Open access

Original research

Daisuke Kato ‍ ‍,1 Ichiro Kawachi,2 Junko Saito ‍ ‍,3 Naoki Kondo4

To cite: Kato D, Kawachi I,
Saito J, et al. Complex
multimorbidity and mortality
in Japan: a prospective
propensity-­matchedcohort study. BMJ Open
2021;11:e046749. doi:10.1136/
bmjopen-2020-046749►► Prepublication history andadditional supplemental material
for this paper are available
online. To view these files,
please visit the journal online.
(http://​dx.​doi.​org/​10.​1136/​
bmjopen-​2020-​046749).

Received 09 November 2020
Accepted 22 July 2021

© Author(s) (or theiremployer(s)) 2021. Re-­use
permitted under CC BY-­NC. No
commercial re-­use. See rights
and permissions. Published by
BMJ.
1

Department of Family Medicine,Mie University Graduate Schoolof Medicine Faculty of Medicine,Tsu, Mie, Japan2
Department of Social &Behavioral Sciences, Harvard
T.H. Chan School of Public
Health, Boston, Massachusetts,USA
3
Behavioral Science Division,
Center for Public Health
Sciences, National Cancer
Center Japan, Chuo-­ku, Tokyo,
Japan
4
Department of Social
Epidemiology, Graduate Schoolof Medicine and School of Public
Health, Kyoto University, Kyoto,
Japan
Correspondence to
Dr Daisuke Kato;​d.​kato0417@g​ mail.​com

ABSTRACT
Objectives There are limitations to defining multimorbidity(MM) based on a simple count of diseases. To address theselimitations, the concept of complex MM (CMM) focuses on
how many body systems are affected in a single patient,rather than counting comorbid conditions. This study
compared the prediction of mortality among older Japanese
adults between CMM and conventional MM.
Design A population-­based prospective cohort study.
Setting The Japan Gerontological Evaluation Study, anationwide longitudinal cohort study, which ran from 2010
to 2016.
Participants Functionally independent individuals who
were older than 65 and had complete illness data at thetime of baseline survey were eligible.
Outcomes measure CMM was defined as thecoexistence of 3 or more body system disorders atbaseline. We calculated the propensity for each individual
to develop CMM based on a wide array of characteristics,
including socioeconomic status and health behaviours.
Individuals with and without CMM were then matchedon their propensity scores before we estimated overall
survival using a log-­rank test.
Results Our 6-­year follow-­up included 38 889 older
adults: 20 233 (52.0%) and 7565 (19.5%) adults with
MM and CMM, respectively. In the MM-­matched cohort(n=15 666 pairs), the presence of MM was significantly
associated with increased mortality (HR 1.07; 95% CI 1.01
to 1.14; p=0.02 by the log-­rank test). A similar mortality
association was found in the CMM-­matched cohort(n=7524 pairs, HR, 1.07; 95% CI 0.99 to 1.16; p=0.08 by
the log-­rank test).
Conclusion This is the first study to report the association
between CMM and mortality among older adults in Japan.
MM and CMM predict mortality in older adults to a similar
degree. This finding needs to be replicated with more
precision in larger samples.

INTRODUCTION
There are limitations in defining multimorbidity (MM; the co-­occurrence of diseases inthe same person) based on a simple countof diseases,1 and a new concept of ‘complex
MM’ (CMM) has thus been proposed.2 CMM
focuses on the impact across the different
body systems rather than counting comorbid
conditions.

Strengths and limitations of this study►► This is the first study to directly compare the as-sociations between two alternative formulations ofmultimorbidity—conventional multimorbidity andcomplex multimorbidity—and survival in a nationwide cohort of older Japanese adults.
►► We used propensity score matching to minimise
confounding bias when comparing the survival ofindividuals with and without multimorbidity.
►► One limitation is that we did not take into accountthe severity of disease at baseline, which may haveunderestimated the impact of comorbid conditions.

