Social capital and core network ties: A validation study of individual-level social capital measures and their association with extra- and intra-neighborhood ties, and self-rated health

Health & Place 17 (2011) 536–544

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Health & Place journal homepage: www.elsevier.com/locate/healthplace

Social capital and core network ties: A validation study of individual-level social capital measures and their association with extra- and intra-neighborhood ties, and self-rated health Spencer Moore a,b,n, Ulf Bockenholt c, Mark Daniel b,d,e,f, Katherine Frohlich d, Yan Kestens b,d, Lucie Richard g,h a

Queen’s University, School of Kinesiology and Health Studies, 69 Union St. PEC Rm. 215, Queen’s University, Kingston, ON, Canada K7L 3N6 Centre de recherche du Centre Hospitalier de l’Universite´ de Montre´al, Canada Desautels Faculty of Management, McGill University, Canada d De´partement de me´decine sociale et pre´ventive, Universite´ de Montre´al, Canada e School of Health Sciences, University of South Australia, Australia f Department of Medicine, St Vincent’s Hospital, The University of Melbourne, Australia g Faculte´ des sciences infirmie res, Universite´ de Montre´al, Canada h Institut de recherche en sante´ publique, Universite´ de Montre´al, Canada b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 10 June 2010 Received in revised form 8 December 2010 Accepted 9 December 2010 Available online 16 December 2010

Research on social capital and health has assumed that measures of trust, participation, and perceived cohesion capture aspects of people’s neighborhood social connections. This study uses data on the personal networks of 2707 Montreal adults in 300 different neighborhoods to examine the association of socio-demographic and social capital variables with the likelihood of having core ties, core neighborhood ties, and high self-rated health (SRH). Persons with higher household income were more likely to have core ties, but less likely to have core neighborhood ties. Persons with greater diversity in extraneighborhood network capital were more likely to have core ties, and persons with greater diversity in intra-neighborhood network capital were more likely to have core neighborhood ties. Generalized trust, perceived neighborhood cohesion, and extra-neighborhood network diversity were shown associated with high SRH. Conventional measures of social capital may not capture network mechanisms. Findings suggest a critical appraisal of the mechanisms linking social capital and health, and the further delineation of network and psychosocial mechanisms in understanding these links. & 2010 Elsevier Ltd. All rights reserved.

Keywords: Social capital Health Neighborhood environments Social networks Trust

1. Introduction Over the past decade, research on social capital and health has become an important sub-field within social epidemiological research. Yet, precise identification of the mechanisms by which social capital affects health has been limited. This limitation reflects in part a lack of knowledge of the validity of current measures of social capital. In health and place research, social capital is often defined as ‘‘network-accessed resources’’ but seen to represent a place-based feature (Moore et al., 2005). Individual proxy indicators of social capital, such as trust and participation, are often aggregated to the census-tract level to represent neighborhood social capital. Associations found between high neighborhood social capital and better health might be attributed in this regard to variations in the degree to which neighborhoods possess n Corresponding author at: Queen’s University, School of Kinesiology and Health Studies, 28 Division St., Queen’s University, Kingston, ON, Canada K7L 3N6. Tel.: + 1 613 533 6000x78667; fax: + 1 613 533 2009. E-mail address: [email protected] (S. Moore).

1353-8292/$ - see front matter & 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.healthplace.2010.12.010

cohesive and resourceful social networks. Despite the reliance of health and place research on proxy indicators of social capital, no research as far as we are aware has examined whether these proxy indicators reflect aspects of people’s social connections within neighborhood settings using formal network data. Greater knowledge of the validity of current socio-relational indicators would assist in the identification of the mechanisms linking neighborhood social environments to health. Using population-based data on the personal social networks of 2707 randomly-selected adults residing in 300 different Montreal neighborhoods, the following study examines the association of (1) individual socio-economic and -demographic factors, such as socio-economic status (SES) and age, with characteristics of participants’ social networks, (2) individual-level social capital variables with characteristics of participants’ networks, and (3) social capital variables with a person’s subjective health status. For this study, two social network characteristics are being treated as main outcomes. The first is whether individuals have people in their social networks with whom they can discuss important matters; the second is whether those who

S. Moore et al. / Health & Place 17 (2011) 536–544

do have those persons in their neighborhoods. Discussant networks have been shown to overlap with people’s core social relationships (McPherson et al., 2006). Core relationships tend to represent stronger ties, and be small and comparatively dense and homogenous compared to other relationships (Marsden, 1987). Core networks have importance for health since they often exert normative pressures on behavior, and individuals can often use such networks for advice and emotional and instrumental support (McPherson et al., 2006). Having or not having core network members should not however be conflated with having or not having social support. Having core networks represents the possession of a key social structural feature from which support or other resources might emerge.

1.1. Individual social capital and self-rated health (SRH) Social capital is often defined as the resources that individuals and potentially groups have access to through their networks (Bourdieu, 1986). The definitional simplicity belies the theoretical and measurement debates surrounding social capital’s use in health research. Theoretically, debate has often centered on the relative merits of more communitarian versus network and political economy approaches (Moore et al., 2005). Communitarian approaches tend to frame social capital as the property of places, e.g., neighborhoods, and thus a public good equally available to all; network approaches tend to frame social capital as an emergent, inter-personal dimension of social relationships. This theoretical debate has often converged with measurement issues. Within the communitarian approach, studies have tended to aggregate proxy indicators of social capital, such as trust, to the neighborhood level to assess the contextual effect of social capital on health (Subramanian et al., 2003). More recently, individual-level studies of social capital have used indicators such as trust, participation, and perceived cohesion to examine the association of individual social capital with a range of health status or health behavior outcomes (Kim et al., 2008; Poortinga, 2006a). In this study, the broad concept of social capital, which encompasses socio-relational indicators such as trust, participation, perceived cohesion, and network social capital, will be eschewed in favor of terminology that reflects the actual measures used. Trust is differentiated into two types: (1) generalized trust and (2) particularized or localized trust (Abbott and Freeth, 2008). Generalized trust involves individual perceptions of how trustworthy overall the social environment may be, whereas particularized trust, in contrast, asks about trust in specific others (e.g., neighbors) (Abbott and Freeth, 2008). Extensive research has shown associations at the individual level between generalized trust and SRH (Helliwell and Putnam, 2004; Kim and Kawachi, 2006; Kim et al., 2008; Pollack and Knesebeck, 2004; Poortinga, 2006a, 2006b; Subramanian et al., 2002; Veenstra et al., 2000, 2005). Less research has been devoted to particularized trust, such as ‘‘trust in neighbors,’’ although this construct has been shown associated with depression (Fujiwara and Kawachi, 2008). Individual social participation has also been shown associated with SRH (Helliwell and Putnam, 2004; Kim et al., 2006; Poortinga, ¨ 2006a; Lindstrom et al., 2004; Hyppa¨ and Maki, 2003). Recent research has suggested that the form of participation might vary according to neighborhood characteristics (Swaroop and Morenoff, 2006). Yet, little research has examined whether it matters for individual health if participation takes place inside or outside a person’s neighborhood. If participation facilitates the development of social networks, individuals who participate closer to home may likely have more neighborhood ties. Indices of perceived social cohesion and informal social control, i.e., collective efficacy, have also been used to measure the neighborhood social environment. Although these indicators tend to be modeled as a neighborhoodlevel construct, research has shown individual perceptions of the neighborhood environment and informal social control associated

