Background
In public health, the concept of social capital has merited attention for more than a decade as a potential approach to health promotion [
1]. The concept is popular, and some believe that social capital is a panacea to improve health for everyone [
2]. Indeed, one of primary criticisms of social capital research in the public health arena has been that researchers have tended to emphasize the positive effects on health [
1]. One approach to address this issue is to distinguish between the effects of bonding and bridging social capital; bonding social capital refers to trusting and cooperative relations with members who are similar in terms of social identity (e.g., social class), whereas bridging social capital refers to connections among individuals who are dissimilar [
3]. They have been thought to have differential effects on health outcomes, and strong bonding social capital can sometimes be seen as detrimental to health since it may impose a burden on people’s already stressful lives [
4]. To date, however, only a small amount of research on bonding/bridging social capital has separately examined their effects on health and most of these studies used race and ethnicity to evaluate bonding and bridging social capital. For example, in a study from the United States [
5], bonding and bridging dimensions of social capital were assessed based on the mix of ethnicity in communities. By using cross-sectional data, this study reported that bonding social capital was beneficial to self-rated health and to reducing stress, whereas bridging social capital did not provide a definitive association with these health outcomes, thereby contradicting the theoretical understanding of bonding/bridging social capital. Although this conceptualization of bonding/bridging social capital makes sense in certain social context (e.g., multiethnic nations/groups), it fails to capture meaningful insight in other social contexts, e.g., in an almost ethnically homogenous nation like Japan. To address this concern, a recent community-based study from Japan measured social capital from responses from participants in several groups, and assessed whether each group represented bonding or bridging social capital, measured by diversity of gender, age, and occupation [
6]. They found that bridging social capital was associated with better self-rated health in both sexes, whereas the association was less consistent for bonding social capital. Additionally, a differential pattern was found by sex: women benefited more from bridging social capital than men, whereas men may benefit more from bonding capital than women.
Although most developed countries (including Japan) now face multiple challenges associated with population aging and overall population decline, few studies have examined the effects of social capital on health, specifically among the elderly. It has long been hypothesized that the elderly may be particularly vulnerable to the health-enhancing or health-damaging aspects of residential environments [
7]. They may also need to rely on community resources for services, and their daily activities (e.g., food shopping, food consumption, recreation, and social interactions) may often take place in the vicinity of their homes. Hence, both their exposure to community conditions and the degree to which those conditions are relevant to their health may be greater than they are for other age groups [
8]. For such a reason, the effect of social capital could be important for the elderly. Given the global trend of population aging, there is an interest in further investigating the association between social capital in a community context and health among the elderly in Japan [
9‐
12], which precedes other countries in experiencing a “super-aging” society [
13]. The findings would also be of interest as Japan has built its own particular style of social cohesion owing to its history [
14,
15]. To date, five studies have examined the association between social capital and health among the elderly in Japan, all of which are from the Aichi Gerontological Evaluation Study (AGES) or the Ohsaki Cohort 2006 Study [
16‐
20]. Although three of these five studies examined the effects of social capital on dental health [
16,
18,
19], these previous studies have implied beneficial effects of social capital on general health among the elderly. For example, a cohort study based on the data of AGES suggested that a smaller friendship network was significantly associated with higher all-cause mortality in both sexes [
17].
To our knowledge, only one study from China has examined the association between social capital and health in the elderly by distinguishing bonding and bridging social capital. Norstrand and Xu [
21] used cross-sectional data from 1250 subjects aged 65 or older and found that bonding social capital (which was measured as trust on family members, friends, neighbors, etc.) is associated with better physical and emotional health among urban residents. By contrast, they found no clear association between bridging social capital (which was measured as the extent to which people help each other in organizations they have participated in) and health outcomes. It is worthy to note, however, that their findings cannot be necessarily applied to social capital in a community context since they operationalized bonding social capital with a measure of trust that does not uniquely refer to communities and bridging social capital with a measure of reciprocity, both in different social contexts.