In CMM, diseases are categorised by the
body system they affect. Because impairmentsof the same body system often have similarinterventions, their impacts on patient prognosis are expected to be similar. Therefore,
it makes biological sense to combine closely
related diseases (eg, osteoporosis and fractures) as affecting a single body system (ie,musculoskeletal and connective disorders)rather than counting them as two separate diseases when evaluating the impact
of multiple comorbid conditions. In turn,disorders of different body systems should becounted separately because they need more
complex and extensive treatment, and thetreatment of one disease may adversely affectanother. Furthermore, from a methodological perspective, focusing on body systemdisorders may be more reliable method forcollecting patient self-­report data as patientsare apt to misclassify individual conditions(eg, osteoarthritis vs rheumatoid arthritis or
asthma vs COPD (chronic obstructive pulmonary disease)) but they are unlikely to mistakethe affected body system. CMM is also likely
to be a more reliable method as it may avoid
issues of whether a clinician sees two very
similar diseases as distinct and thereby avoids
the issue of some clinicians recording a single
condition while others record two.

Kato D, et al. BMJ Open 2021;11:e046749. doi:10.1136/bmjopen-2020-046749

1

BMJ Open: first published as 10.1136/bmjopen-2020-046749 on 2 August 2021. Downloaded from http://bmjopen.bmj.com/ on September 1, 2021 by guest. Protected by copyright.

Complex multimorbidity and mortality
in Japan: a prospective propensity-­
matched cohort study

Open access

Flow diagram of the sample group. ADL, activities of daily living.

A growing number of studies have demonstrated thenegative impact of MM on patient outcomes, showing that
MM is associated with mortality, reduced quality of life,
lower physical functioning and so on.1 3–6 In many reports
claiming these associations, researchers have attempted
to weight diseases according to severity. Although the
MM approach is better than conventional medical carethat tends to focus on a single disease at a time, the newconcept of CMM that focuses on multiple body systemdisorders is expected to result in stronger predictions ofpatient outcomes.
There is little evidence on the impact of CMM on
mortality.7 Although functional disability is associatedwith mortality, no previous studies have evaluated theimpact of CMM by considering baseline activities of daily
Table 1 Definition of body system categories in CMM anddiseases surveyed in JAGES
Category

DiseaseCirculation disorder►► Heart disease (including
arrhythmia)►► Stroke►► High blood pressure

Endocrine-­metabolic
disorder (general system)►► Diabetes (including mild type)►► Obesity►► Dyslipidaemia

Eye disorder►► Impaired vision

Gastrointestinal disorder►► Gastrointestinal disease►► Liver disease

Hearing disorder►► Impaired hearing

Mental and behaviouraldisorder►► Mental disease►► Sleep problem

Musculoskeletal andconnective disorder►► Osteoporosis►► Joint disease/neuralgia►► Injury/fracture

Neoplasm►► Cancer

Respiratory disorder►► Respiratory disease

CMM, complex multimorbidity; JAGES, the Japan Gerontological
Evaluation Study2living status. Furthermore, previous reports that include
both MM and CMM mainly performed descriptive statistics, not inferential statistics.8–11 Against this background,
we used CMM and conventional MM to compare thepredictions of mortality among older Japanese adults.

METHODS
Data sources
We conducted this study using the longitudinal nationwide cohort data from the Japan Gerontological Evaluation Study (JAGES),12 which was established in 2010. This
study focuses on adults in Japan over 65 years of age andaims to establish a society of healthy longevity.
Study population
Self-­administered questionnaires for the baseline survey
were mailed to 95 827 older adults in Japan between
August 2010 and January 2012. Adults were sampled from
13 municipalities in 7 of the 47 prefectures in Japan. All
adults were functionally independent, which was defined
as not receiving public long-­term care (LTC) insurance.
The municipalities were from three of the four majorislands of Japan (Hokkaido, Honshu, Kyushu).
Among the target population, 62 426 individuals
responded to the survey (response rate, 65.1%). We
included individuals who were functionally independent and not receiving any nursing care or home careassistance to avoid reverse causality between MM andfunctional disability, which is a key factor in mortality.
We included individuals who had valid ID, sex, and age
information, and who were linked to LTC insurance certification registers. We excluded individuals whose functional disability status at baseline was unknown, or who
were already receiving nursing care or home care assistance, or whose data on the history of present illness was
missing. Finally, we identified a cohort of 38 889 individuals. Further details of the cohort flow diagram are shownin figure 1.
Kato D, et al. BMJ Open 2021;11:e046749. doi:10.1136/bmjopen-2020-046749

BMJ Open: first published as 10.1136/bmjopen-2020-046749 on 2 August 2021. Downloaded from http://bmjopen.bmj.com/ on September 1, 2021 by guest. Protected by copyright.