537

with SRH (Weden et al., 2008; Moore et al., 2010). Given the amount of research on trust, participation and SRH, SRH provides as a well-documented outcome for assessing the validity of current socio-relational and -capital indicators. Network approaches using formal network measures of social capital have only recently surfaced within the health literature and have focused primarily on individual-level relationships (Moore et al., 2010). These studies have demonstrated an association between network-accessed resources and obesity and overweight status and mastery (Moore et al., 2009a, 2009b). Although proxy indicators of social capital are assumed to reflect in some sense these individual network connections, little is known in this regard. Even less known is the degree to which the different dimensions of network social capital, or whether extra- or intraneighborhood network social capital is more strongly associated with SRH. 1.2. On the theoretical importance of neighborhood attachment Recent research on neighborhood social environments and health has identified neighborhood attachment as central to understanding the relationship between neighborhood social environments and health (Carpiano, 2006, 2007). Neighborhood attachment refers to an individual’s degree of integration into neighborhood networks; those better connected are more likely to access local resources, and experience either the positive or negative consequences of the neighborhood social environment (Carpiano, 2006). For example, in terms of the potential negative consequences, Caughy et al. (2003) showed in impoverished Baltimore neighborhoods that children whose parents had few neighborhood social connections had lower levels of behavioral problems than the children of parents who had more social connections. Previous studies using the concept of neighborhood attachment have applied global measures. Global measures ask respondents for overall assessments of their sets of friends, neighbors, or other relationships, e.g., how close one feels to the friendliest neighbor one knows. Global measures are relatively efficient, require less interview time, and have often shown strong associations with health (Marsden, 2006); yet, such questions place a high cognitive demand on respondents in defining terms such as ‘‘close’’ and ‘‘friends’’ (Marsden, 2006). In addition, it is more difficult to distinguish the different mechanisms linking social networks with health using global compared to network measures (Marsden, 2006). Another advantage of formal network methods in this case is that it allows identification of a person’s core network members first and only after is their place of residence discovered. For this study, having core ties in the neighborhood may be seen to reflect a person’s degree of integration into neighborhood networks. 1.3. Research questions To decipher more clearly the association of trust, participation, and the different dimensions of network social capital with having (1) core network ties, (2) core neighborhood network ties, and (3) high SRH, this analysis will distinguish the socio-relational and capital indicators according to whether they occur inside or outside the neighborhood setting. The study anticipates that trust in neighbors, neighborhood social participation, and neighborhood social capital will be more strongly associated with having core neighborhood social ties than if those socio-relational factors occur farther away from one’s neighborhood. Three sets of questions guide this study: Q1. To what degree are socio-economic and -demographic, and social relational and capital factors (i.e., trust, participation,

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perceived cohesion, and the different dimensions of network capital) associated with the likelihood of having core ties? Q2. To what degree are socio-economic and -demographic, and social relational and capital factors associated with the likelihood of having core neighborhood ties? Q3. To what degree are social relational and capital factors, particularly the different dimensions of network capital, associated with the likelihood of reporting a higher level of SRH? As a test of the validity of current social capital indicators in measuring social connections, knowledge of the individual correlates of having core neighborhood ties is important for several reasons. First, if having core neighborhood ties is associated with socio-economic or -demographic factors in meaningful ways, it may suggest the potential unequal distribution of an effect modifier, i.e., neighborhood attachment, across neighborhoods. For example, if persons with low SES tend to have core neighborhood ties and low SES persons tend to cluster within particular neighborhoods, neighborhood attachment may play a greater role for low SES persons in certain neighborhoods. Second, if having core neighborhood ties is associated with proxy indicators of social capital, such as trust and participation, it would help validate current measures of social capital as measuring neighborhood social connectivity. Finally, examining the degree to which trust, participation, and the different dimensions of social capital is associated with self-reported health will assist in the identification of the specific mechanisms by which social capital or social relations influence health. The parsing of social capital into inside and outside neighborhood ties enables greater attention to the relative importance of neighborhood social connections on self-rated health.

2. Methods 2.1. Sample Data came from the 2008 Montreal Neighborhood Networks and Healthy Aging Study (MoNNETs-HA). The MoNNETs-HA study used a two-stage stratified cluster sampling design. In stage one, Montreal Metropolitan Area (MMA) census tracts (N ¼862) were stratified using 2001 Canada Census data into tertiles of high, medium, and low household income. One hundred census tracts were selected from each tertile (nj ¼300). In stage two, potential respondents within each tract were stratified into three age groups: 25–44 years old, 45–64, and 65 or older. Three respondents were randomly selected within each age stratum and CT for a total of 9 respondents per tract, except for seven tracts in which four participants were selected (ni ¼2707). To be selected, individuals had to (1) be non-institutionalized, (2) have resided at their current address for at least one year, and (3) able to complete the questionnaire in French or English. Random digit dialing of listed telephone numbers was used to select households and a computer assisted telephone interviewing system guided questionnaire administration. Participants completed the telephone interview between mid-June and early August 2008. 2.2. Measures 2.2.1. Outcomes 2.2.1.1. Social ties. To measure core ties, we used the ‘‘discuss important matters’’ name generator. The name generator question asked participants to name up to three individuals, i.e., alters, with whom they may have discussed important matters in the last six months. This question elicits an individual’s close set of confidants (McPherson et al., 2006). To reduce potential measurement error

due to variation among respondents in their reporting of personal networks (Brewer, 2000; Feld and Carter, 2002), participants were restricted to identifying no more than three alters. If participants reported not having discussed important matters with anyone in the last six months, interviewers repeated the question to participants, and asked whether they had not spoken with anyone or preferred not to answer the question. Participants who said that they preferred not to answer this question were dropped from these analyses (n¼72). Among those who answered the name generator question and had no missing responses to covariates (n ¼2556), the first set of analyses examined the likelihood of a participant having ‘‘one or more ties’’ versus ‘‘no ties.’’

