Health & Place 89 (2024) 103336

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Health and Placejournal homepage: www.elsevier.com/locate/healthplace

Community-level social capital and subsequent health and well-being
among older adults in Japan: An outcome-wide longitudinal approach
Sho Takeda a , Maho Haseda b , Koryu Sato c , Koichiro Shiba d , Atsushi Nakagomi e ,Kazushige Ide f , Naoki Kondo b, *a

Health and Counseling Center, Osaka University, Osaka, Japan
Department of Social Epidemiology, Graduate School of Medicine and School of Public Health, Kyoto University, Kyoto, Japan
Faculty of Policy Management, Keio University, Kanagawa, Japand
Department of Epidemiology, Boston University School of Public Health, Boston, USAe
Department of Social Preventive Medical Science, Center for Preventive Medical Sciences, Chiba University, Chiba, Japanf
Department of Community building for Well-being, Center for Preventive Medical Sciences, Chiba University, Chiba, Japanbc

A R T I C L E I N F O

A B S T R A C T

Keywords:
Community-level social capital
Well-being
Outcome-wide epidemiology
Older adults
Multilevel analysis

There is inconsistent evidence on the association between community-level social capital and the health or wellbeing of older adults. This study examined the association between community-level social capital and multidimensional health and well-being outcomes using an outcome-wide approach. We used data from the Japan
Gerontological Evaluation Study, a nationwide cohort study of Japanese older adults (analytic samples: 47,227
for outcomes obtained from the long-term care insurance registry and 34,183 for other outcomes). We assessed
three aspects of school-district-level community social capital in 2016 (civic participation, social cohesion, andreciprocity) and 41 subsequent health and well-being outcomes through 2019. We performed either a modified
multilevel Poisson regression or a multilevel logistic regression analysis. We adjusted for pre-baseline characteristics, prior outcome values, and individual-level social capital from the 2013 wave. Even after Bonferroni
correction, we found that community-level social capital was associated with some subsequent social well-being
and physical/cognitive health. For example, community-level reciprocity was associated with a higher prevalence of taking a social role (Prevalence ratio [PR] = 1.03, 95% confidence interval [CI] = 1.02, 1.04) andundergoing health screening (PR = 1.03, 95% CI: 1.01, 1.04). There was modest evidence that community-level
civic participation was associated with a higher competency of intellectual activity (PR = 1.01, 95% CI: 1.01,
1.02) and community-level social cohesion was associated with a reduced onset of functional disability (PR =0.94, 95% CI: 0.90, 0.98). Community-level social capital may promote social well-being and some physical/
cognitive health outcomes.

1. Introduction
Maintaining and improving health and well-being among older
adults are critical public health issues (Zuidberg et al., 2020). Well-being
is a complex concept encompassing many domains, and health is an
essential aspect of well-being (Centers for Disease Control and Prevention, 2018; VanderWeele, 2017a). Although health and well-being are
inherently multidimensional, existing empirical studies, including thosein the field of public health, have examined a limited range of outcomes(e.g., a single disease) and failed to capture a holistic picture of the
exposure effect on broad well-being (VanderWeele, 2017a). Evaluating

one’s health based only on the presence/absence of disease or disability
might underestimate the human capacity to cope autonomously with thephysical, emotional, and social challenges of life: highlighting a disease
might label people with chronic diseases and disabilities as definitively
ill (Huber et al., 2011). Therefore, it is necessary to capture health andwell-being among older adults from a holistic perspective, which includes social health and other aspects (e.g., eudaimonic well-being, suchas purpose in life) (Ryff et al., 2021). To this end, scholars have introduced the concept of human flourishing—a state in which all aspects of aperson’s life are good. This concept comprises the following five domains of human well-being: 1) happiness and life satisfaction, 2) mental* Corresponding author. Floor 2, Science Frontier Laboratory, Yoshida-konoe-cho, Sakyo-ku, Kyoto, 604-8146, Japan.
E-mail address: kondo.naoki.0s@kyoto-u.ac.jp (N. Kondo).
https://doi.org/10.1016/j.healthplace.2024.103336
Received 26 July 2023; Received in revised form 2 July 2024; Accepted 31 July 2024
Available online 9 August 2024
1353-8292/© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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Health and Place 89 (2024) 103336

and physical health, 3) meaning and purpose, 4) character and virtue,and 5) close social relationships. Human flourishing is a broader morecomprehensive concept than psychological well-being, unique in that it
includes health and virtue as components of well-being (VanderWeele,
2017a).
Social capital has been studied as a factor that affects multidimensional health and well-being among older adults, both positively andnegatively (Kawachi and Berkman, 2014). Social capital is defined as“resources that are accessed by individuals as a result of their membership of a network or a group” (Kawachi and Berkman, 2014).
Conceptually, social capital encompasses both individual- andcommunity-level attributes. Individual-level social capital involves anegocentric social network, whereas community-level social capital reflects contextual effects derived from a membership of community(Kawachi and Subramanian, 2006). It is crucial to study
community-level social capital because it can affect population health,regardless of individual-level social capital (Kawachi et al., 2008).
Despite its potential health benefits, community-level social capital canalso be detrimental to individuals’ health through several pathways (e.
g., excess claims on group members, such as peer pressure) (Portes,
1998; Amemiya et al., 2019; Haseda et al., 2018). Previous empiricalstudies have reported mixed results regarding the relationship between
community-level social capital and health/well-being. In these studies,
the aforementioned appeared beneficial for physical health andwell-being (Murayama et al., 2012; Nyqvist et al., 2013; Rodgers et al.,2019), but to adversely impact risk behaviors and self-rated health(Villalonga-Olives and Kawachi, 2017). Mental health such as depression was both positively and negatively affected by community-levelsocial capital (Ehsan and Silva, 2015; Villalonga-Olives and Kawachi,2017). Essentially, community-level social capital may have differential
and potentially conflicting effects across outcomes. Therefore, it isnecessary to holistically examine how community-level social capital
affects a broad range of health and well-being outcomes.
Although the results are mixed across studies examining different
health and well-being outcomes, the comparison of results across suchstudies remains challenging owing to differences in the following aspects: 1) study design (cross-sectional vs. longitudinal), 2) study population, 3) number and types of confounding factors, and 4) methods formeasuring exposure (Kim et al., 2020). Some studies measurecommunity-level social capital in terms of perceptions of social capital,such as trust in the community (Young et al., 2004), whereas othersmeasure structural social capital, such as the number of elections
attended (Sundquist et al., 2014). Moreover, community-level social
capital has heterogeneous effects based on gender and socioeconomic
status (Uphoff et al., 2013). The impact of community-level social capital on the improvement of long-term care varies depending on thegender and social participation status of individuals (Amemiya et al.,2019).
To address the abovementioned gaps, we adopted an outcome-wide
approach to examine the association between community-level social
capital and health/well-being outcomes using a large longitudinal
dataset of community-dwelling older adults in Japan. The health andwell-being outcomes were assessed using five domains and 41 healthand well-being outcomes based on the human flourishing framework(VanderWeele, 2017a). The outcome-wide approach enabled us to understand the association between community-level social capital andwell-being from a holistic perspective (Vanderweele, 2017b). Thisapproach also enabled us to assess the potentially differential effects ofcommunity-level social capital across outcomes by standardizing thefollowing aspects: 1) study design, 2) study population, 3) covariates,and 4) exposure (VanderWeele et al., 2020). We also investigated theheterogeneous effects of community-level social capital on health andwell-being according to individual socio-demographic characteristics,
including gender and socioeconomic status (SES).