Figure 1

Open access

Characteristic

With MM

Without MMWith CMM

Without CMM

Sample sizeAge

20 23318 6567565

31 324 65–69

4087

43.4

5328

56.6

1205

12.8

8210

87.2

 70–74

5673

49.7

5745

50.3

1955

17.1

9463

82.9

 75–79

5413

56.7

4134

43.3

2162

22.6

7385

77.4

 80–84

3418

59.2

2352

40.8

1485

25.7

4285

74.3

 85–89

1322

60.3

870

39.7

605

27.6

1587

72.4

 90+

320

58.5

227

41.5

153

28.0

394

72.0

 Missing

0

0

0

0

Sex
 Male

8803

49.3

9038

50.7

3051

17.1

14 79082.9 Female

11 430

54.3

9618

45.7

4514

21.4

16 53478.6 Missing

0

0

0

0

No of natural teeth 20 or more

5979

48.6

6313

51.4

2020

16.4

10 27283.6 10–19

4946

51.3

4686

48.7

1807

18.8

7825

81.2

 1–9

5478

54.5

4582

45.5

2170

21.6

7890

78.4

 No natural teeth

3174

56.1

2484

43.9

1311

23.2

4347

76.8

 Missing

656

52.6

591

47.4

257

20.6

990

79.4

 Less than 6 years

582

58.6

412

41.4

296

29.8

698

70.2

 6–9 years

9812

54.2

8297

45.8

3818

21.1

14 29178.9 10–12 years

6234

49.9

6250

50.1

2247

18.0

10 23782.0 13 years or more

3091

49.1

3208

50.9

997

15.8

5302

84.2

Formal education years Other

125

50.6

122

49.4

52

21.1

195

78.9

 Missing

389

51.5

367

48.5

155

20.5

601

79.5

 Married

13 55550.4

13 328

49.6

4772

17.8

22 111

82.2 Widowed

5124

56.5

3944

43.5

2171

23.9

6897

76.1

 Divorced

652

52.3

594

47.7

255

20.5

991

79.5

 Never married

413

55.5

331

44.5

157

21.1

587

78.9

 Other

108

50.2

107

49.8

48

22.3

167

77.7

 Missing

381

52.0

352

48.0

162

22.1

571

77.9

 Live alone

17 19551.5

16 169

48.5

6300

18.9

27 06481.1 Not alone

2730

56.1

2138

43.9

1159

23.8

3709

76.2

 Missing

308

46.9

349

53.1

106

16.1

551

83.9

 None at all

1858

47.9

2018

52.1

608

15.7

3268

84.3

 Slight

8218

49.6

8357

50.4

2817

17.0

13 75883.0 Moderate

5556

54.2

4701

45.8

2144

20.9

8113

79.1

Marital status

Living arrangement

Financial insecurity (worries about
unexpected expenses) Severe

3431

57.9

2494

42.1

1554

26.2

4371

73.8

 Missing
Receiving pension

1170

51.9

1086

48.1

442

19.6

1814

80.4
Continued

Kato D, et al. BMJ Open 2021;11:e046749. doi:10.1136/bmjopen-2020-046749

3

BMJ Open: first published as 10.1136/bmjopen-2020-046749 on 2 August 2021. Downloaded from http://bmjopen.bmj.com/ on September 1, 2021 by guest. Protected by copyright.

