Ishikawa et al. BMC Public Health (2016) 16:394
DOI 10.1186/s12889-016-3082-1

RESEARCH ARTICLE

Open AccessSocial participation and mortality: doessocial position in civic groups matter?
Yoshiki Ishikawa1,2*, Naoki Kondo1, Katsunori Kondo3,4, Toshiya Saito2, Hana Hayashi5,6, Ichiro Kawachi6and for the JAGES group

Abstract
Background: Social participation is known to predict longevity. However, little is known about the effect of social
participation according to an individual’s position in civic groups. We evaluated the influence of social position on
mortality, using data from a large cohort of Japanese older adults (the AGES cohort).
Methods: Of 14,804 individuals aged 65 years and older enrolled in the AGES, 14,286 individuals were followed up
for approximately 5 years from 2003 to 2008. We performed inverse probability of treatment weighted (IPTW) Cox
proportional hazards regression with multiple imputation of missing values to compute hazard ratios (HR) for
all-cause mortality according to the individual’s position in the community organization(s) to which they
belonged. We examined participation in the following civic groups: neighborhood association/senior citizen
club/fire-fighting team, religious group, political organization or group, industrial or trade association, volunteer group,
citizen or consumer group, hobby group, and sports group or club. The values for IPTW were computed based on
demographic variables, socioeconomic status, and self-reported medical condition.
Results: During 22,718 person-years of follow-up for regular members of community groups and 14,014 person-years
of follow-up for participants in leadership positions, 479 deaths and 214 deaths were observed, respectively. Relative toregular members, crude HR for all-cause mortality for occupying leadership positions (e.g. president, manager,or having administrative roles) was 0.72 (95 % CI:0.62–0.85). The IPTW-HR was 0.88 (95 % CI: 0.79–0.99) for
participants occupying leadership positions.
Conclusions: Holding leadership positions in community organization(s) may be more beneficial to healththan being regular members.
Keywords: Japan, Social participation, Older people, Mortality, Leadership role, Propensity score

BackgroundAs population aging has advanced in many countries
worldwide, the maintenance of physical, cognitive, andsocial competencies of older adults has become a criticaltarget of public health interventions. Mounting empiricalevidence has suggested that the promotion of participation in community organizations is health-promoting.
For example, social participation leads to lower mortality
rates [1, 2], less depressive symptoms [3], and higher
cognitive functioning [4] among older adults. These
positive health effects may be attributable to the* Correspondence: ishikun@gmail.com
1
Department of Health and Social Behavior, School of Public Health, The
University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 1130033, Japan
2
Cancer Scan, Shibuya-ku, Tokyo, Japan
Full list of author information is available at the end of the article

psychosocial benefits of social participation including
enhancing self-esteem, creating purpose in life, andstress buffering [5, 6]. However, the magnitude of the
impact of social participation on health varies acrossstudies [7]; including some evidence of negative impacts
on health [8–10]. As such, further understanding of themechanism through which social participation leads tobetter health is warranted.
A second strand of research suggests that an individual’s status within a social hierarchy is relevant for
health. According to Marmot et al, individuals occupyinga higher status within an organization enjoy advantages
in terms of not only material but also psychosocial resources [11]. For example, in a company setting, managers may have, in addition to higher average income,© 2016 Ishikawa et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Ishikawa et al. BMC Public Health (2016) 16:394

psychosocial benefits stemming from the exercise of
supervisory authority as well as higher prestige. On theother hand, the possibility of reverse causality cannot bedenied. For example, Boyce and Oswald reported thatthose with good health were more likely to be promoted[12]. In order to avoid this healthy survivor effect, somestudies have used data on the prestigious award winners
and nominees. For example, winning Oscar Award andNobel Prize was associated with 4.2 years [13] and1.6 years [14] of longevity, respectively. Since the winners and nominees can be considered as relatively
homogeneous, the effect of award or higher status canbe regarded as exogenous shock which enables accurate
estimation of the effect of status on longevity.
However, to our knowledge, there is a dearth of
empirical evidence on the effect of status within civic
groups on longevity among older adults. In order toaddress this research gap, we use data from a large
cohort of Japanese older adults which previously
showed that social participation was associated withlower all-cause mortality [2]. Our hypothesis is thatover and above social participation, those who participate in a civic group with leadership positions (e.g.
president, manager, or having administrative roles)
may enjoy greater health benefits compared to regularparticipants.