Accordingly, we sought to investigate the association between bonding and bridging social capital and self-rated health, using a large population-based sample of elderly Japanese people. As remarked above, it would be of a particular interest to investigate their possible differential effects on health among the elderly in a “super-aging” society like Japan. Following a previous study from Japan [
6], we conceptualized the two dimensions of social capital from the perspective of group involvement and examined the association for men and women separately. To address an issue of unmeasured confounding between social capital and health, we adjusted for type-D personality in this study, which consists of negative affectivity (i.e., the tendency to experience negative emotions across time/situations) and social inhibition (i.e., the tendency to inhibit the expression of emotions/behaviors in social interaction to avoid being against others) [
22,
23].
Results
Of the 11146 subjects, the prevalence of poor self-rated health was 29.95% for men and 29.44% for women. The demographic characteristics of the subjects are shown in Table
1. Men were more likely to be smokers and consumed alcohol more frequently. The proportions of overweight participants and those with type-D personality were similar between the sexes. The number of bonding/bridging group involvements was inversely associated with poor self-rated health in both sexes (p < 0.01; Table
2). This pattern was consistently observed for total group involvement.
Table 1
Demographic characteristics of the subjects responding to a questionnaire, Japan, 2010
Mean age (SD) | 76.13 (6.93) | 77.31 (7.54) |
Educational attainment (%) | | |
Junior high school | 2092 (47.11) | 2927 (43.65) |
High school | 1721 (38.75) | 2816 (42.00) |
Some college or more | 453 (10.20) | 518 (7.73) |
Missing | 175 (3.94) | 444 (6.62) |
Smoking (%) | | |
Never/former | 3487 (78.52) | 5988 (89.31) |
Current | 765 (17.23) | 104 (1.55) |
Missing | 189 (4.26) | 613 (9.14) |
Alcohol consumption (%) | | |
None | 1470 (33.10) | 4817 (71.84) |
1-3 times/month | 510 (11.48) | 749 (11.17) |
1-6 times/week | 957 (21.55) | 566 (8.44) |
Everyday | 1451 (32.67) | 195 (2.91) |
Missing | 53 (1.19) | 378 (5.64) |
Body mass index (%) | | |
<25 kg/m2
| 3534 (79.58) | 5202 (77.58) |
≥25 kg/m2
| 740 (16.66) | 1106 (16.50) |
Missing | 167 (3.76) | 397 (5.92) |
Live alone (%) | | |
Yes | 473 (10.65) | 1,404 (20.94) |
No | 3798 (85.52) | 4958 (73.94) |
Missing | 170 (3.83) | 343 (5.12) |
Type-D personality (%) | | |
Yes | 1853 (41.72) | 2674 (39.88) |
No | 2138 (48.14) | 3084 (46.00) |
Missing | 450 (10.13) | 947 (14.12) |
Self-rated health (%) | | |
Good health | 3111 (70.05) | 4731 (70.56) |
Poor health | 1330 (29.95) | 1974 (29.44) |
Table 2
The distribution of the number of group involvement and poor self-rated health stratified by sex
The number of total group involvement | | | | | | |
0 | 878 | 22.27 | 495 | 50.05 | | 2032 | 30.31 | 899 | 44.24 | |
1 | 830 | 18.69 | 270 | 32.53 | | 1458 | 21.74 | 462 | 31.69 | |
2 | 837 | 18.85 | 212 | 25.33 | | 1293 | 19.28 | 295 | 22.82 | |
3 | 709 | 15.96 | 152 | 21.44 | | 978 | 14.59 | 171 | 17.48 | |
4 | 568 | 12.79 | 98 | 17.25 | | 575 | 8.58 | 99 | 17.22 | |