2. Methods
2.1. Study population
Data were obtained from the Japan Gerontological Evaluation Study(JAGES), a nationwide survey involving Japanese older adults aged 65
years and above (Kondo and Rosenberg, 2018). In this study,
self-administered questionnaires were mailed to older adults without
functional disabilities (i.e., those not certified as requiring public
long-term care). We used data obtained from the 2013, 2016, and 2019
JAGES surveys. The participants were selected from 26 municipalities(n = 178,201). In the 14 large municipalities, the respondents selectedin the previous wave (2010) through a multi-stage random sampling
approach based on their officially registered residents and newly
randomly sampled participants were selected. On the other hand, in the
12 small municipalities, all eligible individuals were included. The
participants responded to the questionnaires in the 2013 survey (n =126,474; response rate = 71.0%). In 2016, a follow-up survey was
conducted (n = 79,049; follow-up rate: 62.5%). Exposure wascommunity-level social capital, assessed in the 2016 survey. We obtained two analytical samples by linking these 79,049 individuals to thesecond follow-up wave of the survey in 2019 (n = 34,183) or to the
public long-term care insurance (LTCI) registry, including informationon the onset of death, dementia, and functional disability between 2016
and 2019 (n = 47,227) (Supplemental methods). Outcomes wereassessed using the 2019 survey or the public LTCI registry. Respondents
who provided inconsistent answers regarding age or gender, height, or
weight between surveys were excluded (n = 2635). Items for the loneliness measure, one of our outcomes, were available only in the submodule questionnaire in 2019 which was distributed to a randomly
selected 12.5% of the participants in 21 follow-up municipalities (n =21,010) and responded to by 15,054 individuals. In the final analyticalsample, respondents who received and answered the 2019 submodule
questionnaire including the loneliness sample were 4384. Items forikigai (ikigai is broadly defined as “what makes life worth living”,
originates from Japanese culture and is widely accepted within it[Mathews, 1996]), another outcome variable in this study, were available only in the submodule questionnaire in 2013, which was distributed to a randomly selected 12.5% of the participants (n = 35,888) and
responded to by 24,383 individuals. In the final analytical sample, respondents who received and answered the 2013 submodule questionnaire including ikigai were 6832.
2.2. Measures
2.2.1. Community-level social capital
Our exposure of interest was community social capital in 2016,
defined at the school district level. We used a scale that was previously
developed and validated using the JAGES data (Saito et al., 2017) andassessed three components of community social capital: civic participation, social cohesion, and reciprocity. Community-level social capital
was calculated by aggregating the responses of individuals who participated in the 2013 and 2016 surveys. We focused on the school district asa geographical unit for the following four reasons: 1) This scale wasvalidated using it as the geographic unit, 2) it is a daily living area formany older adults, 3) it often represents the socio-geographic area of aformer village, and community activities such as senior citizens’ clubs,
agricultural cooperatives, and local festivals are organized within each
district, 4) it represents the smallest geographic unit where we could
maintain adequate precision of aggregated information, based on thesample size within each community (Saito et al., 2017; Noguchi et al.,2019; Ministry of HealthLabor and Welfare, 2017).
Civic participation reflects the structural dimensions of social capital(Islam et al., 2006). Community-level civic participation was defined asa measure of social participation in five groups (hobby, sports, volunteering, study or cultural groups, and activities to teach skills or pass on
2

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Health and Place 89 (2024) 103336

experiences to others). Community-level civic participation wasassessed by summing the proportions of individuals participating in eachgroup by school district (score range: 0–5). Individual-level participation in each group was defined as participation once a month or more(versus participation less than once a month or never).
Social cohesion reflects subjective attitudes, such as trust, perceptionof others’ intentions to help, and attachment within the community.
Community-level social cohesion was assessed by summing the proportions of individuals who answered “very” or “moderately” to thefollowing three items: community trust (“Do you think that people living
in your area can be trusted, in general?), perception of others’ intentions
to help (“Do you think that people living in your area try to help others
in most situations?), and attachment to the residential area (“Howattached are you to the area where you live?”) (score range: 0–3). We
defined individual-level social cohesion as the responses of each individual to community trust, perception of others’ intentions to help, andattachment to a residential area.
Community-level reciprocity reflects the aspect of community social
capital that facilitates the exchange of individual social support.
Community-level reciprocity was assessed by summing the proportionsof individuals who answered “yes” to each of the following three items:
receiving emotional support (“Do you have someone who listens to yourconcerns and complaints?”), providing emotional support (“Do you
listen to someone’s concerns and complaints?”), and receiving instrumental support (“Do you have someone who looks after you when you
are sick and confined to a bed for a few days?) (score range: 0–3). We
defined individual-level reciprocity as the responses of each individual
to receiving emotional support, providing emotional support, andreceiving instrumental support. The details of each item and option arelisted in Supplementary Table S1. All the community-level social capital
was standardized, and a one-unit change in the exposure corresponded
to 1-SD change across the three social capital domains.

between the covariates and community social capital exposure andhelped mitigate the risk of adjusting for potential mediators (VanderWeele et al., 2020).
Individual-level socio-demographic factors included age (continuous), gender (men or women), employment (current worker versus
non-worker), years of education (< high school [9 years or less] versus≥ high school [ten years or more]), and equivalized household income(<2 million yen or ≥ 2 million yen), with reference to previous studies(Murata et al., 2008; Haseda et al., 2018). They also included marital
status (married, single, or others), living alone or not, and ADL(dependent or independent; whether the patient receives care or assistance for walking, bathing, or toileting in their daily life).
Community-level variables included population density (rural; <1000
people/km2, suburban; 1000–1500 people/km2, urban; >1500 people/km2) (Koyama et al., 2016; Noguchi et al., 2019, 2022).
To reduce the possibility of reverse causation, we also controlled forthe pre-baseline values of all outcomes in 2013, except for death andthree other outcomes for which pre-baseline data were unavailable(dementia, functional disability, and loneliness).
The target of our effect estimates was the contextual effects ofcommunity-level social capital. Therefore, individual responses in 2013
were adjusted to remove compositional effects. Items of pre-baseline
individual social capital included the outcomes in 2013, except forperceived emotional support.
2.3. Statistical analysis
We used an outcome-wide analytical approach that enabled the holistic assessment of the impact of a single exposure on a range of outcomes (VanderWeele, 2017b). The data included individuals (first level)
nested in 672 school districts for the panel data and 449 school districts
for the LTCI-based registry (second level). There were no large differences between the two analytic samples on the distribution ofcommunity-level social capital (Supplementary Table S2).
We assessed each outcome in separate multilevel regression models,assuming the random intercepts for school districts and adjusting for all
covariates. We conducted multilevel modified Poisson regression for
non-rare binary outcomes (Zou, 2004) with a prevalence of ≥10%. We
conducted multilevel logistic regression for rare binary outcomes with aprevalence of <10%. Modified Poisson regression estimates the prevalence ratios, whereas logistic regression for rare outcomes estimates theodds ratios approximating the prevalence ratios. To account for multiple
testing, we used Bonferroni correction in which we divided the usualsignificance levels of the test (0.05) by the number of tests and determined the more conservative p-value threshold for Bonferroni adjustment as α = 0.0012 (0.05/41).
Additionally, to assess the heterogeneity of the effects of social
capital across demographics, we conducted a subgroup analysis basedon gender and SES. We defined low SES as reporting both low education(9 years or less) and low equivalized income (2 million yen/year or less)in 2013. We defined high SES as others (either high education [ten years
or more] or high equivalized income [>2 million yen/year]), following
previous research in Japan (Murata et al., 2008; Haseda et al., 2018;
Okuzono et al., 2022). We used the same significance levels describedabove (α = 0.0012 [0.05/41]).
We adopted multiple imputations under the missing at random
assumption. After generating ten imputed datasets, we performed theabove analyses using each dataset and combined the effect estimations
across imputations.
We performed three sensitivity analyses. First, the analyses were
performed on respondents who answered at two time points (2013 and2016 surveys: n = 76,414) because the respondents who could respondto the entire three-wave survey included many healthy people who
survived for six years and could answer a similar questionnaire threetimes, which may have caused bias. Non-responses to questions andoutcomes of persons who did not participate in the 2019 survey were