2.2.1.2. Neighborhood social ties. Participants who nominated one or more alters in the preceding name generator were administered the name interpreter. The name interpreter consists of a series of questions about the nominated alters, e.g., their age. One question asked participants if their alters resided in their (i) household, (ii) neighborhood, (iii) in the MMA, or (iv) outside the MMA. No specific definition of neighborhood was provided to respondents, thereby allowing participants to conceptualize their own neighborhood boundaries. Among those participants who had at least one core tie and no missing responses from model covariates, a second set of analyses examined the likelihood of ‘‘having one or more neighborhood ties’’ versus ‘‘no neighborhood ties’’ (n ¼2268). Household ties were distinguished from neighborhood ties.

2.2.1.3. Self-reported health. To assess self-reported health status, participants were asked if they, generally speaking, would say that their current health was excellent, very good, good, fair, or poor. To compare current findings with other research on social capital and self-reported health, we dichotomized participant responses into high (excellent and very good) and low (good, fair, and poor) categories, and modeled the likelihood of high self-reported health. To facilitate comparison across the analyses, we used the same sample in the SRH analysis as that used in the ‘‘core neighborhood ties’’ analysis.

2.2.2. Main exposure variables The following socio-relational variables were used: (1) outside neighborhood social capital, (2) neighborhood social capital, (3) generalized trust, (4) particularized trust, i.e., trust in neighbors, (5) inside neighborhood social participation, (6) outside neighborhood social participation, and (7) perceived neighborhood cohesion. Outside- and inside-neighborhood network capital were assessed using a position generator. Position generators have been recently applied in public health research (Moore et al., 2009a, 2009b). Position generators assess social capital by asking participants to indicate whether they know someone (on a first-name basis) who holds certain occupations in society, e.g., teacher or taxi driver. By linking these occupations to a context-relevant prestige score, the diversity and potential value of a person’s social connections can be assessed (Lin, 2001). Ten occupations were selected from a listing of 90 occupations that had been ranked according to gender-neutral job prestige scores within Canada (Goyder et al., 2003). The list was divided into octiles ranging from high to low prestige jobs. From each octile, one occupation was randomly selected; two additional occupations (i.e., physician and musician/artist) were selected. These ten occupations were randomly listed in the position generator. If a participant indicated that they knew someone in any of the ten occupations, they were asked if that person resided in their household, neighborhood, outside their neighborhood but in the MMA, or outside the MMA (relationship location). If a respondent reported that they knew more than one person in an

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occupation, they were asked to consider the person who was closest emotionally to them. Lin (2001) argues that the position generator captures three separate dimensions of social capital: (1) upper reachability, (2) diversity, and (3) range. Upper reachability, which is the highest prestige occupation a person knows, represents the uppermost resource a person can reach through their social ties; diversity, which refers to the number of different occupations accessed, reflects, in this case, a person’s network size; and, range, which is the difference between the highest and lowest prestige job accessed, reflects the different types of resources a person might access (Lin, 2001). To aid in the identification of how these different dimensions are associated with the main outcomes, we elected to assess their associations separately rather than bundling them into a single measure. Given that the dimensions were at different scales, the three were standardized to facilitate their comparison among each other. The odds ratio of each standardized network social capital dimension is the amount the odds of the outcome occurring increases (decreases) when that dimension increases (decreases) by one standard deviation. Correlations among the dimensions were not found to pose multicollinearity problems. Ancillary analyses showed that the pattern of results for models with a variable representing a single socio-relational or network-capital dimension, or different subsets of variables were similar to the complete model. Generalized trust in others was assessed using the U.S. General Social Survey question ‘‘Generally speaking, would you say that most people can be trusted or that you can’t be too careful in dealing with people?’’ Participants chose from the following responses: (1) most people can be trusted, (2) can’t be too careful, (3) depends, (4) most people cannot be trusted, and (5) don’t know. ‘‘Don’t know’’ responses were treated as missing (n¼20). Responses were reverse coded so that higher numbers indicated greater trust. To maintain the full content of the question, this variable was modeled in ordinal form. Trust in neighbors was assessed with the single item – ‘‘people in your neighborhood can be trusted’’ – with five-point Likert response scale ranging from strongly agree to strongly disagree. Responses were reverse coded so that higher numbers indicated greater trust. The variable was modeled in ordinal form with ‘‘don’t know’’ responses forming the neutral category (n¼124). Sensitivity analyses were conducted to ensure that this decision did not impact findings. Inside neighborhood participation was assessed by asking participants to indicate whether or not they had been active in the last five years as a volunteer or officer in a neighborhood group or association. Outside neighborhood participation was assessed by asking participants if they had been active in the last five years as a volunteer or officer in a group or association outside the neighborhood. Both were modeled in dichotomous form. Perceived neighborhood social cohesion was assessed with four items: (1) you have trouble with your neighbors, (2) people in your neighborhood are willing to help each other, (3) most people in your neighborhood know you, and (4) your neighborhood is clean. Responses were on a five-point Likert scale from strongly agree to strongly disagree; ‘‘don’t know’’ responses formed the neutral category. Responses were reverse coded with the exception of item one, and centered on the neutral category so that higher numbers indicated a greater perceived cohesion. The cohesion scale had a Cronbach’s alpha of 0.41. Sensitivity analyses, in which the perceived environment items were entered separately or a perceived cohesion factor score was used, held similar results however as those reported using the scale.