Page 2 of 9

Evaluation Study (JAGES). The JAGES is an on-going
longitudinal panel study which seeks to elucidate thesocial determinants of functional decline, cognitive
impairment, and mortality among older adults aged
65 years and older. Figure 1 illustrates the flow chartof the study participants for this study. One wave ofdata were collected for this study in 2003. Of 29,374
people invited to participate, 14,804 people completed
the questionnaire, corresponding to a response rate of
50.4 %. Those who did not respond with the baseline
questionnaire were younger than 80 years old andwere more likely to have higher household income,while there was no difference between men andwomen. Of the 14,804 respondents, we included
10,271 individuals in the present analysis, because 91
individuals were excluded due to deaths or functional
disability before beginning of the follow-up, 45 individuals were excluded due to missing of the linkage
variable before beginning of the follow-up, a further
382 individuals did not respond to questions on age
or sex, and 4015 individuals did not join any civic organizations at the beginning of the follow-up. Detailed
information on the design and data collection of the
AGES baseline survey is available elsewhere [15, 16].

Ethics

Methods
Data source

We used data from the Aichi Gerontological Evaluation
study (AGES), which is a part of Japan Gerontological

The JAGES protocol was reviewed and approved by
the ethics committee in Research of Human Subjects
at Nihon Fukushi University and has been carried out
in compliance with Helsinki Declaration.

Individuals sampled for thebaseline survey.
N=29,374

Individuals responded to thebaseline survey.
n=14,804
91 deaths or functional disability before
beginning of the follow-up.
45 missing of the linkage.
Individuals enrolled in thesurvival follow-up from Nov. 2003.
n=14,668

Individuals included in the analysis.
n=10,271

382 individuals who did not respond toquestions on age or sex.
4,015 individuals who did not join any
organizations at the beginning of thefollow-up.

All-cause mortality data until May 2008
were collected.
n=10,271

Fig. 1 Flow chart of the study participants of the Aichi Gerontological Evaluation Study (AGES), 2003–2008

Ishikawa et al. BMC Public Health (2016) 16:394

Measurements
Dependent variable

All-cause mortality data until May 2008 were obtained
from the six municipalities participating in the AGES
cohort and were linked by the researchers using theidentification number which was assigned to each study
participant. One of the strength of the AGES cohort isthat there is no administrative loss during approximately
5-year follow-up.
Independent variable

Social participation was assessed by the statement“Do you belong to the following organization orgroup?(yes/no)” We inquired about the following
types of community organizations: neighborhood association/senior citizen club/fire-fighting team, religious
group, political organization or group, industrial ortrade association, volunteer group, citizen or consumer group, hobby group, and sports group or club.
This questionnaire on social participation in the
AGES study was derived from JGSS (Japanese GeneralSocial Survey) to make the results of the study comparable to the general population.
For those who participated in one of the above organizations or groups, the respondent’s position in the participating organization was assessed by a dichotomous(yes/no) variable with the statement: “Do you serve as
the head, manager or treasurer in the organization orgroup?” Those who answered “yes” were categorized as“leadership position” in terms of their social positions in
the participating organization, whereas those who answered “no” were categorized as “regular” members.
Covariates