Table 2 Demographic characteristics of the cohort study

Open access

Characteristic

With MM

Without MMWith CMM

Without CMM No

19 191

 Yes

277

51.9

17 779

48.1

7162

19.4

29 808

80.6

57.0

209

43.0

109

22.4

377

77.6

 Missing

765

53.4

668

46.6

294

20.5

1139

79.5

3259

44.6

4055

55.4

952

13.0

6362

87.0

Current employment status Has a paid job Retired

11 34453.0

10 040

47.0

4315

20.2

17 06979.8 Never had a job

2623

56.2

2043

43.8

1125

24.1

3541

75.9

 Missing

3007

54.4

2518

45.6

1173

21.2

4352

78.8

5640

47.8

6164

52.2

1868

15.8

9936

84.2

Alcohol consumption Yes Used to drink

840

57.2

628

42.8

354

24.1

1114

75.9

12 49853.8

10 733

46.2

4844

20.9

18 387

79.1

1255

52.6

1131

47.4

499

20.9

1887

79.1

10 990

52.8

9842

47.2

4195

20.1

16 637

79.9 Stopped smoking 5 or more
years ago

4499

52.4

4086

47.6

1609

18.7

6976

81.3

 Stopped smoking within the past
4 years

913

50.4

899

49.6

334

18.4)

1478

81.6

 Current smoker Missing

1632
2199

46
53.5

1918
1911

54
46.5

546
881

15.4
21.4

3004
3229

84.6
78.6

 No
 MissingSmoking status Never smoked

CMM, complex multimorbidity; MM, multimorbidity

MM and CMM
At baseline, 19 diseases were surveyed in the JAGES.
Among them, as noted in table 1, we analysed thefollowing 17 diseases to calculate MM and CMM: heartdisease (including arrhythmia), stroke, high blood pressure, diabetes (including mild type), obesity, dyslipidaemia, impaired vision, gastrointestinal disease, liverdisease, impaired hearing, mental disease, sleep problems, osteoporosis, joint disease/neuralgia, injury/
fracture, cancer and respiratory disease. The remaining
two symptoms, difficulty swallowing and difficulty withbowel movements, were excluded from the disease list in
this study because they have aspects of dysfunction notdisease. The JAGES did not survey diseases of the nervous
system.
MM was defined as having two or more of the aforementioned diseases concurrently. For CMM, the diseases
surveyed were categorised according to the body systemthey affected.2 13 For example, heart disease and diabetes
were individually categorised into disorders of the circulatory system and endocrine system. Next, CMM wasdefined as the coexistence of 3+ body system disorders atbaseline (see table 1).
OutcomeThe outcome of this study was the 6-­year incidence ofmortality. We ascertained vital status from 2010 to 2016
4by linking the cohort participants to the mortality recordsof the national LTC insurance database (follow-­up
rate=96.2%). The mean follow-­up period was 5.6 years,
and we observed 5183 (13.3%) deaths during the period.
Statistical analysis
Estimation of missing data
Given that the missing data was missing at random, we
conducted multiple imputations using a bootstrapping
Expectation-­Maximisation algorithm.14 We analysed 20
multiply imputed datasets, taking the low missing rateof the cohort (approximately 5%) into consideration.15
Lastly, we combined all estimators by Rubin’s rule.16
Propensity score matching
We used propensity score matching to compare overall
survival among individuals with and without MM/CMM.
To address potential confounding bias, we conducted
propensity score matching within a logistic regression
framework. The participant information included in estimating the propensity score consisted of 44 variables: age,sex, smoking status, alcohol consumption, marital status,
pension, dental health, employment status, consumptionof meat of fish/fruits or vegetable, education, city codeand so on (see online supplemental table S1).
We performed a 1:1 matching between individuals withand without MM/CMM using the nearest-­
neighbour
Kato D, et al. BMJ Open 2021;11:e046749. doi:10.1136/bmjopen-2020-046749

BMJ Open: first published as 10.1136/bmjopen-2020-046749 on 2 August 2021. Downloaded from http://bmjopen.bmj.com/ on September 1, 2021 by guest. Protected by copyright.