2.2.3. Study covariates Covariates included gender, income category, educational attainment, employment status, age, marital status, primary household language, birthplace, and length of residence at current address.

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Participants self-identified gender. Participants selected their income from five categories: (a) less than $28,000, (b) $28,000–$49,000, (c) $50,000–$74,000, (d) $75,000–$100,000, and (e) more than $100,000. Missing responses to the income question were imputed using ordinal regression for 20% of the respondents using (a) questionnaire data on socio-demographic variables, including education, age, and employment status, and (b) Canada census data on median household income for the census tract in which respondents resided. Study participants were asked to select their highest level of educational attainment from seven categories: (a) no high school degree or certificate, (b) high school diploma or equivalent, (c) trade certificate or diploma, (d) college certificate or diploma below Bachelor’s degree, (e) Bachelor’s degree, (f) Master’s degree, or (g) earned doctorate, medical, or professional degree. For employment status, participants indicated if they were currently employed. Participants’ ages were grouped into six categories: (1) 25–34, (2) 35–44, (3) 45–54, (4) 55–64, 65–74, and (6) 75 years or more, with the oldest group used as the reference. Participants were asked to indicate their marital status as: (1) married or in a common-law relationship, (2) single, (3) separated, (4) divorced, or (5) widowed. Those who were married or in a common-law relationship were contrasted with those who were in one of the other categories. Participants were asked to indicate the primary language spoken in their homes. French-speaking households were contrasted with English or other language households. Foreignborn status was based on whether participants reported being born inside or outside of Canada. Residential duration, which was the amount of time in years and months that a person had resided at their current residence, had a skewed distribution and was modeled in continuous terms after logarithmic transformation.

2.3. Statistical analysis procedures The MoNNET-HA response rate was calculated according to American Association for Public Opinion Research standard definitions (AAPOR, 2008). The response rate was calculated as the number of completed interviews divided by the number of interviews, non-interviews, and an estimated proportion of cases of unknown eligibility. The MoNNET-HA study had a response rate of 38.7%. To assess the representativeness of the MoNNET-HA sample, chi-square analyses were used to compare on a CT-by-CT basis the observed sample counts to the expected counts based on the most recent 2006 Canada census. These comparisons were made for a range of census variables, including percentage adults 65 years and older and average length of residence. Minimum cell counts were validated by comparing the results with those from exact binomial tests. Results from these analyses were summarized across the 300 CTs and compared to the null hypothesis of no difference between the sample and the 2006 census. Results of these analyses showed that the MoNNET-HA sample over-represented (1) older adults (by sampling design), (2) individuals with an income less than 50,000 per year, (3) persons who lived in their places of residence for more than five years, (4) females, and (5) those with more than a high school degree. Multilevel logistic regression analysis was used to account for the clustered sampling design. CTs were specified as a random effect. No area-level measures were included in this study. Analyses proceeded in several stages. First, the variance between neighborhoods (i.e., census tracts) in having at least one core tie, having at least one neighborhood tie, and having high SRH was calculated, and reported as the intraclass correlation coefficient (ICC) along with a 95% plausible value interval (PVI). The ICC was (i) 0.00 in having core ties, (ii) 0.02 (95% PVI: 0.02–0.03) in having at least one core neighborhood tie, and (iii) 0.04 (95% PVI: 0.00–0.08) in high SRH. Three statistical analyses were conducted: (i) having at

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least one core network tie versus none, (ii) having at least one core neighborhood network ties versus none, and (iii) reporting high versus low SRH. For each analysis, the first model examined the association between the socio-demographic and -economic study covariates and the outcome. The second model introduced the socio-relational variables and the separate social capital dimensions into model one.

3. Results Table 1 provides information on the socio-demographic characteristics of the MoNNET-HA sample. Table 2 provides information on the network capital characteristics of MoNNET-HA participants. 3.1. Analysis one: having a core tie Out of the MoNNET-HA sample of 2707 respondents, 2635 participants answered the name generator question. From this sample, an additional 79 observations, or 3.0%, were dropped from analysis one for missing data on any of the socio-demographic variables. Among the 2556 participants, 11.1% reported having no core ties. Table 3 provides the results of analysis one: women (OR: 1.52; 95%CI: 1.14–2.04), French-speaking households (OR: 1.53; 95%CI: 1.06–2.22), persons with household income levels greater than $75,000 or educational attainment of a bachelor degree or higher, and younger age groups were more likely to have a core network tie. Respondents who had participated in associations outside the neighborhood in the last five years (OR: 1.93; 95%CI: 1.21–3.07) were more likely to have core network ties. In terms of the different dimensions of network social capital, persons with a more diverse network of contacts outside the neighborhood (OR: 1.77; 95%CI: 1.29–2.44), and those who had a greater reach of ties inside the neighborhood (OR: 1.43; 95%CI: 1.08–1.90) were more likely to have at least one core tie. 3.2. Analysis two: having a core neighborhood tie From the sample of 2332 participants who had at least one core tie, an additional 64 observations, or 2.1%, were dropped in analysis two for missing data on any of the socio-demographic or health variables. Among this sample of participants (n ¼2268), 5979 alters were named: 28.8% of the participants had at least one alter in their household, 48.5% had at least one alter in their neighborhood, 59.6% had at least one in the MMA but outside their neighborhood, and 40.0% had at least one outside the MMA. Out of this sample, 10.6% had only neighborhood ties. Table 4 provides the results of analysis two: higher income participants were less likely to have core neighborhood ties compared to the lowest income group, whereas participants with higher levels of trust in their neighbors (OR: 1.09; 95%CI: 1.00–1.18) and greater diversity in neighborhood social capital (OR: 1.98; 95%CI: 1.57–2.51) were more likely to have a core neighborhood tie. A greater range of network social capital outside the neighborhood decreased the likelihood of having a core neighborhood tie (OR: 0.79; 95%CI: 0.66–0.95). 3.3. Analysis three: having High self-reported health (SRH) Socioeconomic variables (i.e., household income, educational attainment, employment status) were associated with high SRH in expected directions. Participants born outside of Canada were less likely to report high SRH (OR: 0.62; 95%CI: 0.48–0.79). Participants with high generalized trust (OR: 1.18; 95%CI: 1.04–1.33), high neighborhood social participation (OR: 1.28; 95%CI: 1.02–1.60), and a more favorable perception of the neighborhood environment (OR: 1.24; 95%CI: 1.08–1.42) were more likely to report high SRH. In terms of the

