IJE Advance Access originally published online on March 25, 2007
International Journal of Epidemiology 2007 36(2):345-347; doi:10.1093/ije/dym040
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Commentary: Area social cohesion, deprivation and mental health—Does misery love company?
Curtin University of Technology and the Telethon Institute for Child Health Research, PO Box 855, West Perth, 6872, Western Australia. E-mail: S.Zubrick{at}curtin.edu.au
Accepted 14 February 2007
Fone and his colleagues1 ask us to consider the relationship between area-level social cohesion and individual mental health. At the outset readers should appreciate that population measures of mental health distress, similar to the one used by Fone et al. have been shown to be significantly related to measures of serious mental health disorders.2 While there are variations in the quality of these measures, with some providing greater efficiency than others, the emergent evidence suggests that brief, structured screening scales of mental health distress can reproduce classifications based on lengthier clinical interviews of mental disorders.3 Such measurement studies are important in ‘cross-walking’ the findings between routine community surveys of mental health distress with the less frequent and more intense efforts of clinical epidemiology to estimate the prevalence of specific mental health disorders in population surveys.
These mental health measures provide an important indication of population well-being. In global burden of disease parlance, mental health disorders are prevalent and associated with a substantial personal, social and economic burden.4,5 In developed countries, their share of the global burden of disease is predicted to increase. When they occur they tend to be persistent across the lifecourse,6–8 are largely untreated,9 costly when they are treated,10 and associated with inequities in the delivery of health care that lead to significantly higher levels of physical health morbidity and mortality in individuals with mental health disorders.11
Of the many determinants of mental health status studied to date most have been estimated at the individual level with little attention or opportunity to demonstrate meso- and macro- level influences. Fone et al. bring forward such evidence and show that income deprivation and social cohesion at the small-area level are significantly and independently associated with poor mental health status. Moreover, the effect of income deprivation on mental health status is reduced in areas of high social cohesion and is greater in areas of low social cohesion. In their multi-level analyses they show that this effect modification operates at the community level, not the individual level. In practical terms they suggest that in deprived areas, high levels of community social cohesion based on friendships, visiting, and borrowing and exchange of favours with neighbours is potentially of importance in protecting mental health.
So, we add this observation to growing efforts to document how outcomes in physical health and (now) mental health at an individual level vary with characteristics defined and measured at the level of groups—be they families, neighbourhoods, or in this case, small areas. The value of the current contribution is provisional and the authors say this in several ways. Early in their presentation they call for greater clarity in concepts, definitions, measures and the operational procedures for studying causal links between the social environment and health. They later note that no inferences about cause can be made from their cross-sectional data and call for longitudinal methods to investigate causal pathways. The documented effects in their present contribution invite consideration of the next steps and the authors do this by way of calling for better science and theory. What might some of these improvements to future research in this area entail?
Certainly advances are needed in both theory and measures. At present there is a plethora of proposed social capital measures in the absence of highly articulated and specified theoretical models that differentiate these measures and test how they mediate outcomes of interest.12 As one adds levels to a model, theoretical differentiation and measurement precision need to be applied within each level in order to attain theoretical clarity and fidelity about the links between, in this case, social support, social capital and the proposed mechanisms that operate to produce changes in the outcome of interest.
For example, at the level of individuals, measures of social support are commonly used in community and clinical epidemiology. In his review of concepts, measures and models, Barrera noted that the term social support is ‘insufficiently specific to be useful as a research concept’ and instead argued for more specific terminology to distinguish social support concepts along with more molar measures to test their association with outcomes of interest.13 Since then, social support concepts and measures have been differentiated along three principal domains: the extent to which individuals are attached to significant others as measured by their social ties, participation in organizations, contact with friends and family and/or the complexity of their social network (e.g. social embeddedness); the individual's cognitive appraisal (e.g. perceived social support) of the availability and adequacy of support irrespective of the extent of the support; and the responses of others in the provision of emotional support, information, tangible care or material assistance.14–16 These measures have been used in large-scale population surveys of individuals as well as studies of patients seeking health care from physicians.17,18
At the level of measuring communities there is much that remains yet to be done. Most small area analyses are confined to census enumeration districts, and there is still the need for studies that have implemented better methods for setting area boundaries around more meaningful communities or neighbourhoods in terms of the lived lives of individuals. Once done, consideration needs to be given to what constitutes a ‘measured’ community. Raudenbush and Sampson19,20 have underscored the need to take ecological assessment seriously and as conceptually distinct from individual-level assessment. They adapt and extend existing analytical strategies in psychometrics by applying them to both survey-based measures of ecological settings gathered from survey respondents as well as systematic social observations of the area that are independent of the perceptions of the survey respondents. In light of the call for improved multi-level measures any cursory examination of the purported range of measures of social capital and what little we know about their reliability and validity, shows us that considerable scientific gains are yet to made.
While practical work can be undertaken to develop quality measures of social capital and apply them to the appropriate study of individuals and higher level aggregates (i.e. families, schools, neighbourhoods, communities) there is a critical need to articulate the theoretical basis that links social capital to outcomes of interest. Fone et al. show that income deprivation and social cohesion measured at the area level are significantly associated with mental health distress. Acknowledging the need to replicate the finding, is it the case that this relationship is causal? Where do we imagine that change takes place—in other words, how is this effect mediated between individuals and the communities in which they live? Can we think of this as an ‘exposure’—and is more of it better for you? Our measure of social capital as defined here is a measure about friendships, visiting, and borrowing and the exchange of favours among individuals in an area. In other words, they are about connections among people. If we hypothesize that connections between individuals and groups have a bearing on mental health, then our attention must focus on these exchanges and their prompts, facilitators and constraints. Is it possible to move to the study of transactions that are enabled through these connections and that are relevant to health and mental health status? How are these measured at differing levels, and to what extent are they distinct? Critical transactions include relationships among community members that enable social and material benefits to individuals and groups, the provision of institutional resources which make a difference in outcomes, and norms and collective efficacy that produce shared expectations and standards.21 Where do these come from and, importantly, for whom do these transactions work? What is endowed by virtue of individual and group membership, be it gender, race or class, and what are the structural barriers to these transactions? Finally, how do they work over time?
That the extent to which area social cohesion is associated with the individuals living in them feeling more mentally healthy, and that this modifies the negative effect of area deprivation on mental health, may be intuitively pleasing. Perhaps the findings of Fone and colleagues will suggest to some that misery does not like company. In the epidemiology of mental ill-health, misery is certainly prevalent and opportunities to protect and promote population mental health increasingly urgent.22 Practically, however, the call to action here is one that encourages our next steps toward greater rigour in both the theory and the development of quality measures of social capital, along with an explicit articulation and testing of the mechanisms that mediate these area-level influences and individual outcomes.
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