2.2.2. Outcome variables
We chose 41 outcomes based on the multidimensional concept ofhuman flourishing as a framework (VanderWeele, 2017a) and referred
to previous studies (Nakagomi et al., 2022, 2023; Okuzono et al., 2022).
We examined outcomes including dimensions of 1) physical/cognitive, and mental health (no natural remaining teeth, self-rated health,instrumental independence, intellectual activity, body mass index(BMI), self-reported chronic diseases (hypertension, diabetes, dyslipidemia, stroke, heart disease, respiratory disease), depressive symptoms,
hopeless, loneliness, death, dementia, and functional disability (anylevels; level 2 or above), 2) subjective well-being (happiness, life satisfaction, and ikigai), 3) social well-being (participation in hobby, sports,
senior, learning or cultural groups, community trust, perception ofothers’ intentions to help, attachment to the residential area, higher
frequency of meeting friends, number of friends seen within a month,receiving emotional social support/instrumental social support, andsocial roles), 4) pro-social/altruistic behaviors (participation in volunteer groups and activities to teach skills or pass on experiences toothers), and 5) health behavior (current smoking/drinking status, eating
vegetables and fruit, longer walking time, housebound [lower frequencyof going out], and health screening). Outcomes from the LTCI registry(death, dementia, and functional disability) were assessed for outcome
incidence during the 3-year follow-up period (from 2016 and through2019). Other outcomes were assessed in the 2019 survey—three years
later from the baseline wave. In this study, all outcomes were converted
into binary values because some continuous variables were not normally
distributed, and making it easier to interpret the estimated associations.
Supplementary Table S1 shows further details about each outcome.
2.2.3. Covariates
We adjusted for individual socio-demographic and community-levelfactors as potential confounders. All covariates were taken from the prebaseline wave (the 2013 survey), which ensured the temporal order3

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Health and Place 89 (2024) 103336

community-level social cohesion was associated with higher levels ofcommunity trust at the individual level (PR = 1.027, 95% CI: 1.020,
1.033), perception of others’ intentions to help (PR = 1.041, 95% CI:
1.031, 1.051), and attachment to the residential area (PR = 1.016, 95%
CI: 1.010, 1.021). Individuals who lived in communities with higher
levels of reciprocity were more likely to receive instrumental social
support (PR = 1.122, 95% CI: 1.068, 1.178), health screening (PR =1.025, 95% CI: 1.013, 1.036), and to take on social roles (OR = 1.030, 95% CI: 1.018, 1.041). After the Bonferroni correction, these associations
remained below the statistically significant threshold (α < 0.05/41).
We also found modest evidence of several other exposure-outcome
associations in some aspects of well-being, such as physical/cognitive
health, social well-being, and health behavior. First, higher communitylevel civic participation was associated with the prevalence of instrumental independence (OR = 1.055, 95% CI: 1.006, 1.106), having
competency in intellectual activity (PR = 1.011, 95% CI: 1.004, 1.018),
and participation in activities to teach skills or pass on experiences toothers (OR = 1.088, 95% CI: 1.031, 1.148) as well as lower prevalence of
currently smoking (OR = 0.922, 95% CI: 0.855, 0.994) and taking social
roles (PR = 0.982, 95% CI: 0.970, 0.995). Second, higher communitylevel social cohesion was associated with lower prevalence of no natural teeth remaining (OR = 0.916, 95% CI: 0.851, 0.987), self-reported
diabetes (PR = 0.975, 95% CI: 0.957, 0.995), functional disability ofboth any levels (PR = 0.941, 95% CI: 0.904, 0.979) and level 2 or above(OR = 0.919, 95% CI: 0.864, 0.977), having competency in intellectual
activity (PR = 0.992, 95% CI: 0.984, 1.000), and being housebound(lower frequency of going out) (OR = 0.915, 95% CI: 0.848, 0.987).
Third, higher community-level reciprocity was associated with higher
levels of the frequency of eating vegetables and fruits (PR = 1.005, 95%
CI: 1.001, 1.010), lower prevalence of participating in volunteering (PR= 0.974, 95% CI: 0.952, 0.990), and feeling attachment to the residential area (PR = 0.995, 95% CI: 0.991, 0.999). However, after the
Bonferroni correction, these associations were not below the threshold(α < 0.05/41).
There was little evidence of an association between community-levelsocial capital and mental health. For example, we did not observe clear
evidence of the associations between depressive symptoms andcommunity-level civic participation (PR = 1.001, 95% CI: 0.982, 1.020),

imputed. Second, we used different categories for years of education(<6, 6–9, 10–12 or ≥ 13 years) and income (continuous) to assess thedifferences in outcomes resulting from changes in sociodemographic
factor divisions. Third, to evaluate the robustness of the observed associations to unadjusted confounding, we calculated the E-value foreach exposure-outcome association. E-value quantifies the minimum
strength of the association on the risk ratio scale that an unmeasured
confounder would need to have with both the exposure and outcomes
above and beyond the adjusted covariate to explain away the observed
association (VanderWeele and Ding, 2017). All analyses were performed
using STATA, version 17.0 (STATA Corp LLC, College Station, Texas,
USA).
3. Results
The analytical samples included 34,183 individuals (17,702 womenand 16,481 men) from 672 school districts in 21 municipalities for the2019 survey and 47,227 individuals (25,630 women and 21,597 men)from 449 school districts in 19 municipalities for the LTCI registry(Fig. 1).
Table 1 shows the pre-baseline characteristics and prior outcome
values of the respondents of the study sample linked to the 2019 survey(n = 34,183). Participants’ mean age was 72.0 (SD = 4.9), and 51.8%
were female. Similar trends were found for the pre-baseline characteristics and prior outcomes of the study sample linked to the LTCI registry(n = 47,277), the sample that received and answered the 2013 submodule questionnaire including ikigai (n = 6832), and those who
received and answered the 2019 submodule questionnaire including
loneliness (n = 4384) (Supplementary Table S3).
Table 2 shows the estimated associations between the baseline
community social capital exposures and each outcome after adjustingfor pre-baseline confounders. Higher community-level civic participation was associated with higher prevalence of participation in the hobby
group (Prevalence ratio [PR] = 1.035, 95% confidence interval [CI]:1.021, 1.050), sports group (PR = 1.047, 95% CI: 1.031, 1.063), learningor cultural group (Odds ratio [OR] = 1.104, 95% CI: 1.049, 1.154), andvolunteering (PR = 1.064, 95% CI: 1.039, 1.090) and was associatedwith a lower risk of death (OR = 0.906, 95% CI: 0.862, 0.953). Higher

Fig. 1. Flow of sample selection (n = 34,183 in 21 municipalities for the outcomes in the 2019 survey and n = 47,277 in 19 municipalities for the outcomes based on
the long-term care insurance registry).
4

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Health and Place 89 (2024) 103336

Table 1Pre-baseline characteristics and prior outcomes in 2013 stratified by gender of the analytical sample linked to the 2019 survey (n = 34,183).
Characteristic
Age, mean (SD)
Education, n (％)≤9 years≥10 years
missing
Household income, n (%)>2 million yen≤2 million yen
missing
Marital status, n (％)
widowed/divorced/single/others
marriedmissing
Employment, n (％)
non-worker
Current worker
missing
Living with someone, n (％)
noyesmissing
Activities of daily living, n (%)
dependent
independent
missingPrior outcomes
1. Physical/cognitive and mental health
No natural teeth remaining, n (％)
missingGood self-rated health, n (％)
missing
Normal BMI, n (%)
missing
Instrumental independence, n (%)
missing
Having competency in intellectual activity, n (%)
missing
Self-reported hypertension, n (％)
missing
Self-reported diabetes, n (％)
missing
Self-reported dyslipidemia, n (％)
missing
Self-reported stroke, n (％)
missing
Self-reported heart disease, n (％)
missing
Self-reported respiratory disease, n (％)
missing
Depressive symptoms (GDS), n (%)
missing
Hopeless, n (%)
missing
2. Subjective well-being
High happiness level, n (%)
missing
Life satisfaction, n (％)
missing
3. Social well-being
Participation in a hobby group, n (%)
missing
Participation in sports group, n (%)
missing
Participation in learning or cultural group, n (%)
missing
Participation in the senior group, n (%)
missing
Higher levels of community trust, n (%)
missing
Perception of others’ intentions to help, n (%)
missing
Attachment to the resident area, n (%)
missing

Overall (n = 34,183)

Women (n = 17,702)

Men (n = 16,481)

71.99 (4.90)

71.93 (4.86)

72.06 (4.95)

10,795 (31.58)
22,973 (67.21)
415 (1.21)

6246 (35.28)
11,221 (63.39)
235 (1.33)

4549 (27.60)
11,752 (71.31)
180 (1.09)

13,378 (39.14)
16,277 (47.62)
4528 (13.25)

7012 (39.61)
7719 (43.61)
2971 (16.78)

6366 (38.63)8558 (51.93)
1557 (9.45)

7520 (22.00)
26,258 (76.82)
405 (1.18)

5777 (32.63)
11,660 (65.87)
265 (1.50)