Table 2 Continued

Open access

MM

CMM

SMD in multiply
imputed data

SMD in matchingdata

SMD in multiply
imputed data

SMD in matchingdata

Characteristic
 Age

 
 0.24  0.002  0.327  0.025 Sex 0.099 0.001 0.139 0.004 Previous health check-­up 0.01 0.015 0.02 0.005 No of natural teeth 0.11 0.019 0.16 0.005 Consumption of meat and fish  0.009 0.017 0.017 0.016 Consumption of fruits andvegetables 0.003 0.006 0.035 0.012 Formal educational years 0.093 0.045 0.151 0.004 Marital status 0.072 0.015 0.118 0.002 Living arrangement 0.06 0.033 0.1 0.011 Residence type 0.025 0.055 0.058 0.008 Architectural type of home

 0.005 0.086 0.02 0.006 Financial insecurity 0.123 0.004 0.21 0.012 Receiving pension 0.023 0.022 0.022 0.006 Current working status 0.147 0.002 0.225 0.004 Eats meals alone 0.089 0.02 0.17 0.014 Alcohol consumption 0.107 0.015 0.145 0.013 Smoking status 0.079 0.014 0.098 0.016 Falls 0.223 0.004 0.307 0.013 Worries about falls 0.266 0.001 0.396 0.005 Goes upstairs without support  0.265 0.009 0.348 0.005 Gets up out of a chair without  0.251
support 0.02 0.343 0.01 Average time to walk 0.16 <0.001 0.203 0.001 Frequency of going out 0.151 0.015 0.207 0.003 Decrease in the frequency ofgoing out

 0.243 0.001 0.352 0.006 Engagement in leisureactivities 0.105 0.016 0.145 0.008 Trust in neighbours 0.079 0.027 0.135 0.009 Support from neighbours 0.074 0.015 0.109 0.002 Attachment to residence 0.053 0.036 0.086 0.002 Contribution to residence 0.095 0.009 0.129 0.007 Uneasiness about safety inresidence 0.073 0.011 0.105 0.01 Participation in local events 0.085 0.009 0.114 0.008 Interactions withneighbourhood 0.02 0.031 0.049 0.007

Residential environment:

 

 

 

 

 Presence of graffiti or garbage  0.009 0.02 0.019 0.014 Parks or footpaths 0.059 0.045 0.097 <0.001 Locations difficult for walking Risky roads or crossroads for
traffic accidents 0.076 0.044 0.012 0.005 0.132 0.061 0.007 0.002
Continued

Kato D, et al. BMJ Open 2021;11:e046749. doi:10.1136/bmjopen-2020-046749

5

BMJ Open: first published as 10.1136/bmjopen-2020-046749 on 2 August 2021. Downloaded from http://bmjopen.bmj.com/ on September 1, 2021 by guest. Protected by copyright.

Table 3 Standardised mean differences with or without MM/CMM, before and after propensity score matching

Open access

MM

CMM

SMD in multiply
imputed data

SMD in matchingdata

SMD in multiply
imputed data

SMD in matchingdata Aesthetic views or buildings 0.04 <0.001 0.074 0.004 Shops selling fresh fruits andvegetables 0.074 0.023 0.091 0.001 Dangerous place to walk alone  0.013
at night 0.019 0.016 <0.001 Comfortable house or facilities  0.066 0.024 0.107 0.011 Someone who listens to yourconcerns 0.019 0.01 0.075 0.007 Someone to provide care incase of illness 0.049 0.023 0.094 0.026

Attendance

 

 

 

 

 Sports group or club Leisure activity group 0.063 0.06 0.008 0.006 0.117 0.088 0.031 0.007

CMM, complex multimorbidity; MM, multimorbidity; SMD, standardised mean difference

method within a calliper (0.2 of the SD of the logit of thepropensity score).17 18 We evaluated the covariate balance
after matching using standardised differences. An absolute standardised difference of less than 0.1 was considered negligible in the groups (see tables 2 and 3).
Survival data analysis
We estimated the overall survival using Kaplan-­Meiercurves.18 We also compared overall survival between
matched with and without MM/CMM groups using a log-­
rank test.
Sensitivity analysis
While the definition of MM we adopted in this study is
one of the most commonly used definitions in previous
studies,2 we analysed this cohort data with a more sensitive approach. Specifically, we analysed the association
between the number of diseases or body system disorders and the mortality by multivariate analysis with the

Figure 2 Kaplan-­Meier curve for overall survival comparing
patients with and without MM. MM, multimorbidity.