Demographic variables included sex, age (65–69, 70–74,
75–79, 80–84, 85 years and over), marital status (married and their spouse was alive, other), and residence
year (less than 5, 5–9, 10–19, 20–29, 30-39, 40-49,
50 years and more). Socioeconomic status included annual equivalized household income (less than 19,999
JPY, 20,000 JPY to 39,999 JPY”, 40,000 JPY or more”)
and educational attainment (less than 6, 6–9, 10–12,
13 years and more, others). In Japan, six-three-three system of education is employed: 6 years of elementary
school; 3 years of junior high school; and 3 years of highschool.
In order to account for the health endowment effect,self-reported medical condition (No illness, Having
illness but need no treatment, Having illness but
discontinued treatment, Receiving some treatment),
self-reported health status (Very good, Good, Poor,
Very poor), self-reported physical condition (Very good,
Good, Poor, Very poor), and Depression (GeriatricDepression Scale) [17] were considered as covariates.

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Statistical analysis

First, we evaluated the differences in mortality between
the groups according to the participants’ baseline characteristics. Next, in order to address potential treatment
selection bias due to the differential chances of occupying leadership position within the organizations, weused a propensity score approach in an attempt to create pseudo-populations based on the probability of assignment to treatment for estimating the causal effect.
We calculated the propensity score using a logistic regression model, where the dependent variable was the
position in the community organization: regular members vs. leadership position. For explanatory variables,
we selected important variables that potentially affectassignment to treatment (in this instance, occupying aleadership position within an organization), namely,
age, sex, annual equivalized household income, educational attainment, marital status, self-reported health,self-reported physical function, self-reported medical
conditions, Geriatric Depression Scale, and residence
year. After examining whether weighting balanced measured covariates between groups (Table 2), the inverseof the propensity score was incorporated to theweighted Cox proportional hazards models to calculatehazard ratios for mortality according to the social position occupied within the organization(s). This approach is an alternative to implementing propensity
score matching to statistically balance for confounding
variables in non-randomized studies [18, 19].
The overall proportion of missing data was 36.3 %, if
we excluded participants who had missing data on atleast one of the variables considered in our analysis. As
such, missing data on independent variable and covariates were imputed by multiple imputation with method
under the missing at random assumption. Multiple imputations based on multivariate normal model was calculated using all the covariates as explanatory variables:
age, sex, annual equivalized household income, educational attainment, marital status, self-reported health,self-reported physical function, self-reported medical
condition, Geriatric Depression Scale, residence year andposition in the participating organization(s). We produced ten imputed data sets and the estimates werecombined. SAS 9.2 (SAS Institute, Cary, NC) was used
for all statistical analyses.

Results
Fifty one percent of the participants were female.
Average age of the participants was 72.7 years (Standard
deviation: 5.8). Among them, 58 % of them occupied leadership roles in their participating organizations, 35 % ofthem were regular participants, and the positions of 7 %of them were not known due to missing response.

Ishikawa et al. BMC Public Health (2016) 16:394

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Table 1 Mortality rates by baseline characteristics (N=10,271)n(%)

Incidence/person year

Incidence rate per 1000 person-years (95 % CI)

65–69

3680

36 %

139/14,441

9.6 (8.2–11.4)

70–74

3046

30 %

160/11,855

13.5 (11.6–15.7)

75–79

2205

21 %

196/8446

23.2 (20.2–26.6)

80–84

948

9%

143/3510

40.7 (34.7–47.8)

85 or older

392

4%

115/1355

84.9 (71.3–101.1)

Female5261

51 %

255/20,498

12.4 (11.0–14.1)

Male

5010

49 %

498/19,108

26.1 (23.9–28.4)<19,999

3304

32 %

240/12,746

18.8 (16.6–21.3)

20,000–39,999

3880

38 %

292/14,940

19.5 (17.4–21.9)≧40,000

1023

10 %

55/398113.8 (10.6–18.0)

Missing

2064

20 %

166/7939

20.9 (18.0–24.3)

Less than 6 years

381

4%

53/1418

37.4 (28.7–48.7)