1743 (10.58)
14,598 (88.57)
140 (0.85)

23,474 (68.67)
8559 (25.04)
2150 (6.29)

12,771 (72.14)
3422 (19.33)
1509 (8.52)

10,703 (64.94)
5137 (31.17)
641 (3.89)

4573 (13.38)
28,304 (82.80)1306 (3.82)

3205 (18.11)
13,702 (77.40)
795 (4.49)

1368 (8.30)
14,602 (88.60)
511 (3.10)

253 (0.74)
32,976 (96.47)
954 (2.79)

143 (0.81)
17,058 (96.36)
501 (2.83)

110 (0.67)
15,918 (96.58)
453 (2.75)

2135 (6.25)
781 (2.28)
29,718 (86.94)
793 (2.32)
24,515 (71.72)
375 (1.10)
29,656 (86.76)
561 (1.64)
25,865 (75.67)
729 (2.13)
14,230 (41.63)
2193 (6.42)
3841 (11.24)
2193 (6.42)
4911 (14.37)
2193 (6.42)
738 (2.16)
2193 (6.42)
2968 (8.68)
2193 (6.42)
1376 (4.03)
2193 (6.42)
5811 (17.00)
4595 (13.44)4369 (12.78)
742 (2.17)

1001 (5.65)
491 (2.77)
15,484 (87.47)
466 (2.63)
12,720 (71.86)
240 (1.36)
16,697 (94.32)
253 (1.43)
13,723 (77.52)
438 (2.47)
7039 (39.76)
1255 (7.09)
1429 (8.07)
1255 (7.09)
2996 (16.92)
1255 (7.09)
220 (1.24)
1255 (7.09)
1036 (5.85)
1255 (7.09)
646 (3.65)
1255 (7.09)
2866 (16.19)
2988 (16.88)
2198 (12.42)
480 (2.71)

1134 (6.88)
290 (1.76)
14,234 (86.37)
327 (1.98)
11,795 (71.57)
135 (0.82)
12,959 (78.63)
308 (1.87)
12,142 (73.67)
291 (1.77)
7191 (43.63)
938 (5.69)
2412 (14.64)
938 (5.69)
1915 (11.62)
938 (5.69)
518 (3.14)
938 (5.69)
1932 (11.72)
938 (5.69)
730 (4.43)
938 (5.69)
2945 (17.87)
1607 (9.75)
2171 (13.17)
262 (1.59)

8520 (24.92)
688 (2.01)
28,729 (84.04)
531 (1.55)

5029 (28.41)
465 (2.63)
15,055 (85.05)
318 (1.80)

3491 (21.18)
223 (1.35)
13,674 (82.97)
213 (1.29)

12,236 (35.80)4036 (11.81)
9823 (28.74)
4380 (12.81)
3666 (10.72)
4591 (13.43)
2575 (7.53)
4251 (12.44)
24,348 (71.23)
527 (1.54)
18,034 (52.76)
706 (2.07)
27,598 (80.74)
443 (1.30)

7384 (41.71)
2279 (12.87)
5556 (31.39)
2598 (14.68)
2484 (14.03)
2676 (15.12)
1438 (8.12)2452 (13.85)
12,339 (69.70)
305 (1.72)
9343 (52.78)
429 (2.42)
14,212 (80.28)
258 (1.46)

4852 (29.44)
1757 (10.66)
4267 (25.89)
1782 (10.81)
1182 (7.17)
1915 (11.62)
1137 (6.90)
1799 (10.92)
12,009 (72.87)
222 (1.35)
8691 (52.73)
277 (1.68)
13,386 (81.22)
185 (1.12)(continued on next page)5

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Health and Place 89 (2024) 103336

Table 1 (continued )Receiving emotional social support, n (%)
missing
Receiving instrumental social support, n (%)
missing
Higher frequency of meeting friends, n (%)
missing
A large number of friends seen within a month, n (%)
missing
Taking on social roles, n (％)
missing
4. Pro-social/altruistic behaviors
Volunteering, n (%)
missing
Sharing skills and experience, n (%)
missing
5. Health behavior
Current smoking status, n (%)
missing
Current drinking status, n (%)
missing
Higher levels of eating vegetables and fruits, n (%)
missing
Longer walking time, n (%)
missing
Housebound (lower frequency of going out), n (%)
missing
Receiving health screening, n (%)
missing

Overall (n = 34,183)

Women (n = 17,702)

Men (n = 16,481)

32,161 (94.08)
541 (1.58)
32,306 (94.51)
533 (1.56)
12,846 (37.58)
1154 (3.38)
13,600 (39.79)
1122 (3.28)
19,690 (57.60)
821 (2.40)

17,171 (97.00)
235 (1.33)
16,664 (94.14)
326 (1.84)
7739 (43.72)
699 (3.95)
7584 (42.84)
658 (3.72)
11,631 (65.70)
475 (2.68)

14,990 (90.95)
306 (1.86)
15,642 (94.91)
207 (1.26)
5107 (30.99)455 (2.76)
6016 (36.50)
464 (2.82)8059 (48.90)
346 (2.10)4552 (13.32)
4770 (13.95)
2182 (6.38)
4612 (13.49)2403 (13.57)
2820 (15.93)
1230 (6.95)
2700 (15.25)

2149 (13.04)
1950 (11.83)
952 (5.78)
1912 (11.60)

3088 (9.03)
380 (1.11)
13,796 (40.36)368 (1.08)
27,819 (81.38)
178 (1.01)
27,314 (79.27)
455 (1.33)
513 (1.50)
374 (1.09)
22,290 (65.21)
625 (1.83)

541 (3.06)
214 (1.21)
3431 (19.38)
205 (1.16)
15,420 (87.11)
178 (1.01)
14,032 (79.27)
266 (1.50)
246 (1.39)
181 (1.02)
11,514 (65.04)
384 (2.17)

2547 (15.45)
166 (1.01)
10,365 (62.89)
163 (0.99)
15,420 (75.23)
203 (1.23)
13,282 (80.59)
189 (1.15)
267 (1.62)
193 (1.17)
10,776 (65.38)
241 (1.46)

Abbreviations: SD, standard deviation; BMI, Body Mass Index; GDS, Geriatric depression scale.
See Supplementary Table S1 for the definition of outcome and exposure variables.

community-level social cohesion (PR = 0.993, 95% CI: 0.973, 1.013),
and community-level reciprocity (PR = 1.004, 95% CI: 0.987, 1.018).
When conducting a subgroup analysis based on gender, the results
were generally similar between men and women for most outcomes.
However, there was some modest evidence of effect modification basedon gender (Supplementary Table S4). For example, higher communitylevel civic participation was associated with a lower prevalence of taking on social roles among men but not among women (P for interaction<0.001). Higher community-level social cohesion was more clearly
associated with higher prevalence of currently smoking among womenthan men (P for interaction = 0.040). Additionally, we found somemodest effect modifications of SES between community-level social
capital and some social well-being outcomes, although this was not clear(Supplementary Table S5). For example, for the low SES group, the effect sizes of the association between community-level civic participationand participation in hobby and sports groups were larger than those forthe high SES group.
In the sensitivity analyses, we used data from those who responded
to only the 2013 and 2016 surveys (n = 76,414) and imputed the outcomes in 2019. No substantial differences in demographics were foundcompared with the analytical sample, who answered all the 2013, 2016,
and 2019 surveys (Supplementary Table S6). These results were alsosimilar to the main results obtained using data from individuals who
responded to the three waves (Supplementary Table S7). Similar results
were also obtained in models with different years of education and income categories (Supplementary Table S8). Moreover, the E-values
showed some robustness in the observed associations between
community-level social capital and subsequent health and well-being.
For example, to explain the observed association between communitylevel reciprocity and taking on social roles, an unmeasured
confounder needs to be associated with both exposure and outcomes,
after controlling for the adjusted covariates, by a risk ratio of 1.204-foldeach and by a risk ratio of 1.153-fold to shift the CI to include the nullvalue (Table 3).