Table 1 Characteristics of Montreal Neighborhood Networks and Healthy Aging Study (MoNNETs-HA), social ties sample, 2008. Variables

Social capital measures Generalized trust Neighborhood trust Outside neighborhood social participation Neighborhood social participation Perceived neighborhood environment Outside neighborhood network social capital Neighborhood network social capital

Core ties analysis Core neighborhood ties (n¼ 2556) analysis (n¼ 2268)

3.3 1.1 0.23

3.3 1.1 0.25

0.24

0.25

0.77

0.77

0.01

0.10

0.00

0.05

Income Less than $28,000 $28,000–$49,000 $50,000–$74,000 $75,000–$100,000 More than $100,000

20.4% 28.2% 26.8% 12.9% 11.7%

18.7% 28.8% 25.7% 11.3% 15.5%

Gender Female Male

64.7% 35.3%

64.9% 35.1%

11.6%

9.2%

29.4%

27.9%

20.7% 38.3%

21.6% 41.3%

14.8% 17.9% 20.4% 16.2% 20.7% 9.9%

16.1% 19.6% 21.0% 16.6% 19.0% 7.7%

Education Less than a high school degree High school degree or trade certificate College certificate Bachelors degree and higher Age group 25–34 years old 35–44 years old 45–54 years old 55–64 years old 65–74 years or 75 years and more Marital status Married/common-law relationship Single Divorced/separated Widowed

54.4%

56.1%

20.2% 14.8% 10.1%

20.7% 14.6% 8.6%

Household language French (bilingual) English Other

78.1% 13.7% 8.2%

78.2% 13.9% 7.9%

Foreign born status Residential duration

19.5% 13.9 years

19.5% 13.3 years

Social connection outcomes No core ties 11.1% No core neighborhood ties –

– 51.5%

network social capital, greater diversity in outside network social capital was associated with high SRH (OR: 1.20; 95%CI: 1.01–1.42).

4. Discussion To assess the validity of current socio-relational and -capital measures used in social epidemiological research, this study addressed three sets of questions related to the association of (i) individual-level social capital with having social ties, (ii) having those ties inside the neighborhood, and (iii) reporting high SRH.

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Table 2 Characteristics of position generator indicators of individual social capital and place of residence of named alter (n ¼2707), sorted by order of appearance in the questionnaire. Occupation

High school teacher Carpenter Musician/artist Taxi driver Physician Janitor Registered nurse Welder Accountant Receptionist

Prestige value

68 52 60 35 93 34 77 55 67 40

Do you know someone on a first name basis?

Place of residence

No (%)

Yes (%)

Household (%)

Neighborhood (%)

Beyond neighborhood (%)

Don’t know (%)

55.7 56.9 49.3 81.4 40.9 67.7 49.5 76.0 34.4 57.5

44.3 43.1 50.7 18.6 59.1 32.3 50.5 24.0 65.6 42.5

0.8 1.5 2.8 0.2 0.3 0.5 1.4 1.0 1.6 0.9

11.9 12.1 11.5 5.9 11.8 14.3 12.9 5.5 13.4 8.6

31.4 29.4 36.1 12.1 45.0 17.0 35.9 17.5 50.1 32.2

0.2 0.2 0.2 0.3 1.9 0.5 0.4 0.1 0.5 0.9

4.1. Having core ties Women, individuals residing in higher income households, higher educational attainment, and younger adults were more likely to have confidants with whom they could discuss important matters. These findings support research in the U.S. that has shown more educated individuals and women tend to have larger discussion networks (McPherson et al., 2006). Increasing age has also been found in other studies to be associated with having fewer core ties (McPherson et al., 2006; Oh, 2003; Stoller and Pugliesi, 1988). In Montreal, individuals who resided in French language households were more likely to have core ties than those who did not reside in such households. Among the socio-relational and capital measures, general social participation, general diversity in outside social capital, and the range of social ties within the neighborhood were associated with having core ties. As anticipated, greater diversity of social ties reported in the position generator was associated with a greater likelihood of having core ties. For Lin (2001), the position generator tends to capture people’s weaker, socially- or emotionally-distant relationships. In this regard, participants who reported a greater number of weaker social connections outside the neighborhood were also more likely to report core network ties outside the neighborhood. In terms of the different dimensions of neighborhood social capital, those persons who were able to access resources and persons with higher social status within the neighborhood (i.e., greater reach) were also more likely to have general core network ties. Longitudinal research is needed, but the finding highlights the relationship among within-neighborhood weak ties, social status, and core social connections outside the neighborhood.

4.2. Having core neighborhood ties and low income The study showed a significant socio-demographic pattern in the likelihood of having a core neighborhood tie with individuals residing in lower income households more likely to have core neighborhood ties. This finding has several implications. First, work in the U.S. has shown that more educated individuals tend to have more spatially dispersed ties (Fisher, 1982). Our study showed that low-income persons in Montreal may be more likely to have core neighborhood network ties than those of higher income. Second, Carpiano (2007) suggests that neighborhood attachment operates as an effect modifier of the relationship between the neighborhood social environment and health. Given that income is important in individual self-selection into neighborhoods, study findings would suggest that neighborhood attachment is unequally distributed across neighborhood groupings. Neighborhoods may not only differ on the distribution of an important

