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Social Capital and Self-rated Health Amongst Older People in Western Finland and Northern Sweden: A Multi-level Analysis

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Abstract

Background

Social capital can be conceptualised as an individual resource residing in relationships between individuals or as a collective resource produced through interactions in neighbourhoods, communities or societies. Previous studies suggest that social capital is, in general, good for health. However, there is a shortage of studies analysing the association between individual and collective social capital in relation to health amongst older people.

Purpose

The purpose of this study was to assess the relationship between municipal- and individual-level social capital and self-rated health amongst older people in Western Finland and Northern Sweden.

Method

Data were retrieved from a cross-sectional postal questionnaire survey conducted in 2010. The study included, in total, 6,838 people aged 65, 70, 75 and 80 years living in the two Bothnia regions, Västerbotten, Sweden and Pohjanmaa, Finland. The association between social capital and self-rated health was tested through multi-level logistic regression analyses with ecometric tests. Social capital was measured by two survey items: interpersonal trust and social participation.

Results

Individual-level social capital including social participation and trust was significantly associated with self-rated health. A negative association was found between municipal-level trust and health. However, almost all variation in self-rated health resided on the individual level.

Conclusions

We conclude that contextual-level social capital on a municipal level is less important for understanding the influence of social capital on health in the Bothnia region of Finland and Sweden. On the other hand, our study shows that individual-level social participation and trust have a positive and significant association with self-rated health. We suggest that other ways of defining social capital at the collective level, such as the inclusion of neighbourhood social capital, could be one direction for future research.

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Notes

  1. To check for robustness, we tried a number of alternative ecometrics specifications: (1) without accounting for “difficulty” bias and (2) a two-level model that uses the same individual-level social capital variables as are later used in the substantive models. For all specifications, we also performed sensitivity checks for the number of integration points and the adaptive quadrature methods employed in GLLAMM vis-à-vis the Stata command xtmelogit (the latter command results in Empirical Bayes modal predictions instead of the posterior means reported by GLLAMM). The municipal-level social capital variables resulting from these ecometrics analyses all correlate very highly with each other, and the results in the substantive models (Table 3) are comparable to the reported results. Thus, our results are robust to a variety of alternative specifications and estimation methods. This corresponds to earlier research (see footnotes 12 and 14 in [41]).

  2. We compared the model fits for alternative specifications of these control variables, such as a categorical predictor of a variety of income levels. Model fit was optimal using the reported specification, and model outcomes in Table 3 are substantively similar.

  3. The estimated equation mimics the ecometrics multi-level model. Y ij takes on a value of unity if a respondent i reports high health in municipality j, with Y ij  = 0 if not. Then, m ij is the probability that Y ij  = 1, and the log-odds (η ij ) of this probability is estimated as the two-level model: η ij  = γ + γ 1 X ij  + γ 2 Z j  + u j  + ε ij . In this example, we only introduce two covariates, X ij and Z j , although we, of course, introduce more covariates in each model (see Table 3). The γ is the grand-mean of self-rated health across all municipalities; X ij is an individual-level predictor (which has a different value for each individual i in municipality j), and γ 1 reflects the parameter estimate of that predictor; similarly, Z j is a municipality-level predictor and γ 2 refers to the parameter estimate of this predictor; u j refers to municipality-level random effect and ε ij refers to residual error. In several explorative models, we tested for random coefficients and cross-level interactions. Analytically, random coefficients are modelled by introducing a new interaction term between a specific covariate and group-level residuals of the slope of the individual explanatory variables. Cross-level interactions add the interaction term between individual-level and municipality-level covariates.

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Acknowledgments

The GERDA Project was supported by the Interreg-programme Botnia Atlantica, the Regional Council of Ostrobothnia, Umeå Municipality, and the GERDA Project partners, including Åbo Akademi University, Novia University of Applied Sciences, and Umeå University. The work by FN was financially supported by the Academy of Finland (project no. 250054) as part of the FLARE-2 programme.

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Authors and Affiliations

  1. National Institute for Health and Welfare (THL), Mental Health Promotion Unit, Sarjakatu 2C/2, 65320, Vaasa, Finland

    Fredrica Nyqvist

  2. Department of Social Policy, Åbo Akademi University, Vaasa, Finland

    Mikael Nygård

  3. Netherlands Institute for the Study of Crime and Law Enforcement (NSCR), Amsterdam, The Netherlands

    Wouter Steenbeek

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  1. Fredrica Nyqvist
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Correspondence to Fredrica Nyqvist.

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Nyqvist, F., Nygård, M. & Steenbeek, W. Social Capital and Self-rated Health Amongst Older People in Western Finland and Northern Sweden: A Multi-level Analysis. Int.J. Behav. Med. 21, 337–347 (2014). https://doi.org/10.1007/s12529-013-9307-0

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