5 | 338 | 7.61 | 68 | 20.12 | | 269 | 4.01 | 36 | 13.38 | |
6 | 170 | 3.83 | 35 | 20.59 | <0.001 | 100 | 1.49 | 12 | 12.00 | <0.001 |
The number of bonding group involvement | | | | | | |
0 | 3058 | 68.86 | 1009 | 33.00 | | 4681 | 69.81 | 1554 | 33.20 | |
1 | 905 | 20.38 | 231 | 25.52 | | 1301 | 19.40 | 294 | 22.60 | |
2 | 319 | 7.18 | 61 | 19.12 | | 503 | 7.50 | 91 | 18.09 | |
3 | 105 | 2.36 | 18 | 17.14 | | 159 | 2.37 | 24 | 15.09 | |
4 | 34 | 0.77 | 8 | 23.53 | | 45 | 0.67 | 10 | 22.22 | |
5 | 15 | 0.34 | 2 | 13.33 | | 12 | 0.18 | 1 | 8.33 | |
6 | 5 | 0.11 | 1 | 20.00 | <0.001 | 4 | 0.06 | 0 | 0.00 | <0.001 |
The number of bridging group involvement | | | | | |
0 | 1319 | 29.70 | 582 | 44.12 | | 2752 | 41.04 | 1083 | 39.35 | |
1 | 1022 | 23.01 | 303 | 29.65 | | 1677 | 25.01 | 461 | 27.49 | |
2 | 822 | 18.51 | 191 | 23.24 | | 1103 | 16.45 | 234 | 21.21 | |
3 | 624 | 14.05 | 126 | 20.19 | | 641 | 9.56 | 121 | 18.88 | |
4 | 377 | 8.49 | 77 | 20.42 | | 336 | 5.01 | 52 | 15.48 | |
5 | 194 | 4.37 | 40 | 20.62 | | 151 | 2.25 | 18 | 11.92 | |
6 | 83 | 1.87 | 11 | 13.25 | <0.001 | 45 | 0.67 | 5 | 11.11 | <0.001 |
Table
3 shows ORs for poor self-rated health associated with each type of group involvement. Most of the group involvements were (though not significant) associated with lower odds for poor self-rated health, even after adjusting for all covariates (Model 3). Overall, the associations were more pronounced among men; we found a total of five statistically significant associations, four of which were for men. Notably, bonding social capital for participating in an alumni association was significantly associated with lower odds of poor self-rated health for both sexes.
Table 3
Odds ratios for poor self-rated health associated with each group involvement, stratified by sex
Men | | | | | | | |
The elderly club, sports/hobby/culture circle | | | | | | | |
No involvement | 854 (36.92) | 1.00 | | 1.00 | | 1.00 | |
Bonding social capital | 129 (23.45) | 0.69 | (0.56-0.85) | 0.72 | (0.54-0.96) | 0.75 | (0.56-1.02) |
Bridging social capital | 300 (21.02) | 0.51 | (0.44-0.59) | 0.61 | (0.50-0.74) | 0.60 | (0.49-0.75) |
Alumni association | | | | | | | |
No involvement | 785 (40.93) | 1.00 | | 1.00 | | 1.00 | |
Bonding social capital | 160 (18.89) | 0.48 | (0.40-0.58) | 0.55 | (0.43-0.70) | 0.57 | (0.44-0.75) |
Bridging social capital | 335 (22.27) | 0.55 | (0.48-0.64) | 0.77 | (0.63-0.93) | 0.83 | (0.67-1.02) |
Political campaign club | | | | | | | |
No involvement | 1071 (31.66) | 1.00 | | 1.00 | | 1.00 | |
Bonding social capital | 31 (24.80) | 0.77 | (0.51-1.16) | 0.87 | (0.50-1.54) | 1.06 | (0.58-1.92) |
Bridging social capital | 187 (23.23) | 0.66 | (0.55-0.79) | 0.80 | (0.64-1.02) | 0.90 | (0.70-1.15) |
Citizen’s group, environmental preservation group | | | | | | | |
No involvement | 1027 (33.11) | 1.00 | | 1.00 | | 1.00 | |
Bonding social capital | 29 (17.16) | 0.47 | (0.31-0.71) | 0.53 | (0.31-0.89) | 0.55 | (0.32-0.95) |
Bridging social capital | 224 (22.36) | 0.60 | (0.51-0.71) | 0.92 | (0.74-1.14) | 1.00 | (0.80-1.26) |
Community association | | | | | | | |
No involvement | 808 (39.90) | 1.00 | | 1.00 | | 1.00 | |