4. Discussion
To the best of our knowledge, this is the first study to examine theassociations between community-level social capital and multidimensional health and well-being using an outcome-wide longitudinal design.
This study has four main findings. First, community-level social capitalis associated with certain dimensions of human well-being, including
physical/cognitive health and health behavior. However, the observed
associations were modest or mixed, according to the outcomes and dimensions of social capital. For example, community-level civic participation was positively associated with lower risk of death and higher
competency in intellectual activity, whereas community-level social
cohesion was negatively associated with lower competency in intellectual activity. Second, we found that higher community-level civic
participation, social cohesion, and reciprocity were associated withseveral positive aspects of subsequent social well-being and pro-social/
altruistic behaviors according to the subtypes of social capital. Third,
there was little evidence of an association between community-levelsocial capital, mental health, and subjective well-being. Finally, there
was a modest effect modification based on gender and SES on the association between community-level social capital and social well-being,
whereas the exposure-outcome associations were comparable acrossgroups for most outcomes.
Some of our results are consistent with the trends from previous
studies, but there are some differences. This might be due to methodological differences from other studies, including 1) study design, 2)
measurement of community-level social capital, 3) number and types ofcovariates controlled, 4) sample composition, and 5) adjustments for all
the pre-baseline outcomes. Previous studies have shown that social
capital has protective effects on mental health, subjective well-being,
and health behaviors, such as alcohol use (De Silva et al., 2005; Ehsan
and Silva, 2015; Nyqvist et al., 2013; Bryden et al., 2013). However,these results were not observed in this study, possibly due to the study
design. A previous systematic review showed that community (ecological)-level social capital was associated with a reduced risk of common
mental disorders in a cross-sectional study. However, no longitudinal

6

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Health and Place 89 (2024) 103336

Table 2
Association between community-level social capital and subsequent health and well-being in 2019.

7

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Health and Place 89 (2024) 103336

contagion, 2) good access to services and amenities, and 3) good community coalitions.
Social contagion is the concept in which habits and behaviors spreadthrough a close social network (Kawachi and Berkman, 2014). In thecase of physical/cognitive health, individuals with higher civic participation may be more intellectually active as they are exposed to various
media to obtain information about activities and common topics ofconversation in their communities, which might prevent dementia andother diseases (Murayama et al., 2018). This may result in reduced riskof death. Communities with high reciprocity could provide and receive
social support and take on social roles in the community.
Good access to services and amenities may facilitate social wellbeing (Kawachi and Berkman, 2000). Communities with high civic
participation may have more facilities, systems, and services forengaging in social activities and gathering information. Easier access tosuch resources through social participation might have played a role inpotentially mitigating the decline in intellectual activities and a decreasein mortality.
According to the community coalition action theory, community
coalition is defined as bringing people together, expanding available
resources, and focusing on community concerns that will improve
community capacities and social outcomes (Butterfoss, 2013). In thisstudy, it could be thought that the community context of highcommunity-level social capital enhances the quality of community coalitions, such as community care meetings and regional councils formedby the administration, municipalities, and community residents. As aresult, social capacity could be improved by enriching social resources
and services to improve the health of residents living in the community,
which might influence social outcomes, such as individual social
participation, perceptions of community trust, and attachment.
However, evidence of a protective association between communitylevel social capital and subsequent physical/cognitive health was
limited or modest. The potential mechanisms of social capital and its

study has observed this relationship (Ehsan and Silva, 2015), which issimilar to our results. Additionally, a previous study published in the
JAGES demonstrated that community-level civic participation was
associated with depressive symptoms; however, this association disappeared after adjusting for individual-level civic participation (Yamaguchi et al., 2019). In our analysis, each domain of pre-baseline
individual social capital was adjusted so that we would have similarly
found no association. Additionally, no studies in the systematic review
of subjective well-being and social capital have used multilevel analysis(Nyqvist et al., 2013). Therefore, it is possible that they would haveobserved similar trends if they were using the same design and variables
to be adjusted. Furthermore, previous studies on physical health outcomes (Sundquist et al., 2014) and previous studies on the JAGES(Nakagomi et al., 2019; Noguchi et al., 2019; Noguchi et al., 2022; Aidaet al., 2013; Fujihara et al., 2019) showed a positive association betweensocial capital and physical/cognitive health, such as death, functional
ability, cognitive function, and chronic diseases (e.g., heart disease,diabetes, and stroke). In this study, community-level civic participation
was associated with reduced mortality. However, other associations
were modest and mixed. For example, there were modest associations
between community-level civic participation and higher competency inintellectual activity, community-level social cohesion, functional
disability, and diabetes, which may be due to the measurement ofcommunity-level social capital and adjustment for all the pre-baseline
outcomes. In a previous study conducted in Sweden, community-levelsocial capital was associated with all-cause mortality andcause-specific mortality (e.g., heart disease, diabetes, and stroke)(Sundquist et al., 2014). This Swedish study measured social capital via
a single neighborhood voting rate. This study used a validated composite
community-level social capital indicator, which may have affected thedifferences in the results.
Potential mechanisms linking community-level social capital tosubsequent health and well-being may be explained by 1) social
8

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Health and Place 89 (2024) 103336

Table 3
Robustness to unmeasured confounding (E-values) of associations between community-level social capital and subsequent health and well-being in 2019.
E-values
Community-level social capital
Civic participation
1. Physical/cognitive and mental health
No natural teeth remaining
Self-rated health
BMI
Instrumental independence
Having competency in Intellectual activity
Self-reported hypertension
Self-reported diabetes
Self-reported dyslipidemia
Self-reported stroke
Self-reported heart disease
Self-reported respiratory disease
Depressive symptoms
Hopeless
Loneliness
Death
Dementia
Functional disability (any level)
Functional disability (≥ level 2)
2. Subjective well-being
High happiness level
Life satisfaction
Having ikigai
3. Social well-being
Participation in hobby group
Participation in sports group
Participation in learning or cultural group
Participation in senior citizens clubHigher community trust
Perception of others’ intentions to help
Higher attachment to the residential area
Higher frequency of meeting friends
Large number of friends seen within a month
Receiving emotional social support
Receiving instrumental social support
Taking on social roles
4. Pro-social/altruistic behaviors
Volunteering
Sharing skills and experience
5. Health behavior
Current smoking status
Current drinking status
Higher levels of eating vegetables and fruits
Longer walking time
Housebound (lower frequency of going out)
Receiving health screening

Social cohesion

Reciprocity

Effect estimate

CI limit

Effect estimate

CI limit

Effect estimate

CI limit

1.196
1.050
1.096
1.295
1.117
1.096
1.116
1.180
1.030
1.105
1.144
1.036
1.137
1.142
1.441
1.271
1.133
1.130

1.000
1.000
1.000
1.080
1.067
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.275
1.000
1.000
1.000

1.407
1.061
1.067
1.105
1.096
1.085
1.186
1.047
1.357
1.111
1.030
1.094
1.041
1.057
1.069
1.343
1.322
1.395

1.128
1.000
1.000
1.000
1.002
1.000
1.080
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.172
1.171

1.197
1.031
1.046
1.058
1.060
1.087
1.123
1.036
1.247
1.092
1.147
1.051
1.019
1.069
1.060
1.161
1.086
1.275

1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000

1.151
1.049
1.048

1.000
1.000
1.000

1.108
1.049
1.101

1.000
1.000
1.000

1.040
1.030
1.042

1.000
1.000
1.000

1.227
1.269
1.442
1.052
1.037
1.053
1.006
1.091
1.094
1.271
1.064
1.152

1.166
1.209
1.293
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.073

1.040
1.091
1.100
1.301
1.192
1.248
1.142
1.060
1.105
1.259
1.194
1.093

1.000
1.000
1.000
1.098
1.163
1.211
1.113
1.000
1.000
1.000
1.000
1.000

1.092
1.069
1.191
1.165
1.051
1.033
1.076
1.111
1.089
1.280
1.492
1.204

1.000
1.000
1.000
1.000
1.000
1.000
1.026
1.000
1.000
1.000
1.339
1.153

1.326
1.383

1.239
1.187

1.033
1.132

1.000
1.000

1.207
1.196

1.111
1.000

1.388
1.023
1.072
1.088
1.068
1.085

1.086
1.000
1.000
1.000
1.000
1.000

1.320
1.028
1.021
1.026
1.412
1.079

1.000
1.000
1.000
1.000
1.126
1.000

1.029
1.062
1.077
1.072
1.147
1.184

1.000
1.000
1.028
1.000
1.000
1.132

Abbreviation: CI, confidence interval; BMI. Body Mass Index.
For information on the calculation of E-values, see VanderWeele and Ding(2017) for the formula.