risk condition (i.e., income) for disease, but neighborhoods may also differ in the prevalence of a key effect modifier in the relationship between neighborhood social environments and health. Further research is necessary to understand the importance of neighborhood attachment as an effect modifier in different neighborhood settings. 4.3. Social capital and core neighborhood ties Previous research on social capital and health has suggested that proxy indicators of social capital, such as trust, participation, and perceived cohesion, reflect aspects of people’s neighborhood social connections. Our study suggests that among current proxy indicators of social capital, only trust in neighbors is associated with having core neighborhood ties. Unlike questions on generalized trust, questions regarding a person’s trust in neighbors capture assessments based on knowledge of particular individual personalities and past behavior (Abbott and Freeth, 2008). Among the different dimensions of network social capital within the neighborhood, greater diversity in weak, neighborhood social connections was found associated with having core neighborhood network ties. This finding paralleled that on overall network diversity and core network ties, indicating the degree to which the position generator is able not only to capture a person’s weak ties but also their core ties. In addition, the study showed that persons with a greater range in the types of resources that they could generally access outside the neighborhood were less likely to have core neighborhood ties. 4.4. Social-relational and -capital measures and SRH Our study supports previous research in showing an association among several social-relational indicators (generalized trust, neighborhood social participation, perceived neighborhood environment) and SRH. In addition, however, our study found that diversity in extra-neighborhood social capital was also associated with high SRH. Among these variables associated with SRH, only diversity in extra-neighborhood social capital was associated with having general core network ties; none were associated with having core neighborhood network connections. These findings have several implications when it comes to our work on social capital and SRH. First, our study questions whether generalized trust at the individual level is a valid, direct proxy of actual individual social ties outside or inside the neighborhood. Although researchers tend to develop neighborhood-level measures of social capital through the aggregation of individual responses to the generalized trust question, such measures may not be capturing the degree to which people do or do not have social connections, particularly within the neighborhood. It may be that the

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Table 3 Adjusted Odds ratios and 95% confidence intervals of ‘‘Having one or more core social ties versus no core social ties’’ (ni ¼2556; nct ¼ 300), and ‘‘Having one or more core neighborhood ties versus no core neighborhood ties’’ (ni ¼ 2268; nct ¼ 300), Montreal Neighborhood Networks and Healthy Aging Study (MoNNET-HA), 2008. Variable

Analysis one: having core network ties (n¼ 2556)

Analysis two: having core neighborhood ties (n¼ 2268)

Model 1

Model 2

Model 1

Model 2

Gender Female Male (referent)

1.46 (1.10–1.93)** 1.00

1.52 (1.14–2.04)** 1.00

1.04 (0.87–1.25) 1.00

1.03 (0.85–1.24) 1.00

Household income per year Less than $28,000 (referent) $28,000 – $49,000 $50,000–$74,000 $75,000–$100,000 More than $100,000

1.00 1.58 1.59 3.31 8.36

1.00 1.28 1.15 2.21 5.30

1.00 1.03 0.78 0.71 0.67

1.00 0.95 0.70 0.65 0.54

Employment status Unemployed (referent) Employed

1.00 1.14 (0.77–1.68)

1.00 1.05 (0.70–1.57)

1.00 0.92 (0.73–1.16)

1.00 0.91 (0.72–1.17)

Education Less than a high school degree (referent) High school degree or trade certificate College certificate Bachelors degree and higher

1.00 1.44 (1.02–2.04)* 2.25 (1.42–3.57)** 3.51 (2.24–5.50)***

1.00 1.16 (0.80–1.67) 1.53 (0.94–2.49) 1.91 (1.18–3.10)**

1.00 1.29 (0.93–1.80) 1.16 (0.81–1.66) 1.15 (0.81–1.62)

1.00 1.14 (0.81–1.62) 0.98 (0.67–1.43) 0.97 (0.67–1.40)

Age category 25–34 years old 35–44 years old 45–54 years old 55–64 years old 65–74 years old 75 years or more

7.74 (3.75–16.00)*** 8.20(4.16–16.16)*** 3.00 (1.79–5.01)*** 3.12 (1.96–4.97)*** 1.90 (1.32–2.74)** 1.00

8.32 (3.94–17.55)*** 8.99(4.44–18.23)*** 2.89 (1.68–4.95)*** 3.05 (1.88–4.97)*** 1.88 (1.28–2.75)*** 1.00

1.34 1.31 1.23 0.97 1.33 1.00

1.42 1.34 1.36 1.06 1.38 1.00

Marital status Married/common law union

0.86 (0.63–1.17)

0.90 (0.65–1.24)

1.09 (0.89–1.32)

1.05 (0.85–1.29)

(1.12–2.24)** (1.04–2.43)* (1.64–6.70)** (2.86–24.47)***

(0.89–1.85) (0.74–1.80) (1.07–4.58)* (1.78–15.82)**

(0.78–1.35) (0.58–1.05) (0.49–1.03) (0.45–0.98)*

(0.86–2.11) (0.85–2.01) (0.82–1.85) (0.66–1.44) (0.92–1.92)

(0.71–1.27) (0.51–0.96)* (0.44–0.96)* (0.36–0.82)**

(0.89–2.28) (0.85–2.11) (0.88–2.08) (0.70–1.60) (0.94–2.03)

Household language French language (English/other language referent)

1.36 (0.96–1.94)

1.53 (1.06–2.22)*

1.03 (0.82–1.28)

1.12 (0.88–1.42)

1.00

1.00

1.00

1.00

Birthplace Foreign born Canada born (referent) Residential length (log10)

0.68 (0.47–0.98)* 1.00 1.15 (0.82–1.60)

0.72 (0.49–1.06) 1.00 1.03 (0.72–1.47)

0.91 (0.72–1.15) 1.00 1.16 (0.91–1.47)

0.95 (0.74–1.22) 1.00 0.94 (0.73–1.21)

High generalized trust Low generalized trust Trust in neighbors Outside neighborhood social participation Neighborhood social participation Perceived neighborhood environment

– – – – – –

1.12 (0.94–1.34) 1.00 1.00 (0.89–1.12) 1.93 (1.21–3.07)** 1.09 (0.74–1.60) 0.98 (0.79–1.23)

– – – – – –

1.06 1.00 1.09 1.08 1.12 1.09

Social capital Outside neighborhood reach Outside neighborhood range Outside neighborhood diversity Neighborhood reach Neighborhood range Neighborhood diversity Likelihood-ratio test (G2)

– – – – – – (From null) 294.67***

1.04 (0.89–1.20) 1.04 (0.81–1.33) 1.77 (1.29–2.44)*** 1.43 (1.08–1.90) * 0.78 (0.59–1.03) 0.93 (0.63–1.39) 110.29***

– – – – – – (From null) 27.07

1.12 (0.96–1.30) 0.79 (0.66–0.95)* 1.06 (0.89–1.26) 1.13 (0.97–1.32) 0.89 (0.76–1.04) 1.98 (1.57–2.51)*** 229.51***

(0.93–1.20) (1.00–1.18)* (0.86–1.35) (0.90–1.40) (0.95–1.25)