6

covariates used in the propensity score calculation. The
results of this analysis did not change the direction orsignificance of the MM/CMM effect (data not shown).
We used R software packages (V.4.0.1) for all statisticalanalyses, and the statistical significance level was 0.05 for
all analyses.
Patient and public involvement
This was a nationwide cohort study focusing on
community-­dwelling individuals. No patients and the
public were involved in this research.
RESULTS
Baseline population characteristics
Among the current cohort study, 20 233 (52.0%) participants out of 38 889 had MM and 7565 (19.5%) had CMM.
Table 2 presents the demographic characteristics of the

Figure 3 Kaplan-­Meier curve for overall survival
comparing patients with and without CMM. CMM, complex
multimorbidity.
Kato D, et al. BMJ Open 2021;11:e046749. doi:10.1136/bmjopen-2020-046749

BMJ Open: first published as 10.1136/bmjopen-2020-046749 on 2 August 2021. Downloaded from http://bmjopen.bmj.com/ on September 1, 2021 by guest. Protected by copyright.

Table 3 Continued

Open accessMM outcome
After the 1:1 propensity score matching, 31 332 patientswere recruited and evenly classified into propensity-­
matched MM and propensity-­matched non-­MM groups.
The C-­
statistics before matching for evaluation of thediscriminatory ability of the propensity score model was
0.64 (95% CI 0.63 to 0.64).19 The two matched cohorts
were well balanced (see table 3). The populations withMM had a 7% higher mortality than those without MM as
shown in figure 2 (HR 1.07; 95% CI 1.01 to 1.14; p=0.02
by the log-­rank test).
CMM outcome
After the 1:1 propensity score matching, 15 048 patientswere recruited and evenly classified into propensity-­
matched CMM and propensity-­
matched non-­CMM
groups. The C-­statistics before matching for evaluationof the discriminatory ability of the propensity scoremodel was 0.69 (95% CI 0.68 to 0.69).19 The two matchedcohorts were well balanced (see table 3). The populations with CMM had slightly higher mortality than those
without CMM as shown in figure 3 (HR, 1.07; 95% CI 0.99
to 1.16; p=0.08 by the log-­rank test).

DISCUSSION
To the best of our knowledge, this is the first study to report
the association between CMM and mortality among older
adults in Japan. MM and CMM predict mortality in older
adults to a similar degree.
MM is both an individual and a social issue. Low socioeconomic status (SES) individuals develop MM roughly
10–15 years earlier compared with high SES individuals.20 Therefore, to evaluate whether the presence of
MM/CMM is causally related to mortality, SES should beconsidered as a confounding factor. There were larger
intergroup differences in baseline variables for the CMM-­
matched cohort compared with the MM-­matched cohort.
Although CMM was already known to be associated withlower SES,21 the current findings indicate that CMM may
be more closely related to social factors than MM.
We found that the impact of MM and CMM on
mortality was similar. Furthermore, CMM was marginallystatistically significantly associated with mortality. Thismay be partly because the current study did not consider
disease severity or disease status except in the baseline
survey. That is, it may not sufficiently represent body
Kato D, et al. BMJ Open 2021;11:e046749. doi:10.1136/bmjopen-2020-046749