Bonding social capital | 70 (23.41) | 0.71 | (0.54-0.93) | 0.65 | (0.44-0.96) | 0.71 | (0.47-1.07) |
Bridging social capital | 408 (20.43) | 0.42 | (0.37-0.49) | 0.62 | (0.51-0.75) | 0.69 | (0.56-0.84) |
Religious organization | | | | | | | |
No involvement | 1076 (31.32) | 1.00 | | 1.00 | | 1.00 | |
Bonding social capital | 36 (33.03) | 1.16 | (0.77-1.74) | 1.61 | (0.97-2.68) | 1.51 | (0.87-2.62) |
Bridging social capital | 183 (23.49) | 0.67 | (0.56-0.80) | 0.77 | (0.60-0.98) | 0.79 | (0.61-1.02) |
Women | | | | | | | |
The elderly club, sports/hobby/culture circle | | | | | | | |
No involvement | 1246 (36.68) | 1.00 | | 1.00 | | 1.00 | |
Bonding social capital | 206 (20.02) | 0.55 | (0.47-0.65) | 0.68 | (0.46-0.99) | 0.73 | (0.49-1.09) |
Bridging social capital | 436 (21.67) | 0.57 | (0.50-0.64) | 0.88 | (0.66-1.17) | 0.94 | (0.69-1.29) |
Alumni association | | | | | | | |
No involvement | 1334 (37.03) | 1.00 | | 1.00 | | 1.00 | |
Bonding social capital | 200 (18.50) | 0.49 | (0.41-0.58) | 0.64 | (0.45-0.93) | 0.63 | (0.43-0.93) |
Bridging social capital | 324 (19.95) | 0.51 | (0.45-0.59) | 0.63 | (0.45-0.87) | 0.75 | (0.53-1.05) |
Political campaign club | | | | | | | |
No involvement | 1739 (30.92) | 1.00 | | 1.00 | | 1.00 | |
Bonding social capital | 22 (20.75) | 0.62 | (0.39-1.00) | 1.46 | (0.38-5.58) | 1.36 | (0.35-5.27) |
Bridging social capital | 101 (17.50) | 0.48 | (0.38-0.60) | 0.59 | (0.36-0.97) | 0.66 | (0.39-1.13) |
Citizen’s group, environmental preservation group | | | | | | | |
No involvement | 1658 (32.10) | 1.00 | | 1.00 | | 1.00 | |
Bonding social capital | 30 (17.34) | 0.49 | (0.33-0.73) | 0.13 | (0.02-1.01) | 0.15 | (0.02-1.13) |
Bridging social capital | 153 (16.91) | 0.44 | (0.37-0.53) | 0.82 | (0.56-1.20) | 0.92 | (0.62-1.37) |
Community association | | | | | | | |
No involvement | 1324 (37.67) | 1.00 | | 1.00 | | 1.00 | |
Bonding social capital | 108 (20.30) | 0.59 | (0.47-0.73) | 0.86 | (0.53-1.41) | 0.81 | (0.48-1.37) |
Bridging social capital | 451 (19.20) | 0.43 | (0.38-0.49) | 0.69 | (0.51-0.93) | 0.77 | (0.56-1.06) |
Religious organization | | | | | | | |
No involvement | 1701 (30.56) | 1.00 | | 1.00 | | 1.00 | |
Bonding social capital | 27 (21.26) | 0.64 | (0.42-0.99) | 0.86 | (0.31-2.36) | 0.72 | (0.23-2.22) |
Bridging social capital | 155 (21.89) | 0.64 | (0.53-0.78) | 0.59 | (0.38-0.93) | 0.67 | (0.42-1.07) |
Table
4 shows the gender-stratified associations between different levels of bonding/bridging social capital and poor self-rated health. For men, both bonding and bridging social capital were inversely associated with poor self-rated health in Model 3, with apparently similar magnitudes (high bonding social capital; OR: 0.55, 95% CI: 0.31–0.99, high bridging social capital; OR: 0.62, 95% CI: 0.48–0.81). By contrast, for women, bonding social capital was significantly inversely associated with poor self-rated health while bridging social was not (high bonding social capital; OR: 0.34, 95% CI: 0.12–1.00, high bridging social capital; OR: 0.69, 95% CI: 0.44–1.07). After mutually adjusting for bonding and bridging social capital, no substantial change was observed, and bonding social capital remained significantly inversely associated with poor self-rated health in both sexes (Model 4).