* po 0.05, ** p o 0.01, *** po 0.001.

association often found (as in this study as well) between generalized trust and SRH operates through psychosocial (Abbott and Freeth, 2008), rather than network mechanisms. Second, although overall participation was associated with having core ties in general and has been shown associated with SRH in other studies, it was not shown to be associated with SRH in this study. Instead, neighborhood social participation was associated with SRH. Future research might use a more sensitive typology of social participation that distinguishes between different forms of participation and their location to assess more accurately the importance of participation for SRH. Third, although the study found the perceived neighborhood environment associated with SRH, the study found

no association between the perceived neighborhood environment scale or its separate items and the likelihood of having core neighborhood ties. The scale had a low reliability and caution should be taken in interpreting this finding. Yet, as suggested about generalized trust, people’s perceptions of the neighborhood environment may relate less to network mechanisms of resource accessibility and more to psychosocial mechanisms involving people’s sense of social integration and control. Finally, the study supports the validity of the position generator in measuring extraand intra-neighborhood social ties, and shows that individuals with greater diversity in extra-neighborhood social capital are more likely to report high SRH. More broadly, the study thus

S. Moore et al. / Health & Place 17 (2011) 536–544

Table 4 Adjusted Odds ratios of self-reporting ‘‘Excellent’’ or ‘‘Very Good’’ health versus selfreporting ‘‘Good,’’ ‘‘Fair,’’ or ‘‘Poor’’ health (MoNNETs-HA) (ni ¼2268; nct ¼ 300) 2008.

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capital indicators. Second, the interview asked participants to define for themselves where their neighborhood boundaries lied. This meant that within neighborhoods, participants may hold different conceptions of those boundaries. Theoretically, this decision is justifiable for this study since the key factor is whether the participants themselves perceived their social ties as spatiallyproximate. Finally, the study is cross-sectional in design, meaning for example that there may be recursive effects of trust in neighbors on having core neighborhood ties. Trust is more upstream in this study since research has posited trust as a proxy for social capital. Future longitudinal research would help delimit potential recursive relationships among trust, participation, social capital, and neighborhood connections.

Variable

Model 1

Model 2

Gender Female Male (referent)

1.13 (0.94–1.37) 1.00

1.12 (0.92–1.35) 1.00

Household income per year Less than $28,000 $28,000–$49,000 $50,000–$ 74,000 $75,000–$100,000 More than $100,000

1.00 1.36 1.86 2.11 3.33

1.00 1.28 (0.97–1.69) 1.69 (1.24–2.30)*** 1.90 (1.30–2.78)*** 2.87 (1.89–4.36)***

Employment status Unemployed Employed

1.00 1.30 (1.02–1.64)*

1.00 1.30 (1.03–1.66)*

1.00

1.00

1.53 (1.09–2.15)* 2.21 (1.53–3.19)*** 2.27 (1.59–3.23)***

1.53 (1.09–2.17)* 2.11 (1.46–3.06)*** 2.11 (1.46–3.04)***

Age category 25–34 years old 35–44 years old 45–54 years old 55–64 years old 65–74 years or 75 years and more

1.02 0.96 0.94 0.93 0.99 1.00

1.07 0.94 0.98 0.92 0.97 1.00

Marital status Married/common law union

1.04 (0.85–1.27)

1.02 (0.83–1.25)

The breadth of research that has shown significant associations between generalized trust, participation, and health highlights the importance of those constructs for social epidemiological research. Yet, the specific mechanisms by which social capital influences health have remained unclear. This study suggests that conventional measures of social capital, such as generalized trust, participation, or the perceived neighborhood environment, may not successfully capture network mechanisms related to having core connections within one’s neighborhood social environment. For health and place research, more broadly, our findings call for a more critical appraisal of the mechanisms linking social capital and health, and the further delineation of network and psychosocial mechanisms in understanding these links.

1.04 (0.83–1.31) 1.00

1.07 (0.85–1.35) 1.00

Acknowledgements

0.59 (0.47–0.76)*** 1.00 1.00 (0.78–1.28)

0.62 (0.48–0.79)*** 1.00 0.92 (0.71–1.18)

– – –

1.18 (1.04–1.33)** 1.00 1.05 (0.97–1.13) 0.91 (0.73–1.14)

– –

1.28 (1.02–1.60)* 1.24 (1.08–1.42)**

– – – – – – 182.67***

1.02 (0.88–1.18) 0.85 (0.71–1.01) 1.20 (1.01–1.42)* 1.04 (0.90–1.20) 1.14 (0.98–1.34) 0.87 (0.71–1.06) 41.87***

Education Less than a high school degree High school degree or trade certificate College certificate Bachelors degree and higher

Household language French language (English/other language referent) Birthplace Foreign born Canada born (referent) Residential length (log10) High generalized trust Low generalized trust Trust in neighbors Outside neighborhood social participation Neighborhood social participation Perceived neighborhood environment Social capital Outside neighborhood reach Outside neighborhood range Outside neighborhood diversity Neighborhood reach Neighborhood range Neighborhood diversity Likelihood-ratio test (G2)

(1.03–1.79)* (1.37–2.52)*** (1.45–3.08)*** (2.21–5.02)***

(0.65–1.62) (0.62–1.48) (0.62–1.42) (0.63–1.39) (0.68–1.43)

(0.68–1.70) (0.60–1.46) (0.64–1.49) (0.61–1.37) (0.66–1.40)

* po 0.05, ** p o 0.01, *** po 0.001.

suggests for the general adult population that high SRH is associated more with people’s connections and access to resources outside the neighborhood than inside. 4.5. Limitations Findings should be interpreted in light of the following considerations. First, this study has focused as an outcome on core network ties in general and the neighborhood in particular. This is only one aspect of a person’s social networks and other aspects, such as weak, acquaintance relationships, may be shown in the future to have different associations with socioeconomic and social