effects on physical, cognitive, and mental health include 1) health behaviors and 2) psychosocial processes (Kawachi and Berkman, 2000).
Higher community-level social capital may have promoted 1) higher
individual-level social participation and 2) reduced stress owing tocohesion, which may have reduced the risk of lower IADL, self-reported
diabetes, and functional disability. In communities with high levels ofsocial cohesion, community residents may make efforts to maintain theirhealth. This might be because they may not want to cause trouble forother residents or feel embarrassed if seen as unhealthy or functionallydependent, which reflects the cohesive nature of Japanese society.
Another potential reason why these associations were not evident is thatthe follow-up period was too short (three years) and not enough for theeffects of community-level social capital to manifest. In this study, weobserved a clear association between community-level social capital andphysical health only for death. If there is an association between social
capital and death, it could be through the mechanism described above,

but the other associations with physical health are modest. Therefore,
the results must be interpreted carefully.
This study found modest and potentially harmful associations between community-level social capital and several outcomes. For
example, higher community-level social cohesion and being housebound (lower frequency of going out), higher community-level reciprocity and a lower probability of volunteering. Regarding social
contagion, both positive and adverse health effects of community factors
can spread (Christakis and Fowler, 2008). Solid connections and cohesion might harm some aspects of older adults’ health. Specifically, theexclusion of outsiders (those who are not integrated into such communities) might cause them to go out less. However, a negative association
with an outcome does not necessarily imply that the outcome has a truly
harmful association with health. Higher reciprocity usually involves thenorm of helping each other within the community and may not be
considered volunteering.
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Health and Place 89 (2024) 103336

Moreover, the direction of the effect could be opposite, depending on
the aspects of social capital. For example, competency in intellectual
activity was positively associated with community-level civic participation but negatively associated with social cohesion. Additionally,taking on social roles was negatively associated with community-level
civic participation but positively associated with reciprocity. This difference may be explained by the different properties of each aspect ofsocial capital. Previous studies on the JAGES have also indicated thatdifferent aspects of community-level social capital may have opposing
associations with outcomes (Saito et al., 2017; Fujihara et al., 2019).
When considering social capital, it is important to consider its negative
influences as well.
We observed almost homogeneous associations between communitylevel social capital and well-being outcomes based on gender or SES.
However, we observed some heterogeneous associations in several
outcomes. One possible mechanism for the heterogeneity based on
gender and SES is social exclusion (Portes, 1998). In this study, the association between higher community-level civic participation and alower prevalence of taking on social roles was more evident among men
than among women. According to our data, men were less likely toparticipate in social activities in the pre-baseline than women were. Men
who are less involved in such social activities might be marginalized in
the community and have fewer opportunities to engage with neighboring friends and younger people. Another possible mechanism is social contagion owing to strong community ties (Villalonga-Olives andKawachi, 2017). The association between community-level social
cohesion and the risk of smoking was observed only among women,
which may have been more strongly influenced by smokers in morecohesive communities where women have greater networks compared
to men. Moreover, for some social well-being outcomes, such as
participation in hobby and sports groups, participants with low SES
might be more positively affected by community-level social capital. In
communities with higher social capital, health-related information andbehaviors may spread to individuals with low SES. Individuals with low
SES were more likely to be affected by community-level social capitalthan those with high SES by compensating for health-affecting factors
associated with low SES. For example, individuals with low SES mightlack available resources and information compared to those with highSES (Adler and Ostrove, 1999). Community-level social capital may
have facilitated information contagion to individuals with low SES andbuffered the health disparities associated with SES.
This study has some strengths. First, this is the first study to use an
outcome-wide longitudinal approach to assess the association between
community-level social capital and holistic health and well-being. Second, we utilized a large-scale nationwide longitudinal cohort, including
over 30,000 analytical samples, rich in survey items encompassing
physical, mental, and social factors. The longitudinal study design
enabled us to ensure the temporal ordering between exposure and outcomes and adjust for pre-baseline covariates controlling for confounding
while avoiding the overadjustment of potential mediators. Third, thecomprehensive data allowed us to adjust for a broad range of prebaseline potential confounders. Such adjusted covariates include prebaseline outcome values, which addresses reverse causality, at least
partially. Lastly, our geographically diverse sample and large sample
size per community enabled us to use the validated scale of communitylevel social capital.
This study has several limitations. First, our results on the effects ofcommunity-level social capital may be conservative because the threeyear follow-up period may not be sufficiently long for communitylevel factors to manifest. Moreover, community-level social capitalmight have a small effect size, which may not have been detected owing
to the lack of statistical power and a more conservative threshold forstatistical significance after multiple testing corrections. Second, most of
the outcomes were self-reported and may have been susceptible to information bias. However, this study also included objectively measured
outcomes from LTCI registry, which are less susceptible to information

bias. Third, we did not set a criterion for the number of individuals
included in the school districts in this study. The number of individuals
within the school districts we included in this study ranged from eight to1308, with an average of 108 people for the sample who answered the2013 and 2016 surveys from which community-level social capital was
calculated (Supplementary Table S2). This suggested that the small
number of individuals within the school districts might have reduced theaccuracy of the estimates. We did not establish criteria to prevent selection bias by excluding participants and ensuring that the sample was
as large as possible. Furthermore, it is important to note that school
districts may not necessarily be appropriate geographical units. It remains unclear whether similar results would be obtained if this scale
were applied to a community defined by an alternative geographic unit.
Fourth, we did not adjust for the pre-baseline levels of the exposure
because community-level social capital could be relatively stable overtime and had limited variability between the pre-baseline and baseline
waves. In this case, the estimated effects corresponded to the cumulative
exposure to community social capital rather than the incident changes atthe baseline. Fifth, the response rate of the target population wasapproximately 70%, and the follow-up rate was approximately 30–40%,
leaving the possibility of selection bias owing to selective attrition.
However, we reviewed the demographics and pre-baseline outcome
values of the participants who were lost to the follow-up in 2019 andconfirmed that they were nearly the same as those of the analyticalsamples (Supplementary Table S9). We also conducted a sensitivity
analysis with imputed outcomes for the respondents who answered in
the two waves of the 2013 and 2016 surveys. These results were similar
to the main results (Supplementary Table S7). Sixth, the study adjusted
for a comprehensive set of covariates. However, because it used observational data, the possibility of unmeasured confounders remained. The
results of the E-value analyses were somewhat robust to the unmeasured
confounders, although some of the E-values were not sufficiently large
to deny the existence of unmeasured confounders that could explain theobserved results. Thus, it is crucial to recognize that our statisticalfindings from the observational data may not necessarily indicate thecausal effect of community social capital. Seventh, we divided SES into
only two categories using educational status and household income,which may have prevented us from observing clear heterogeneity. To
assess heterogeneity in detail, it is necessary to approach SES from amore multidimensional perspective (e.g., occupational status) or use
another method, such as a machine learning-based approach (Athey
et al., 2019). Eighth, the outcome-wide approach addresses a broad
range of outcomes and does not allow for an in-depth discussion of eachoutcome individually (VanderWeele et al., 2020). Contrarily, the conventional approach of assessing a single outcome does not provide insights into the impacts of exposure on a holistic range of well-being
outcomes and has other methodological limitations, such as p-hacking
and publication bias (VanderWeele, 2017b). These approaches playcomplementary roles in promoting our understanding of social capitaland health. Finally, the findings were based on a limited population ofJapanese older adults aged 65 years and above who responded to thesurvey in multiple waves. The results may vary depending on differencesin social and cultural context, such as age and social norms. It should be
noted that the results might not be generalizable to other populations.
5. Conclusion
Community-level social capital may promote social well-being andsome aspects of physical and cognitive health, such as reducing the riskof death and functional disability among older adults, regardless ofgender and SES. Therefore, fostering community-level social capital may
improve individual-level social well-being and physical and cognitive
health without increasing health inequality. Meanwhile, communitylevel social capital did not show clear associations with most aspectsof physical, cognitive, and mental health, subjective well-being, andhealth behaviors. Depending on the type of social capital, community10

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Health and Place 89 (2024) 103336

from Japan Agency for Medical Research and development (AMED)(JP18dk0110027, JP18ls0110002, JP18le0110009, JP20dk0110034,
JP21lk0310073, JP21dk0110037, JP22lk0310087), the Research
Funding for Longevity Sciences from National Center for Geriatrics andGerontology (24-17, 24-23, 29–42, 30-30, 30-22, 20-19, 21-20). The
sponsors had no role in the study design, in the collection or interpretation of the data, in writing the report, or in the decision to submit thearticle for publication.

level social capital may have detrimental effects on several health outcomes. Therefore, to maximize well-being among older adults, especially to improve aspects not associated with or negatively associatedwith community-level social capital in this study, additional implementations in the social environments may be necessary. Further studies
are required to examine the heterogeneous and long-term effects ofcommunity-level social capital in more detail.
Data availability statement

Appendix A. Supplementary data

The data underlying this article were provided by the JAGES data
administration office by permission. Data will be shared on request to
the corresponding author with the permission of the JAGES data
administration office (https://www.jages.net/data_application/).

