K.H.; Sund, E.R.; Krokstad, S. Associations between complex multimorbidity, activities of daily living
and mortality among older Norwegians. A prospective cohort study: The HUNT Study, Norway. BMC Geriatr. 2020, 20, 1–8.
https://doi.org/10.1186/s12877-020-1425-3.
Warner, D.F.; Schiltz, N.K.; Stange, K.C.; Given, C.W.; Owusu, C.; Berger, N.A.; Koroukian, S.M. Complex multimorbidity andhealth outcomes in older adult cancer survivors. Fam. Med. Community Health 2017, 5, 129–138.
https://doi.org/10.15212/fmch.2017.0127.
Kato, D.; Kawachi, I.; Saito, J.; Kondo, N. Complex Multimorbidity and Incidence of Long-Term Care Needs in Japan: A
Prospective Cohort Study. Int. J. Environ. Res. Public Health 2021, 18, 10523. https://doi.org/10.3390/ijerph181910523.
Head, A.; Fleming, K.; Kypridemos, C.; Schofield, P.; Pearson-Stuttard, J.; O’Flaherty, M. Inequalities in incident and prevalentmultimorbidity in England, 2004–2019: A population-based, descriptive study. Lancet Health Longev. 2021, 2, e489–97.
https://doi.org/10.1016/S2666-7568(21)00146-X.
Kondo, K. Progress in aging epidemiology in Japan: The JAGES project. J. Epidemiol. 2016, 26, 331–336.
Ministry of Health Labour and Welfare. Comprehensive Survey on Living Condisions 2019. 2020. Available online:
https://www.mhlw.go.jp/toukei/saikin/hw/k-tyosa/k-tyosa19/index.html (accessed on 21 June 2021). (In Japanese).
Shirai, K.; Iso, H.; Fukuda, H.; Toyoda, Y.; Takatorige, T.; Tatara, K. Factors associated with “Ikigai” among members of a public
temporary employment agency for seniors (Silver Human Resources Centre) in Japan; Gender differences. Health Qual. Life
Outcomes 2006, 4, 1–6. https://doi.org/10.1186/1477-7525-4-12.
Noriyuki, N. ‘Ikigai’ in older Japanese people. Age Ageing 1999, 28, 323–324. https://doi.org/10.1093/ageing/28.3.323.
Shiba, K.; Kondo, N.; Kondo, K.; Kawachi, I. Retirement and mental health: Dose social participation mitigate the association?
A fixed-effects longitudinal analysis. BMC Public Health 2017, 17, 1–10. https://doi.org/10.1186/s12889-017-4427-0.
Okuzono, S.S.; Shiba, K.; Kim, E.S.; Shirai, K.; Kondo, N.; Fujiwara, T.; Kondo, K.; Lomas, T.; Trudel-Fitzgerald, C.; Kawachi, I.;et al. Ikigai and subsequent health and wellbeing among Japanese older adults: Longitudinal outcome-wide analysis. Lancet
Reg. Health-West. Pacific 2022, 21, 100391. https://doi.org/10.1016/j.lanwpc.2022.100391.
Tanno, K.; Sakata, K.; Ohsawa, M.; Onoda, T.; Itai, K.; Yaegashi, Y.; Tamakoshi, A.; JACC Study Group. Associations of ikigai
as a positive psychological factor with all-cause mortality and cause-specific mortality among middle-aged and elderly Japanesepeople: Findings from the Japan Collaborative Cohort Study. J. Psychosom. Res. 2009, 67, 67–75.
https://doi.org/10.1016/j.jpsychores.2008.10.018.
Sheikh, J.I.; Yesavage, J.A. Geriatric Depression Scale (GDS): Recent evidence and development of a shorter version. Clin.
Gerontol. 1986, 5, 165–173. https://doi.org/10.1300/j018v05n01_09.
Koyano, W.; Shibata, H.; Nakazato, K.; Haga, H.; Suyama, Y. Measurement of competence: Reliability and validity of the TMIG
Index of Competence. Arch. Gerontol. Geriatr. 1991, 13, 103–116. https://doi.org/10.1016/0167-4943(91)90053-s.
Honaker, J.; King, G.; Blackwell, M. Amelia II: A program for missing data. J. Stat. Softw. 2011, 45, 1–47.