5. Conclusion

S.M. holds a New Investigator Award from the Canadian Institutes of Health Research (CIHR)—Institute of Aging. U.B. holds a Canada Research Chair in e-Marketing. M.D. holds a Research Chair for Social Epidemiology from the University of South Australia, and held a Canada Research Chair for Biopsychosocial Pathways in Population Health from the CIHR during project design and funding of the results reported here. K.F. holds a CIHR New Investigator Award and is a Humboldt Experienced Researcher. L.G. holds a CIHR/Centre de Recherche en Prevention de l’Obe´site´ (CRPO) Applied Public Health Chair in Neighborhoods, Lifestyle, and Healthy Body Weight. L.R. is a FRSQ National Scholar (#16207). Data collection and analysis was supported by a team grant from the Canadian Institutes of Health Research (Grant no. MOP-84584). References Abbott, S., Freeth, D., 2008. Social capital and health: starting to make sense of the role of generalized trust and reciprocity. Journal of Health Psychology 13, 874–883. Brewer, D.D., 2000. Forgetting in the recall-based elicitation of personal and social networks. Social Networks 22, 29–43. Bourdieu, P., 1986. In: Richardson, J.G.., Westport, CT (Eds.), ‘‘Forms of Capital’’ in Handbook of Theory for the Sociology of Education. Greenwood Press, pp. 241–258. Carpiano, R.M., 2006. Toward a neighborhood resource-based theory of social capital for health: can Bourdieu and sociology help? Social Science & Medicine 62, 165–175. Carpiano, R.M., 2007. Neighborhood social capital and adult health: an empirical test of a Bourdieu-based model. Health & Place 13 (3), 639–655. Caughy, M.O., O’Campo, P.J., Muntaner, C., 2003. When being alone might be better: neighborhood poverty, social capital, and child mental health. Social Science & Medicine 57 (2), 227–237. Feld, S., Carter, W., 2002. Detecting measurement bias in respondent reports of personal networks. Social Networks 24, 365–383. Fisher, C., 1982. To Dwell Among Friends. University of Chicago Press, Chicago. Fujiwara, T., Kawachi, I., 2008. A prospective study of individual-level social capital and major depression in the United States. Journal of Epidemiology and Community Health 62, 627–633.

544

S. Moore et al. / Health & Place 17 (2011) 536–544

Goyder, J., Guppy, N., Thompson, M., 2003. The allocation of male and female occupational prestige in an Ontario urban area: a quarter-century replication. The Canadian Review of Sociology and Anthropology 40 (4), 417–440. Helliwell, J.F., Putnam, R., 2004. The social context of well-being. Proceedings of the Royal Society B: Biological Sciences 359, 1435–1446. ¨ M.T., Maki, ¨ Hyppa, J., 2003. Social participation and health in a community rich in stock of social capital. Health Education Research 18, 770–779. Kim, D., Kawachi, I., 2006. A multilevel analysis of key forms of community- and individual-level social capital as predictors of self-rated health in the United States. Journal of Urban Health 83, 813–826. Kim, D., Subramanian, S.V., Kawachi, I., 2008. Social capital and physical health: a systematic review of the literature. In: Kawachi, I., Subramanian, S.V., Kim, D. (Eds.), Social Capital and Health. Springer Press, New York, pp. 139–190. ¨ Lindstrom, M., Moghaddassi, M., Merlo, J., 2004. Individual self-reported health, ¨ Sweden. social participation, and neighborhood: a multilevel analysis in Malmo, Preventive Medicine 39, 135–141. Lin, N., 2001. Social Capital: A Theory of Social Structure and Action. Cambridge University Press, Cambridge. Marsden, P., 1987. Core discussion networks of Americans. American Sociological Review 52, 122–131. Marsden, P., 2006. Network methods in social epidemiology. In: Oakes, M., Kaufman, J. (Eds.), Methods in Social Epidemiology. Jossey-Bass, San Francisco, pp. 267–286. McPherson, M., Smith-Lovin, L., Brashears, M., 2006. Social isolation in America: changes in core discussion networks over two decades. American Sociological Review 71, 353–375. Moore, S., Shiell, A., Hawe, P., Haines, V.A., 2005. The privileging of communitarian ideas: citation practices and the translation of social capital into public health research. American Journal of Public Health 95 (8), 1330–1337. Moore, S., Daniel, M., Paquet, C., Dube´, L., Gauvin, L., 2009a. Association of individual network social capital with abdominal adiposity, overweight, and obesity. Journal of Public Health March 31, 175–183.

Moore, S., Daniel, M., Gauvin, L, Dube´, L., 2009b. Not all social capital is good capital. Health & Place 15, 1071–1077. Moore, S., Gauvin, L., Daniel, M., Kestens, Y., Dube´, L., Bockenholt, U., Richard, L., 2010. Associations among socioeconomic status, perceived neighborhood control, perceived individual control, and self-reported health. Journal of Community Psychology 38 (6), 729–741. Oh, J-H., 2003. Assessing the social bonds of elderly neighbors: the roles of length of residence, crime victimization, and perceived disorder. Sociological Inquiry 73 (4), 490–510. Pollack, C.E., von dem Knesebeck, O., 2004. Social capital and health among the aged: comparisons between the United States and Germany. Health & Place 10, 383–391. Poortinga, W., 2006a. Social capital: an individual or collective resource for health? Social Science & Medicine 63, 292–302. Poortinga, W., 2006b. Do health behaviors mediate the association between social capital and health? Preventive Medicine 43, 488–493. Stoller, E., Pugliesi, K., 1988. Informal networks of community-based elderly. Research on Aging 10, 499–516. Subramanian, S.V., Kim, D.J., Kawachi, I., 2002. Social trust and self-rated health in US communities: a multilevel analysis. Journal of Urban Health 79, S21–S34. Subramanian, S.V., Lochner, K., Kawachi, I., 2003. Neighborhood differences in social capital: a compositional artifact or a contextual construct? Health & Place 9, 33–44. Swaroop, S., Morenoff, J., 2006. Building community: the neighborhood context of social organization. Social Forces 84 (3), 1665–1695. Veenstra, G., 2000. Social capital, SES, and health: an individual-level analysis. Social Science & Medicine 50, 619–629. Veenstra, G., Luginaah, I., Wakefield, S., Birch, S., Eyles, J., Elliott, S., 2005. Who you know, where you live: social capital, neighborhood and health. Social Science & Medicine 60, 2799–2818. Weden, M., Carpiano, R, Robert, S.A., 2008. Subjective and objective neighborhood characteristics and adult health. Social Science & Medicine 66 (6), 1256–1270.