Table 4
Odds ratios for poor self-rated health associated with social capital stratified by sex
Men | | | | | | | | | |
Social capital | | | | | | | | | |
None | 495 (50.05) | 1.00 | | 1.00 | | 1.00 | | | |
Low | 270 (32.53) | 0.48 | (0.40-0.58) | 0.70 | (0.52-0.94) | 0.72 | (0.52-0.98) | | |
Middle | 212 (25.33) | 0.34 | (0.28-0.41) | 0.55 | (0.41-0.73) | 0.55 | (0.40-0.76) | | |
H0069gh | 353 (19.78) | 0.25 | (0.21-0.29) | 0.41 | (0.32-0.53) | 0.46 | (0.35-0.61) | | |
Bonding social capital | | | | | | | | | |
None | 1009 (33.00) | 1.00 | | 1.00 | | 1.00 | | | |
Low | 231 (25.52) | 0.70 | (0.59-0.82) | 0.87 | (0.70-1.09) | 0.88 | (0.69-1.12) | 0.89 | (0.70-1.14) |
Middle | 61 (19.12) | 0.48 | (0.36-0.64) | 0.54 | (0.37-0.80) | 0.55 | (0.36-0.83) | 0.51 | (0.34-0.78) |
High | 29 (18.24) | 0.45 | (0.30-0.68) | 0.46 | (0.26-0.83) | 0.55 | (0.31-0.99) | 0.48 | (0.26-0.86) |
Bridging social capital | | | | | | | | | |
None | 582 (44.12) | 1.00 | | 1.00 | | 1.00 | | | |
Low | 303 (29.65) | 0.53 | (0.45-0.63) | 0.80 | (0.62-1.04) | 0.82 | (0.62-1.07) | 0.83 | (0.63-1.09) |
Middle | 191 (23.24) | 0.38 | (0.32-0.47) | 0.58 | (0.44-0.77) | 0.59 | (0.44-0.80) | 0.59 | (0.43-0.79) |
High | 254 (19.87) | 0.31 | (0.26-0.37) | 0.55 | (0.43-0.71) | 0.62 | (0.48-0.81) | 0.59 | (0.45-0.77) |
Women | | | | | | | | | |
Social capital | | | | | | | | | |
None | 899 (44.24) | 1.00 | | 1.00 | | 1.00 | | | |
Low | 462 (31.69) | 0.58 | (0.51-0.67) | 1.04 | (0.69-1.57) | 1.19 | (0.76-1.86) | | |
Middle | 295 (22.82) | 0.37 | (0.32-0.44) | 0.6 | (0.39-0.94) | 0.72 | (0.45-1.17) | | |
High | 318 (16.55) | 0.25 | (0.22-0.29) | 0.49 | (0.33-0.74) | 0.62 | (0.40-0.97) | | |
Bonding social capital | | | | | | | | | |
None | 1554 (33.20) | 1.00 | | 1.00 | | 1.00 | | | |
Low | 294 (22.60) | 0.59 | (0.51-0.68) | 0.78 | (0.55-1.11) | 0.78 | (0.54-1.13) | 0.76 | (0.53-1.10) |
Middle | 91 (18.09) | 0.44 | (0.35-0.56) | 0.68 | (0.42-1.10) | 0.70 | (0.42-1.17) | 0.65 | (0.39-1.09) |
High | 35 (15.91) | 0.38 | (0.26-0.55) | 0.39 | (0.15-1.01) | 0.34 | (0.12-1.00) | 0.31 | (0.10-0.89) |
Bridging social capital | | | | | | | | | |
None | 1083 (39.35) | 1.00 | | 1.00 | | 1.00 | | | |
Low | 461 (27.49) | 0.58 | (0.51-0.67) | 0.91 | (0.63-1.30) | 0.96 | (0.65-1.41) | 0.96 | (0.65-1.42) |
Middle | 234 (21.21) | 0.41 | (0.35-0.49) | 0.68 | (0.45-1.03) | 0.75 | (0.48-1.16) | 0.72 | (0.46-1.12) |
High | 196 (16.71) | 0.31 | (0.26-0.37) | 0.54 | (0.36-0.82) | 0.69 | (0.44-1.07) | 0.62 | (0.40-0.97) |
Discussion
By distinguishing bonding and bridging social capital, the present study examined their differential relationships with self-rated health in the Japanese elderly. The findings suggest that in men, both bonding and bridging social capital are inversely associated with poor self-rated health, even after adjusting for relevant covariates. By contrast, in women, the beneficial effect is more likely limited to bonding social capital, and the association between bridging social capital and self-rated health was less clear when adjusting for covariates. The present study complements previous findings by clarifying the most relevant domain(s) of social capital by distinguishing bonding and bridging social capital.