system disorders in terms of the number of disease groups
affected. This finding needs to be replicated with more
precision in larger samples.
There are several limitations to this study. First, the self-­
administered questionnaire was the basis for disease information, which may have led to recall bias. This reporting
error may lead to bias in either direction because its
extent depends on the type of disease and age.22 Second,although the results are based on a nationwide cohort
study, the participants were not nationally representative,
and hence external generalisability is not assured. The
response rate (around 65%) was comparable to that ofother cohort studies for community-­dwelling individuals.
Third, because this study was observational, our findings
cannot be interpreted as indicating causality. Nonetheless, we attempted to minimise confounding bias throughthe use of propensity score matching.18
CONCLUSION
Both MM and CMM predicted future mortality amongolder adults in Japan. These findings indicate the importance of the interactive effects of multiple diseases.
Acknowledgements The authors are grateful to all of the staff who conducted thebaseline and follow-­up surveys, all survey participants, and JAGES group members.
Contributors All authors (DK, IK, JS and NK) made contributions to the conception
and design of this study. DK analysed the data and authored the paper. IK checked
the results and advised edits. All authors agreed on the final version of thismanuscript.
Funding This study used data from JAGES (the Japan Gerontological Evaluation
Study). This study was supported by the Japan Society for the Promotion of
Science, KAKENHI Grant (JP15H01972), Health Labour Sciences Research Grant(H28-­Choju-­Ippan002), Japan Agency for Medical Research and Development(JP17dk0110017, JP18dk0110027, JP18ls0110002, JP18le0110009,
JP20dk0110034, JP20dk0110037), Open Innovation Platform with Enterprises,
Research Institute and Academia (OPERA, JPMJOP1831) from the Japan Scienceand Technology (JST), a grant from Innovative Research Program on Suicide
Countermeasures(1-4), a grant from Sasakawa Sports Foundation, a grant fromJapan Health Promotion & Fitness Foundation, a grant from Chiba Foundation for
Health Promotion & Disease Prevention, the 8020 Research Grant for fiscal 2019from the 8020 Promotion Foundation (adopted number: 19-2-06), a grant from
Niimi Universit (1915010), grants from Meiji Yasuda Life Foundation of Health andWelfare and the Research Funding for Longevity Sciences from National Center for
Geriatrics and Gerontology (29-42, 30-22).
Competing interests None declared.
Patient consent for publication Not required.
Ethics approval JAGES participants were informed that participation was
voluntary and that their consent to participate in the study was shown by returning
the questionnaire via mail. The Nihon Fukushi University Ethics Committee (no.
10-5), National Center for Geriatrics and Gerontology (no. 992-2), and Chiba
University Ethics Committee (no. 2493) approved the parent JAGES protocol.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement Data are available on reasonable request. Data arefrom the JAGES study. All inquiries are to be addressed to the data management
committee via email: ​dataadmin.​ml@​jages.​net. All JAGES datasets have ethical andlegal restrictions for public deposition due to the inclusion of sensitive informationfrom human participants.
Supplemental material This content has been supplied by the author(s). It has
not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been
peer-­reviewed. Any opinions or recommendations discussed are solely thoseof the author(s) and are not endorsed by BMJ. BMJ disclaims all liability andresponsibility arising from any reliance placed on the content. Where the content

7

BMJ Open: first published as 10.1136/bmjopen-2020-046749 on 2 August 2021. Downloaded from http://bmjopen.bmj.com/ on September 1, 2021 by guest. Protected by copyright.

cohort study. Table 3 summarises the background characteristics of the participants between the two groups before
and after matching. Populations with MM/CMM weremore likely to be older, were more likely to have fewer
teeth, and were more vulnerable to financial insecurity(worries about unexpected expenses) compared withthose without MM/CMM. Furthermore, compared withpopulations with MM, populations with CMM were more
likely to be female, to have lower education, to eat meals
alone and to be unmarried.

Open access

Open access This is an open access article distributed in accordance with theCreative Commons Attribution Non Commercial (CC BY-­NC 4.0) license, which
permits others to distribute, remix, adapt, build upon this work non-­commercially,and license their derivative works on different terms, provided the original work is
properly cited, appropriate credit is given, any changes made indicated, and the use
is non-­commercial. See: http://​creativecommons.​org/​licenses/​by-​nc/​4.​0/.
ORCID iDs
Daisuke Kato http://​orcid.​org/​0000-​0002-2​ 449-​0548
Junko Saito http://​orcid.​org/​0000-​0002-6​ 923-​7298