6–9 years

5298

52 %

411/20,435

20.1 (18.3–22.1)

10–12 years

2964

29 %

175/11,494

15.2 (13.1–17.6)

13 years and more

1002

10 %

61/3857

15.8 (12.3–20.3)

Other

33

0%

5/117

42.7 (18.1–100.7)

Missing593

6%

48/2285

21.0 (15.9–27.8)

Age

Sex

Annual equivalized household income (JPY)

Educational attainment

Marital status
Married and spouse is alive

6932

67 %

473/26,825

17.6 (16.1–19.3)

Other

2670

26 %

220/10,213

21.5 (18.9–24.5)

Missing669

7%

60/2568

23.4 (18.2–30.0)

Self-reported health
Very good861

8%

36/3377

10.7 (7.7–14.8)

Good

6542

64 %

353/25,48513.9 (12.5–15.4)

Poor

2126

21 %

237/8057

29.4 (25.9–33.3)

Very poor

438

4%

101/1533

65.9 (54.6–79.6)

Missing

304

3%

26/115422.5 (15.4–32.9)

Self-reported physical function
Very good

1373

13 %

68/5372

12.7 (10.0–16.0)

Good

6844

67 %

406/26,562

15.3 (13.9–16.8)

Poor

1522

15 %

192/5721

33.6 (29.2–38.6)

Very poor

329

3%

69/1180

58.5 (46.5–73.5)

Missing203

2%

18/771

23.3 (14.8–36.9)

Self-reported medical conditions
No illness

1707

17 %

67/670910.0 (7.9–12.7)

Having illness but need no treatment

987

10 %

74/381719.4 (15.5–24.3)

Having illness but discontinued treatment

627

6%

37/2438

15.2 (11.0–20.9)

Receiving some treatment

6323

62 %

537/24,210

22.2 (20.4–24.1)

Missing627

6%

38/2433

15.6 (11.4–21.4)

Ishikawa et al. BMC Public Health (2016) 16:394

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Table 1 Mortality rates by baseline characteristics (N=10,271) (Continued)
Geriatric Depression Scale
Normal6109

59 %

339/23,803

14.2 (12.8–15.8)

Mildly depressed

1944

19 %

192/7387

26.0 (22.6–29.9)

Severely depressed

348

3%

49/128338.2 (29.0–50.3)

Missing

1870

18 %

173/713324.3 (20.9–28.1)

Less than 10 years

316

3%

20/1205

16.6 (10.7–25.6)

10–19 years

439

4%

27/1670

16.2 (11.1–23.5)

20–29 years

760

7%

53/2926

18.1 (13.9–23.7)

30–39 years

1237

12 %

67/481413.9 (11.0–17.7)

40–49 years

1593

16 %

82/620713.2 (10.7–16.4)

50 years and more

5648

55 %

473/21,713

21.8 (19.9–23.8)

Missing278

3%

31/1072

28.9 (20.4–40.9)

5928

58 %

479/22,718

21.1 (19.3–23.0)

Residence year

Social participation
Regular position in the social organization(s)Leadership position in one or more social organization(s)

3600

35 %

214/14,014

15.3 (13.4–17.4)

Missing743

7%60/2874

20.9 (16.3–26.8)

During 22,718 person-years of follow-up for regularmembers and 14,014 person-years of follow-up for those
occupying leadership positions, 479 deaths and 214
deaths were observed, respectively. The mortality rate
was 21.1 per 1000 person-years (95 % Confidence Interval (CI): 19.3–23.0) for regular members and 15.3 per1000 person-years (95 % CI: 13.4–17.4) for those occupying leadership positions, respectively (Table 1).
Before IPTW adjustment, all of the differences inbaseline characteristics comparing regular memberswith those occupying leadership positions were statistically significant, whereas those differences were
eliminated after the inverse probability of weighting(Table 2).
The crude HR for mortality, relative to those who
were regular members in the social organizations, was
0.72 (95 % CI: 0.62–0.85) for those occupying a leadership position in one or more community organizations (Crude model in Table 3). The IPTW-HR ofmortality was attenuated to 0.88 (95 % CI: 0.79–0.99)
for individuals in leadership positions (IPTW modelin Table 3).