Supplementary data to this article can be found online at https://doi.
org/10.1016/j.healthplace.2024.103336.
References

Ethical approvals

Adler, N.E., Ostrove, J.M., 1999. Socioeconomic status and health: what we know andwhat we don’t. In: Annals of the New York Academy of Sciences. New York Academyof Sciences, pp. 3–15. https://doi.org/10.1111/j.1749-6632.1999.tb08101.x.
Aida, J., Kondo, K., Kawachi, I., Subramanian, S.V., Ichida, Y., Hirai, H., Kondo, N.,
Osaka, K., Sheiham, A., Tsakos, G., Watt, R.G., 2013. Does social capital affect theincidence of functional disability in older Japanese? A prospective population-based
cohort study. J. Epidemiol. Community Health 67 (1978), 42–47. https://doi.org/
10.1136/jech-2011-200307.
Amemiya, A., Saito, J., Saito, M., Takagi, D., Haseda, M., Tani, Y., Kondo, K., Kondo, N.,
2019. Social capital and the improvement in functional ability among older people
in Japan: a multilevel survival analysis using JAGES data. Int. J. Environ. Res. Publ.
Health 16. https://doi.org/10.3390/ijerph16081310.
Athey, S., Tibshirani, J., Wager, S., 2019. Generalized random forests. Ann. Stat. 47,
1179–1203. https://doi.org/10.1214/18-AOS1709.
Bryden, A., Roberts, B., Petticrew, M., McKee, M., 2013. A systematic review of theinfluence of community level social factors on alcohol use. Health Place 21, 70–85.
https://doi.org/10.1016/j.healthplace.2013.01.012.
Butterfoss, F.D., 2013. Toward a comprehensive understanding of community coalitions:moving from practice to theory. In: Emerging Theories in Health Promotion Practiceand Research, second ed. Jossey-Bass.
Centers for Disease Control and Prevention, 2018. Well-Being Concepts. https://www.
cdc.gov/hrqol/wellbeing.htm. (Accessed 12 June 2023).
Christakis, N.A., Fowler, J.H., 2008. The collective dynamics of smoking in a large social
network. N. Engl. J. Med. 358, 2249–2258. https://doi.org/10.1056/
NEJMsa0706154.
De Silva, M.J., McKenzie, K., Harpham, T., Huttly, S.R.A., 2005. Social capital andmental illness: a systematic review. J. Epidemiol. Community Health. https://doi.
org/10.1136/jech.2004.029678, 1978.
Ehsan, A.M., Silva, D., 2015. Social capital and common mental disorder: a systematic
review. J. Epidemiol. Community Health 69, 1021–1028. https://doi.org/10.1136/
jech.
Fujihara, S., Tsuj, T., Miyaguni, Y., Aida, J., Saito, M., Koyama, S., Kondo, K., 2019. Does
community-level social capital predict decline in instrumental activities of dailyliving? A JAGES prospective cohort study. Int. J. Environ. Res. Publ. Health 16.
https://doi.org/10.3390/ijerph16050828.
Haseda, M., Kondo, N., Ashida, T., Tani, Y., Takagi, D., Kondo, K., 2018. Community
social capital, built environment, and income-based inequality in depressive
symptoms among older people in Japan: an ecological study from the JAGES project.
J. Epidemiol. 28, 108–116. https://doi.org/10.2188/jea.JE20160216.
Huber, M., André Knottnerus, J., Green, L., Van Der Horst, H., Jadad, A.R., Kromhout, D.,
Leonard, B., Lorig, K., Loureiro, M.I., Van Der Meer, J.W.M., Schnabel, P., Smith, R.,
Van Weel, C., Smid, H., 2011. How should we define health? BMJ (Online) 343.
https://doi.org/10.1136/bmj.d4163.
Islam, M.K., Merlo, J., Kawachi, I., Lindström, M., Gerdtham, U.G., 2006. Social capitaland health: does egalitarianism matter? A literature review. Int. J. Equity Health.
https://doi.org/10.1186/1475-9276-5-3.
Kawachi, I., Berkman, L.F., 2014. Social capital, social cohesion, and health. In:
Berkman, L.F., Kawachi, I., Glymour, M.M. (Eds.), Social Epidemiology, second ed.
Oxford University Press, pp. 290–319.
Kawachi, I., Subramanian, S.V., 2006. Measuring and modeling the social and geographic
context of trauma: a multilevel modeling approach. J. Trauma Stress 195–203.
https://doi.org/10.1002/jts.20108.
Kawachi, I., Subramanian, S.V., Kim, D., 2008. Social capital and health: a decade ofprogress and beyond. In: Kawachi, I., Subramanian, S.V., Kim, D. (Eds.), Social
Capital and Health, first ed. Springer, pp. 1–26.
Kawachi, I., Berkman, L., 2000. Social cohesion, social capital, and health. In:
Berkman, L.F., Kawachi, I. (Eds.), Social Epidemiology. Oxford University Press,
pp. 174–190.
Kim, E.S., Chen, Y., Kawachi, I., VanderWeele, T.J., 2020. Perceived neighborhood social
cohesion and subsequent health and well-being in older adults: an outcome-widelongitudinal approach. Health Place 66. https://doi.org/10.1016/j.
healthplace.2020.102420.
Kondo, K., Rosenberg, M., 2018. Advancing Universal Health Coverage throughKnowledge Translation for Healthy Ageing: Lessons Learnt from the JapanThe ethics committees of Kyoto University (R3153-2), Nihon Fukushi
University (13–14), and Chiba University (2493; 3442) approved thisstudy.
CRediT authorship contribution statement
Sho Takeda: Conceptualization, Data curation, Formal analysis,
Investigation, Methodology, Project administration, Software, Validation, Writing – original draft, Writing – review & editing. Maho Haseda:
Conceptualization, Methodology, Project administration, Writing –original draft, Writing – review & editing. Koryu Sato: Methodology,
Supervision, Writing – review & editing. Koichiro Shiba: Formal
analysis, Methodology, Writing – review & editing. Atsushi Nakagomi:
Formal analysis, Methodology, Project administration, Writing – review& editing. Kazushige Ide: Formal analysis, Methodology, Project
administration, Writing – review & editing. Naoki Kondo: Conceptualization, Data curation, Funding acquisition, Methodology, Project
administration, Resources, Supervision, Writing – original draft, Writing– review & editing.
Declaration of competing interest
None declared.
Data availability
Data will be made available on request.
Acknowledgment
This study used data from JAGES (the Japan Gerontological Evaluation Study). This study was supported by Grant-in-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, 25713027, 26285138, 26460828, 26780328,
18H03018, 18H04071, 18H03047, 18H00953, 18H00955, 18KK0057,
19H03901, 19H03915, 19H03860, 19K04785, 19K10641, 19K11657,
19K19818, 19K19455, 19K24060, 19K20909, 20H00557, 21K19635,
21H03153) from JSPS (Japan Society for the Promotion of Science);
Health Labour Sciences Research Grants (H26-Choju-Ippan-006, H27Ninchisyou-Ippan-001 H28- Choju-Ippan-002, H28-Ninchisyou-Ippan002,
H30-Kenki-Ippan-006,
H29-Chikyukibo-Ippan-001,
H30Jyunkankinado-Ippan-004,
19FA1012,
19FA2001,
21FA1012,
22FA2001, 22FA1010), Research project on health and welfare promotion for the elderly from the Ministry of Health, Labour and Welfare,Japan; the Research and Development Grants for Longevity Science11