REFERENCES

1 Violan C, Foguet-­Boreu Q, Flores-­Mateo G, et al. Prevalence,
determinants and patterns of multimorbidity in primary care:a systematic review of observational studies. PLoS One
2014;9:e102149.
2 Johnston MC, Crilly M, Black C, et al. Defining and measuringmultimorbidity: a systematic review of systematic reviews. Eur J
Public Health 2019;29:182–9.
3 Wei MY, Mukamal KJ. Multimorbidity, mortality, and long-­term
physical functioning in 3 prospective cohorts of community-­dwelling
adults. Am J Epidemiol 2018;187:103–12.
4 John PDS, Tyas SL, Menec V. Multimorbidity, disability, andmortality in community-­dwelling older adults. Can Fam Physician
2014;60:272–80.
5 Tooth L, Hockey R, Byles J, et al. Weighted multimorbidity indexes
predicted mortality, health service use, and health-­related quality oflife in older women. J Clin Epidemiol 2008;61:151–9.
6 Nunes BP, Flores TR, Mielke GI, et al. Multimorbidity and mortality inolder adults: a systematic review and meta-­analysis. Arch Gerontol
Geriatr 2016;67:130–8.
7 Storeng SH, Vinjerui KH, Sund ER, et al. Associations between
complex multimorbidity, activities of daily living and mortality amongolder Norwegians. A prospective cohort study: the HUNT study,
Norway. BMC Geriatr 2020;20:1–8.

8

8 Harrison C, Henderson J, Miller G, et al. The prevalence of diagnosed
chronic conditions and multimorbidity in Australia: a method for
estimating population prevalence from general practice patientencounter data. PLoS One 2017;12:e0172935.
9 Singer L, Green M, Rowe F, et al. Social determinants ofmultimorbidity and multiple functional limitations among the ageing
population of England, 2002–2015. SSM - Popul Heal 2019;8:1–9.
10 Harrison C, Henderson J, Miller G, et al. The prevalence of complex
multimorbidity in Australia. Aust N Z J Public Health 2016;40:239–44.
11 Lujic S, Simpson JM, Zwar N, et al. Multimorbidity in Australia:
comparing estimates derived using administrative data sources andsurvey data. PLoS One 2017;12:e0183817.
12 Kondo K. Progress in aging epidemiology in Japan: the JAGES
project. J Epidemiol 2016;26:331–6.
13 Linn BS, Linn MW, Gurel L. Cumulative illness rating scale. J Am
Geriatr Soc 1968;16:622–6.
14 Honaker J, King G, Blackwell M. A program for missing data. J Stat
Softw 2011;45:1–47.
15 Graham JW, Olchowski AE, Gilreath TD. How many imputations arereally needed? some practical clarifications of multiple imputation
theory. Prev Sci 2007;8:206–13.
16 Rubin DB. Multiple imputation after 18+ years. J Am Stat Assoc
1996;91:473–89.
17 Rosenbaum PR, Rubin DB. The central role of the propensity score in
observational studies for causal effects. Biometrika 1983;70:41–55.
18 Austin PC. The use of propensity score methods with survival
or time-­to-­event outcomes: reporting measures of effectsimilar to those used in randomized experiments. Stat Med
2014;33:1242–58.
19 Westreich D, Cole SR, Funk MJ, et al. The role of the c-­statistic invariable selection for propensity score models. Pharmacoepidemiol
Drug Saf 2011;20:317–20.
20 Barnett K, Mercer SW, Norbury M, et al. Epidemiology ofmultimorbidity and implications for health care, research, andmedical education: a cross-­sectional study. Lancet 2012;380:37–43.
21 Vinjerui KH, Bjerkeset O, Bjorngaard JH, et al. Socioeconomic
inequalities in the prevalence of complex multimorbidity in a
Norwegian population: findings from the cross-­sectional HUNT
study. BMJ Open 2020;10:e036851.
22 Fortin M, Haggerty J, Sanche S, et al. Self-­Reported versus healthadministrative data: implications for assessing chronic illness
burden in populations. A cross-­sectional study. CMAJ Open2017;5:E729–33.

Kato D, et al. BMJ Open 2021;11:e046749. doi:10.1136/bmjopen-2020-046749

BMJ Open: first published as 10.1136/bmjopen-2020-046749 on 2 August 2021. Downloaded from http://bmjopen.bmj.com/ on September 1, 2021 by guest. Protected by copyright.

includes any translated material, BMJ does not warrant the accuracy and reliabilityof the translations (including but not limited to local regulations, clinical guidelines,
terminology, drug names and drug dosages), and is not responsible for any error
and/or omissions arising from translation and adaptation or otherwise.