Discussion
The primary findings of this study of Japanese older
adults was that, among those participating in community
organizations, leaders who were occupying managerial
or administrative positions within the organization was
associated with a 12 % risk reduction of mortality independent of demographic variables, socioeconomic status,and self-reported medical condition. This was evaluatedby using propensity score method.
The potential pathways linking higher social positions
and health include: (1) better access to material resources to maintain health [20] and (2) psychosocial
benefits attendant on occupying a higher status position[11, 21]. While higher position within certain social
structures (such as a workplace) can determine accessto material resources such as higher incomes, stock options, fringe benefits (e.g. housing allowances), andmore generous pension benefits, these are unlikely toapply to older people occupying higher positions within
civic (mostly voluntary) groups. Instead, occupying aleadership position within a community organization is
likely to be associated with psychosocial benefits, suchas prestige, meaningful social role, sense of purpose,
psychological well-being and decision-making authority. For example, past research using AGES cohort data
supports this idea that older women with roles involving responsibility in community organizations enjoyedbetter mental health compared to those participating as
regular members [22].
Another potential mechanism through which higher
social position leads to lower all-cause mortality is thetheory of communication inequality [23, 24]. According
to the theory [23], communication inequalities are defined as “differences among social classes in the generation, manipulation, and distribution of information at
the group level and differences in access to and ability totake advantage of information at the individual levelIshikawa et al. BMC Public Health (2016) 16:394

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Table 2 Relationship between social position and baseline characteristics before/after weighting the inverse probability of treatment
Before weighting the inverse probability of treatment

After weighting the inverse probability of treatment

Regular position (%)

Regular position (%)

Leadership position (%)

Age

P value

Leadership position (%)<.0001

0.96

65–69

31.8

42.5

35.7

35.4

70–74

28.3

31.9

29.6

29.4

75–79

23.6

18.0

21.6

21.9

80–84

11.3

5.8

9.3

9.4

85 or older

5.0

1.8

3.8

3.9

Sex

<.0001

0.73

Female

57.3

41.2

51.4

51.6

Male

42.7

58.8

48.7

48.4

Annual equivalized householdincome (JPY)<.0001

0.42<19,999

43.7

38.3

42.0

42.9

20,000–39,999

44.6

49.3

46.1

45.3

≧40,000

11.7

12.4

11.9

11.8

4.8

2.1

3.8

4.0

Educational attainment
Less than 6 years<.0001

0.86

6–9 years

57.7

50.4

55.2

55.7

10–12 years

28.8

33.7

30.5

30.0

13 years and more

8.4

13.4

10.2

10.0

Other

0.3

0.4

0.3

0.3

Married and spouse is alive

67.9

78.9

71.9

71.3

Other

32.1

21.1

28.1

28.7

Marital status<.0001

Self-reported health

0.35<.0001

0.55

Very good

7.0

11.3

8.7

8.5

Good

63.9

67.8

65.3

64.8

Poor

23.8

17.8

21.6

21.9

Very poor

5.2

3.1

4.4

4.8

Very good

11.3

17.5

13.6

13.3

Good

67.3

68.8

67.8

67.6

Poor

17.4

11.7

15.3

15.4

Very poor

3.9

2.1

3.3

3.7

Self-reported physical function<.0001

Self-reported medical conditions

0.31<.0001

0.93

No illness

16.1

21.1

18.0

17.8

Having illness but need notreatment

9.2

12.2

10.2

10.1

Having illness but
discontinued treatment

6.5

6.1

6.4

6.4

Receiving some treatment

68.3

60.6

65.4

65.8

Geriatric Depression Scale

P value<.0001

0.58

Normal

63.3

76.6

68.1

67.7

Mildly depressed

33.6

21.9

29.3

29.9

Ishikawa et al. BMC Public Health (2016) 16:394

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Table 2 Relationship between social position and baseline characteristics before/after weighting the inverse probability of treatment(Continued)
Severely depressed