S. Takeda et al.

Health and Place 89 (2024) 103336
outcome-wide analysis. Lancet Reg Health West Pac 21, 100391. https://doi.org/
10.1016/j.
Portes, A., 1998. Social capital: its origins and applications in modern sociology. Annu.
Rev. Sociol. 24 https://doi.org/10.1146/annurev.soc.24.1.1.
Rodgers, J., Valuev, A.V., Hswen, Y., Subramanian, S.V., 2019. Social capital andphysical health: an updated review of the literature for 2007–2018. Soc. Sci. Med.
https://doi.org/10.1016/j.socscimed.2019.112360.
Ryff, C.D., Boylan, J.M., Krisch, J.A., 2021. Eudaimonic and hedonic well-being: an
integrative perspective with linkages to sociodemographic factors and health. In:
Lee, M.T., Kubzansky, L.D., VanderWeele, T.J. (Eds.), Measuring Well-Being:
Interdisciplinary Perspectives from the Social Science and the Humanities. Oxford
University Press, pp. 92–135.
Saito, M., Kondo, N., Aida, J., Kawachi, I., Koyama, S., Ojima, T., Kondo, K., 2017.
Development of an instrument for community-level health related social capital
among Japanese older people: the JAGES project. J. Epidemiol. 27, 221–227.
https://doi.org/10.1016/j.je.2016.06.005.
Sundquist, K., Hamano, T., Li, X., Kawakami, N., Shiwaku, K., Sundquist, J., 2014.
Linking social capital and mortality in the elderly: a Swedish national cohort study.
Exp. Gerontol. 55, 29–36. https://doi.org/10.1016/j.exger.2014.03.007.
Uphoff, E.P., Pickett, K.E., Cabieses, B., Small, N., Wright, J., 2013. A systematic reviewof the relationships between social capital and socioeconomic inequalities in health:a contribution to understanding the psychosocial pathway of health inequalities. Int.
J. Equity Health 12, 54. https://doi.org/10.1186/1475-9276-12-54.
VanderWeele, T.J., 2017a. On the promotion of human flourishing. Proc. Natl. Acad. Sci.
USA 114, 8148–8156. https://doi.org/10.1073/pnas.1702996114.
Vanderweele, T.J., 2017b. Outcome-wide epidemiology. Epidemiology 28, 399–402.
https://doi.org/10.1097/EDE.0000000000000641.
VanderWeele, T.J., Ding, P., 2017. Sensitivity analysis in observational research:
introducing the E-value. Ann. Intern. Med. 167, 268. https://doi.org/10.7326/M162607.
VanderWeele, T.J., Mathur, M.B., Chen, Y., 2020. Outcome-wide longitudinal designs forcausal inference: a new template for empirical studies. Stat. Sci. 35 https://doi.org/
10.1214/19-sts728.
Villalonga-Olives, E., Kawachi, I., 2017. The dark side of social capital: a systematic
review of the negative health effects of social capital. Soc. Sci. Med. 194, 105–127.
https://doi.org/10.1016/j.socscimed.2017.10.020.
Yamaguchi, M., Inoue, Y., Shinozaki, T., Saito, M., Takagi, D., Kondo, K., Kondo, N.,
2019. Community social capital and depressive symptoms among older people inJapan: a multilevel longitudinal study. J. Epidemiol. 29, 363–369. https://doi.org/
10.2188/jea.JE20180078.
Young, A.F., Russell, A., Powers, J.R., 2004. The sense of belonging to a neighbourhood:
can it be measured and is it related to health and well being in older women? Soc.
Sci. Med. 59, 2627–2637. https://doi.org/10.1016/j.socscimed.2004.05.001.
Zou, G., 2004. A modified Poisson regression approach to prospective studies with binary
data. Am. J. Epidemiol. 159, 702–706. https://doi.org/10.1093/aje/kwh090.
Zuidberg, M.R.J., Shriwise, A., De Boer, L.M., Johansen, A.S., 2020. Assessing progressunder health 2020 in the European region of the world health organization. Eur. J.
Publ. Health 30, 1072–1077. https://doi.org/10.1093/eurpub/ckaa091.

Gerontological Evaluation Study. World Health Organization, Geneva. https://apps.
who.int/iris/handle/10665/279010.
Koyama, S., Aida, J., Saito, M., Kondo, N., Sato, Y., Matsuyama, Y., Tani, Y., Sasaki, Y.,
Kondo, K., Ojima, T., Yamamoto, T., Tsuboya, T., Osaka, K., 2016. Community social
capital and tooth loss in Japanese older people: a longitudinal cohort study. BMJ
Open 6, 10768. https://doi.org/10.1136/bmjopen-2015.
Mathews, G., 1996. What Makes Life Worth Living? How Japanese and Americans Make
Sense of Their Worlds. University of California Press.
Ministry of Health, Labor and Welfare, 2017. Long-Term Care, Health and Welfare
Services for the Elderly. https://www.mhlw.go.jp/english/policy/care-welfare/carewelfare-elderly/index.html. (Accessed 12 June 2023).
Murata, C., Kondo, K., Hirai, H., Ichida, Y., Ojima, T., 2008. Association between
depression and socio-economic status among community-dwelling elderly in Japan:
the Aichi Gerontological Evaluation Study (AGES). Health Place 14, 406–414.
https://doi.org/10.1016/j.healthplace.2007.08.007.
Murayama, H., Fujiwara, Y., Kawachi, I., 2012. Social capital and health: a review ofprospective multilevel studies. J. Epidemiol. https://doi.org/10.2188/jea.
JE20110128.
Murayama, H., Sugiyama, M., Inagaki, H., Okamura, T., Miyamae, F., Ura, C.,
Edahiro, A., Motokawa, K., Awata, S., 2018. Is community social capital associatedwith subjective symptoms of dementia among older people? A cross-sectional study
in Japan. Geriatr. Gerontol. Int. 18, 1537–1542. https://doi.org/10.1111/ggi.13519.
Nakagomi, A., Shiba, K., Kawachi, I., Ide, K., Nagamine, Y., Kondo, N., Hanazato, M.,
Kondo, K., 2022. Internet use and subsequent health and well-being in older adults:
an outcome-wide analysis. Comput. Hum. Behav. 130 https://doi.org/10.1016/j.
chb.2021.107156.
Nakagomi, A., Tsuji, T., Saito, M., Ide, K., Kondo, K., Shiba, K., 2023. Social isolation andsubsequent health and well-being in older adults: a longitudinal outcome-wide
analysis. Soc. Sci. Med. 327 https://doi.org/10.1016/j.socscimed.2023.115937.
Nakagomi, A., Tsuji, T., Hanazato, M., Kobayashi, Y., Kondo, K., 2019. Association
between community-level social participation and self-reported hypertension inolder Japanese: a JAGES multilevel cross-sectional study. Am. J. Hypertens. 32,
503–514. https://doi.org/10.1093/ajh/hpz028.
Noguchi, T., Kondo, K., Saito, M., Nakagawa-Senda, H., Suzuki, S., 2019. Community
social capital and the onset of functional disability among older adults in Japan: amultilevel longitudinal study using Japan Gerontological Evaluation Study (JAGES)
data. BMJ Open 9. https://doi.org/10.1136/bmjopen-2019-029279.
Noguchi, T., Murata, C., Hayashi, T., Watanabe, R., Saito, M., Kojima, M., Kondo, K.,
Saito, T., 2022. Association between community-level social capital and frailty onset
among older adults: a multilevel longitudinal study from the Japan Gerontological
Evaluation Study (JAGES). J. Epidemiol. Community Health 76 (1978), 182–189.
https://doi.org/10.1136/jech-2021-217211.
Nyqvist, F., Forsman, A.K., Giuntoli, G., Cattan, M., 2013. Social capital as a resource formental well-being in older people: a systematic review. Aging Ment. Health 17,
394–410. https://doi.org/10.1080/13607863.2012.742490.
Okuzono, S.S., Shiba, K., Kim, E.S., Shirai, K., Kondo, N., Fujiwara, T., Kondo, K.,
Lomas, T., Trudel-Fitzgerald, C., Kawachi, I., Vanderweele, T.J., 2022. Ikigai andsubsequent health and wellbeing among Japanese older adults: longitudinal12