The same questionnaire on social capital was used by Iwase et al. [
6] on a general population (20–80 years old), who found that Japanese women benefited more from bridging social capital and men may benefit more from bonding social capital. Interestingly, this pattern was nearly reversed in the present study, which found that elderly men benefit from both bonding and bridging social capital, whereas elderly women benefit from bonding social capital. It is likely that today’s elderly men in Japan sought and enjoyed stronger associations with their colleagues until retirement in the “close-knit” nature of their companies [
34]. While increasing emphasis is placed on cooperation and collaboration inside the workplace [
35], they have gained more benefit from bonding social capital from ‘similar’ people in their companies, as implied in the proverb “birds of a feather flock together” [
36]. When this strong commitment to the company is lost after retirement, however, they may experience a variety of changes in living arrangements, which leads to changes in physical and mental health [
37]. While favorable effects of bonding social capital could remain after retirement, the loss of frequent connection with colleagues could create new challenges for them to establish relationships with community residents, resulting in involvement in new groups with greater diversity in composition respect to gender, age, and previous occupation. This may enable them to construct their identities more easily as their skills and experiences are more likely appreciated by other members in the groups, which could impose beneficial effects on their self-rated health.
The findings about elderly women from the present study may be interpreted from the perspective of flexibility to tolerate and acknowledge different opinions: although they could get along with diverse neighbors in early adulthood, their flexibility may decline with age. Another explanation may be that most women subjects had not been employed and that they have been involved in group activities in the communities over a long time. Even though they initially felt that the group was ‘diverse’, it might become more ‘similar’ over time. The adverse effect of bonding social capital tended to be referred in disadvantaged communities [
1]. Our study communities are located in general residential area, which may explain the present findings of bonding social capital. Our findings also suggest that the “threshold” for the beneficial effect of bonding social capital is different in men and women: for women, bonding social capital at the middle level was not significantly inversely associated with poor self-rated health, but it was in men. Although the reason remains unclear, there is a possibility of social desirability among women [
38], i.e., elderly women may have tended to over-report the number of group involvement, compared to elderly men.