3.1

1.5

2.6

2.5

Less than 10 years

3.9

2.2

3.3

3.7

10–19 years
20–29 years

5.1

3.7

4.6

4.9

8.1

7.4

7.9

7.9

30–39 years

12.9

12.8

12.9

12.9

40–49 years

16.3

16.8

16.5

16.4

50 years and more

53.8

57.0

54.8

54.3

Residence year<.0001

0.67

As multiple imputation was applied to the dataset, the result is based on the first of ten dataset but almost same results were acquired from the other datasets[p. 287].” For example, those in leadership position
might be more likely to be exposed to health information than those in regular position, which leadthem to make healthy decision makings decisions. However, little empirical studies have examined whether suchinequality in exposure to health information exists in thecontext of social participation among older adults. Further
study on the characteristics of those in leadership position
compared with those in regular position may contributeto the understanding of the effect of status on health.
This study has several limitations. First and foremost,
we cannot completely eliminate the possibility that there
is selection of individuals into both social participationand leadership roles (i.e. healthy and robust individuals
join more groups, and they are more likely to volunteer
for leadership roles in the groups which they belong to).
Although we carefully controlled for selection using the
IPTW approach, we were only able to take account ofobserved variables in the dataset. It is still possible that
respondents differed with respect to unobservable factors influencing both their participation and their subsequent mortality risks (i.e. residual confounding). For
example, past research using panel data shows that theperson’s long-term unemployment history is negatively
associated with health status [25], which might reducethe chance of occupying leadership position. Future
AGES cohort study should incorporate these known potential factors in their new wave of data collection.
Secondly, our measures of social participation
remained crude. For example, Aida et al have reported that horizontal (as compared to vertical) organizations had beneficial effects on oral health in older
Japanese adults, using AGES data [26]. We hypothesized that the same phenomenon can be applied to
the result of this study that the effect of social position on mortality differs by type of the organizations.
In the AGES study, the question on social position
was as follows: “Do you serve as the head, manager
or treasurer in the organization or group?” Therefore,

we could not know which type of organization the respondents serve in a leadership capacity. Also, ourmeasure of social participation does not capture theintensity of involvement. Further study is warranted
to uncover the interactive effect of the social position
and the type of/the intensity of involvement in thecommunity organization on health.
Third, we could not measure the change in status
within the participating organization or the change inorganizational membership during the follow-up period,
since the measurement was carried out just once at the
beginning of the study. Future study should use multiwave panel data to take this aspect into account.
Fourth, long-term effects of participation in community organizations beyond the period of 5-year follow up
could not be evaluated. Past longitudinal study reportsthat the impact of increasing social status on psychological wellbeing persists over 5 years [27]. As such, it isan interesting research question to examine how longthe effect of increasing social status will last. Future
study with longer follow-up period should examine
whether similar results can be obtained or not.
Fifth, the existence of non-respondents (response rate:
50.4 %) could be a source of potential selection bias,
resulting in either over- or under-estimation of the effectof social position on all-cause mortality.
In summary, our results emphasize the importance of
simultaneously considering the social position of individuals within the organization when examining the influence of social participation on health. Over and abovesocial participation, findings of our study suggest that
promoting older Japanese people to leadership positionas many as possible may have positive impact on longevity. Nonetheless, it is plausible that taking leadership ina group may provide health benefits only for selected
persons with specific characteristics, e.g., personality andsocioeconomic statuses. Further study should investigate
the interaction between group leadership and those factors on health.