Rubin, D.B. Multiple Imputation after 18+ Years. J. Am. Stat. Assoc. 1996, 91, 473–489. https://doi.org/10.18637/jss.v045.i07.
Austin, P.C. The relative ability of different propensity score methods to balance measured covariates between treated anduntreated subjects in observational studies. Med. Decis. Mak. 2009, 29, 661–677. https://doi.org/10.1177/0272989x09341755.
Austin, P.C. Optimal caliper widths for propensity-score matching when estimating differences in means and differences inproportions in observational studies. Pharm. Stat. 2011, 10, 150–161. https://doi.org/10.1002/pst.433.
Austin, P.C. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensityscore matched samples. Stat. Med. 2009, 28, 3083–3107. https://doi.org/10.1002/sim.3697.
Beehr, T.A.; Glazer, S.; Nielson, N.L.; Farmer, S.J. Work and Nonwork Predictors of Employees’ Retirement Ages. J. Vocat. Behav.
2000, 57, 206–225. https://doi.org/10.1006/jvbe.1999.1736.
Wang, M.; Shultz, K.S. Employee retirement: A review and recommendations for future investigation. J. Manag. 2010, 36, 172–206. https://doi.org/10.1177%2F0149206309347957.
Westreich, D.; Cole, S.R.; Funk, M.J.; Brookhart, M.A.; Stürmer, T. The role of the c-statistic in variable selection for propensity
score models. Pharmacoepidemiol. Drug Saf. 2011, 20, 317–320. https://doi.org/10.1002/pds.2074.
Sorlie, P.D.; Rogot, E. Mortality by employment status in the national longitudinal mortality study. Am. J. Epidemiol. 1990, 132,
983–992. https://doi.org/10.1093/oxfordjournals.aje.a115741.
Tomioka, K.; Kurumatani, N.; Hosoi, H. Beneficial effects of working later in life on the health of community-dwelling older
adults. Geriatr. Gerontol. Int. 2018, 18, 308–314. https://doi.org/10.1111/ggi.13184.
Office, C. Survey on Attitudes of Older People toward Daily Life (2014). 2015. Available online:
https://www8.cao.go.jp/kourei/ishiki/h26/sougou/zentai/index.html (accessed on 15 July 2021). (In Japanese).
Ministry of Education Culture Sports Science and Technology. Summary and Report on the Results of Physical Fitness andExercise
Capacity
Survey(2019).
2020.
Available
online:https://www.mext.go.jp/sports/b_menu/toukei/chousa04/tairyoku/kekka/k_detail/1421920_00001.htm (accessed on 15 July2021). (In Japanese).
National Institute of Population and Social Security Research. Japan’s Future Population Projections (April 2017 Estimates).
2017. Available online: http://www.ipss.go.jp/pp-zenkoku/j/zenkoku2017/pp_zenkoku2017.asp (accessed on 15 July 2021). (InJapanese).

Int. J. Environ. Res. Public Health 2022, 19, 6553

35.
36.
37.

38.

11 of 11

Vauclair, C.M.; Marques, S.; Lima, M.L.; Bratt, C.; Swift, H.J.; Abrams, D. Subjective social status of older people across countries:
The role of modernization and employment. J. Gerontol.-Ser. B Psychol. Sci. Soc. Sci. 2015, 70, 650–660.
https://doi.org/10.1093/geronb/gbu074.
OECD. Pensions at a Glance 2021; OECD: Paris, France, 2021.
Regidor, E.; Ronda, E.; Martínez, D.; Calle, M.E.; Navarro, P.; Domínguez, V. Occupational social class and mortality in a
population of men economically active: The contribution of education and employment situation. Eur. J. Epidemiol. 2005, 20,
501–508. https://doi.org/10.1007/s10654-005-4262-y.
Pickard, L. The Effectiveness and Cost-Effectiveness of Support and Services to Informal Carers of Older People; Literuature review for
Audit Commissioning; London School of Economics: London, UK, 2004.