Notably, unlike a previous Japanese study of general population [
6], our findings suggest that bonding social capital is inversely associated with poor self-rated health among the elderly men and women, even after adjusting for bridging social capital. It has been suggested that strong bonding social capital may have a harmful effect on health since it imposes a burden on people’s already stressful lives [
4]. Thus, our findings suggest that the “dark side” of bonding social capital may be outweighed by its “bright side” among the elderly. This finding may be partly attributed to a cohort effect; our study subjects were born before the end of World War II, and this generation may highly appreciate the tradition of social commitment, considering it as an opportunity to promote their well-being even if they are required to contribute much more to the groups they belong to.
In the examination of the associations between each type of group involvement and poor self-rated health, bonding social capital for alumni associations was significantly inversely associated with poor self-rated health for both sexes. We thus a posteriori hypothesize that the elderly tend to benefit from keeping in touch with old friends. Positive interaction and psychological support from old friends, who share the same circumstances, may be health-promoting, making group members feel a sense of solidarity. Furthermore, these group members may even get the urge to make an active contribution to the groups, which may in turn generate a sense of self-worth in them.
One criticism of social capital that has been repeatedly cited is the possibility that the association between social capital and health is confounded by unmeasured common cause(s), e.g., personality, early childhood environment, and genetic factors. To tackle this problem, type-D personality was controlled for as a covariate. To date, no studies have investigated the associations between social capital and health outcome by adjusting for type-D personality. Thus, the present study is expected to yield robust findings between social capital and health. On a related issue, the use of twin studies is increasingly recognized as an important approach in this field. For example, a recent study from the United States examined the association between social capital and health using data from adult twins [
39]. In general, twins are likely to share personality and their early childhood environment, which would provide an opportunity to remove confounding bias due to these factors. By eliminating the effect of these confounders, individual-level cognitive social trust was found to be significantly associated with better self-rated physical health.
There are some limitations to the present study. First, the bonding/bridging instrument did not specify by which factor group members were diverse: gender, age, or occupation (i.e., participants were just asked whichever, in sum). Although diversity in each of these dimensions could theoretically be beneficial to health, diversity in occupational background would be less relevant among the elderly compared with working populations. Rather, considering the finding of a randomized controlled trial of intergenerational interaction [
40], diversity in age groups may be the first key component to building social capital among the elderly. Further studies are warranted on the precise nature of group member diversity, preferably for each organization. Furthermore, it may be also beneficial to employ more robust assessment of group involvement, by specifying the frequency of participation for each organization. Second, effects of social capital obviously depend on cultural setting, and we need to consider urban/rural differences. Our study was conducted in rural area, then it is necessary for us to interpret the present results in a careful manner when extrapolating to other setting (e.g., urban areas). Third, there is a possibility of selection bias, since the likelihood of responding to the questionnaire may be influenced by the recipient’s level of social participation as well as the recipient’s health conditions. However, even under this constraint, the ORs are not biased when group involvement and self-rated health are non-interacting (i.e., the effect of group involvement on responding to the questionnaire is independent of the effect of self-rated health on responding to the questionnaire [
41]). Fourth, owing to the cross-sectional design of our study, a possibility of reverse causation cannot be ruled out. It may be that an elderly person can participate in more groups if his/her health condition is good. Finally, both exposure and outcome were surveyed by the same, self-evaluated questionnaire, which may have resulted in bias away from the null.
Acknowledgments
The authors thank Atsushi Ninomiya, Kenzo Fujita, Masanori Honda, Masayuki Noguchi, Tomoko Matsushima, Miyuki Okamoto, and the public health nurses in charge of the survey in the investigated municipalities and public health centers. The authors are also grateful to Ichiro Kawachi for his advice in conducting this study. This work was supported by the Fund for Urgent Improvement of Local Suicide Prevention Measures from the Cabinet Office and the Ministry of Health, Labour and Welfare, Japanese Government. The funder had no role in study design, data collection and analysis, preparation of the manuscript, or decision to submit the manuscript for publication.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
YK performed the statistical analysis and drafted the manuscript. ES planned the study, supervised the data analysis, and revised the manuscript for important intellectual content. TI and HD helped plan the study and contributed to revising the paper. ST supervised the study and contributed to revising the paper. All authors read and approved the final manuscript.