Ishikawa et al. BMC Public Health (2016) 16:394

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Table 3 Hazard ratios for all-cause mortality: results of Cox
proportional hazard model (N=10,271)

Table 3 Hazard ratios for all-cause mortality: results of Cox
proportional hazard model (N=10,271) (Continued)

Crudea)IPTWb)

1.30 (0.96 to 1.77)

HR (95 % CI)

Having illness but
discontinued treatment

1.52 (1.02 to 2.27)

HR (95 % CI)

Receiving some treatment

2.18 (1.70 to 2.80)

1.28 (1.04 to 1.59)

referencereference

Geriatric Depression Scale

Social participation
Regular position in thesocial organization(s)Leadership position in one
0.72 (0.62 to 0.85)
or more social organization(s)0.88 (0.79 to 0.99)

Age
65–69

referencereference

70–74

1.40 (1.12 to 1.76)

1.27 (1.07 to 1.50)

75–79

2.42 (1.95 to 3.01)

2.20 (1.87 to 2.59)

80–84

4.27 (3.38 to 5.40)

3.79 (3.17 to 4.54)

85 or older

9.03 (7.05 to 11.56) 7.34 (5.84 to 9.21)

referencereferencereference

Mildly depressed

1.59 (1.36 to 1.85)

1.17 (1.03 to 1.33)

Severely depressed

2.18 (1.73 to 2.74)

1.19 (0.96 to 1.48)

Residence year

Sex
FemaleNormalreferenceLess than 10 years

0.80 (0.51 to 1.26)

0.97 (0.69 to 1.37)

10–19 years

0.80 (0.55 to 1.17)

0.88 (0.67 to 1.16)

20–29 years

0.89 (0.68 to 1.17)

1.22 (0.97 to 1.53)

30–39 years

0.67 (0.52 to 0.86)

0.94 (0.76 to 1.15)

40–49 years

0.63 (0.50 to 0.79)

1.11 (0.93 to 1.31)

50 years and morereferencereference

a)

Male

2.10 (1.81 to 2.44)

2.75 (2.40 to 3.15)

Crude HR unadjusted models using each covariate as a single exposure when
examining association with allcause mortality
IPTW HR inverse probability of treatment weighted HRb)

Individual-level equivalent
income (JPY)<19,999

referencereference

20,000–39,999

1.01 (0.85 to 1.20)

1.19 (1.03 to 1.37)≧40,000

0.78 (0.56 to 1.10)

1.01 (0.74 to 1.37)

Less than 6 years

referencereference

6–9 years

0.54 (0.41 to 0.73)

0.79 (0.60 to 1.06)

10–12 years

0.41 (0.30 to 0.56)

0.78 (0.59 to 1.03)

Educational attainment

13 years and more

0.43 (0.30 to 0.62)

0.65 (0.45 to 0.94)

Other

1.16 (0.47 to 2.91)

1.38 (0.69 to 2.75)

Conclusions
Holding leadership positions in community organization(s) may be more beneficial to health than being
regular members.
Ethics

Our study protocol and informed consent procedure
were approved by the Ethics Committee on the Researchof Human Subjects at Nihon Fukushi University.
Availability of supporting data

Marital status
Married and spouse is alive

0.82 (0.70 to 0.97)

0.86 (0.75 to 0.99)

Otherreferencereference

Very goodreferencereferenceGood

1.28 (0.91 to 1.80)

0.97 (0.72 to 1.31)

Poor

2.67 (1.88 to 3.79)

1.58 (1.13 to 2.21)

Very poor

5.98 (4.08 to 8.75)

2.55 (1.74 to 3.73)

Very goodreferencereferenceGood

1.20 (0.93 to 1.55)

1.01 (0.79 to 1.28)

Poor

2.61 (1.98 to 3.44)

1.32 (0.98 to 1.77)

Very poor

4.59 (3.28 to 6.41)

1.48 (1.03 to 2.13)

Self-reported healthSelf-reported physical function

Self-reported medical
conditions
No illness

referencereference

Having illness but needno treatment

1.91 (1.37 to 2.66)

1.39 (1.08 to 1.80)

The dataset supporting the conclusions of this article isavailable in response to the request from the researchers
admitted by the JAGES committee.
Competing interest
The authors declare that they have no competing interests.
Authors’ contribution
YI had participated in its design, performed the data analysis and drafted themanuscript as a principal author. NK participated in the interpretation of theresults and discussions for manuscript writing and finalization. KK had theidea for the study, participated in acquiring the data and with design, andcritically reviewed the manuscript. TS performed the data analysis andparticipated in the interpretation of the results and discussions formanuscript writing and finalization. HH and IK participated in theinterpretation of the results and discussions for manuscript writing andfinalization. All authors read and approved the final manuscript.
Acknowledgement
This study used data from the Aichi Gerontological Evaluation study (AGES),
which is a part of Japan Gerontological Evaluation Study (JAGES). We would
like to express our deepest gratitude to everyone who participated in thesurveys.
Members of the JAGES group are as follows: Kondo K. (the lead
investigator;BZH12275@nifty.ne.jp), Hanazato M., Hikichi H., Miyaguni Y.,
Sasaki Y., Nagamine Y., Chiba University, Chiba; Ashida T., Kondo N.,
Takagi D., Tani Y., The University of Tokyo, Tokyo; Aida J., Osaka K.,

Ishikawa et al. BMC Public Health (2016) 16:394

Tsuboya T., Tohoku University, Miyagi; Jeong S., Murata C., Saito T.,
National Center for Geriatrics and Gerontology, Aichi; Ojima T., Okada E.,
Hamamatsu University School of Medicine, Shizuoka; Shirai K., Todoriki H.,
University of the Ryukyus, Okinawa; Saito M., Nihon Fukushi University,
Aichi; Hirai H., Iwate University, Iwate; Misawa J., Rikkyo University, Tokyo;
Suzuki K., Aichi Gakuin University, Aichi; Ichida Y., Doctoral Institute forEvidence Based Policy, Tokyo; Takeda T., Seijoh University, Aichi; Yamamoto T.,
Kanagawa Dental University, Kanagawa; Nakade M., Tokaigakuen University,
Aichi; Cable N., University College London, London; Tamakoshi A., Hokkaido
University Graduate School of Medicine, Hokkaido; Fujino Y., University ofOccupational and Environmental Health, Fukuoka; Shobugawa Y., Niigata
University, Niigata; Hayashi T., Tokai College of Medical Science, Aichi.
Funding information
This study and the AGES projects were in part supported by a Grant-in-Aid
for Scientific Research (A) (Nos. 23243070, 25253052) from the Japanese Society for the Promotion of Science, 2013; a Comprehensive Research on Agingand Health (H25-Chouju-Ippan-003) 2013-2015 from the Japanese Ministry of
Health, Labour and Welfare (MHLW); Health and Labor Sciences Research
Grant (H25-kenki-wakate-015) by the MHLW; and Japan Agency for Medical
Research and Development. The funders had no role in study design, datacollection and analysis, decision to publish, or preparation of the manuscript.
Author details
1
Department of Health and Social Behavior, School of Public Health, The
University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo 1130033, Japan. 2Cancer
Scan, Shibuya-ku, Tokyo, Japan. 3Center for Preventive Medical Sciences,
Chiba University, Chiba, Japan. 4Center for Gerontology and Social Science,
National Center for Geriatrics and Gerontology, Nagoya, Japan. 5Departmentof Public Health, Mccann Health Communications, Minato-ku, Tokyo, Japan.
6
Department of Social and Behavioral Sciences, Harvard School of Public
Health, Boston, Massachusetts, USA.
Received: 28 August 2015 Accepted: 4 May 2016

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