Journal of Geographical Systems

, Volume 12, Issue 1, pp 1–23 | Cite as

A multiple indicator, multiple cause method for representing social capital with an application to psychological distress

Original Article
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Abstract

This paper describes a structural equation methodology for obtaining social capital scores for survey subjects from multiple indicators of social support, neighbourhood and trust perceptions, and memberships of organizations. It adjusts for variation that is likely to occur in levels of social capital according to geographic context (e.g. level of area deprivation, geographic region, level of urbanity) and demographic group. Social capital is used as an explanatory factor for psychological distress using data from the 2006 Health Survey for England. A highly significant effect of social capital in reducing the chance of psychiatric caseness is obtained after controlling for other individual and geographic risk factors. Allowing for social capital has considerable effects on the impacts on psychiatric health of other risk factors. In particular, the impact of area deprivation category is much reduced. There is also evidence of significant differentiation in social capital between population categories and geographic contexts.

Keywords

Social capital Structural equation Psychological distress Area deprivation Bayesian 

JEL Classification

C11 C30 I10 
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Notes

Acknowledgments

The analysis in this paper is based on the Health Survey for England data held at the ESRC Data Archive. Neither the survey depositors nor the Data Archive bear any responsibility for the analysis presented in the paper.

References

  1. Bayard-Burfield l,Sundquist J, Johansson S (2001) Ethnicity, self reported psychiatric illness, and intake of psychotropic drugs in five ethnic groups in Sweden. J Epidemiol Community Health 55(9):657–664CrossRefGoogle Scholar
  2. Besag J, York J, Mollie A (1991) Bayesian image restoration, with two applications in spatial statistics. Ann Inst Stat Math 43(1):1–21CrossRefGoogle Scholar
  3. Best N, Arnold R, Thomas A (1999) Bayesian models for spatially correlated disease and exposure data. In: Bernardo J, Berger J, Dawid A, Smith A (eds) Bayesian statistics, vol 6. Oxford University Press, Oxford, pp 131–156Google Scholar
  4. Brooks S, Gelman A (1998) General methods for monitoring convergence of iterative simulations. J Comput Graph Stat 7(4):434–445CrossRefGoogle Scholar
  5. Cheng S, Fung H, Chan A (2008) Living status and psychological well-being: social comparison as a moderator in later life. Aging Ment Health 12(5):654–661CrossRefGoogle Scholar
  6. Calderwood L, Tait, C. (2000) Self-reported health and psychosocial well-being. In: Erens B, Primatesta P, Prior G (eds) Health Survey for England 1999. HMSO, London. Available at http://www.archive.official-documents.co.uk/document/doh/survey99/hse99-02.htm
  7. Cooper H, Arber S, Fee L, Ginn J (1999) The influence of social support and social capital on health: a review and analysis of British data. Health Education Authority, LondonGoogle Scholar
  8. Everitt B (1984) An introduction to latent variable models. Chapman and Hall, LondonGoogle Scholar
  9. Farmer A, Chubb H, Jones I, Hillier J, Smith A, Borysiewicz L (1996) Screening for psychiatric morbidity in subjects presenting with chronic fatigue syndrome. Br J Psychiatry 168:354–358CrossRefGoogle Scholar
  10. Feinstein L, Sabates R, Anderson T, Sorhaindo A, Hammond C (2006) What are the effects of education on health. In: Desjardins R Schuller T (eds) Measuring the Effects of Education on Health and Civic Engagement, Ch 4. OECD, Paris, pp 171–354Google Scholar
  11. Gelfand A, Smith A (1990) Sampling based approaches to calculate marginal densities. J Am Stat Assoc 85(410):398–409CrossRefGoogle Scholar
  12. Hall C, Rafferty A, Higgins V (2008) Social capital: introductory user guide. ESDS government. Available online at http://www.esds.ac.uk
  13. Harpham T, Grant E, Thomas E (2002) Measuring social capital within health surveys: key issues. Health Policy Plan 17(1):106–111CrossRefGoogle Scholar
  14. Harpham T, Grant E, Rodriguez, C (2004) Mental health and social capital in Cali, Colombia. Soc Sci Med 58(11):2267–2277CrossRefGoogle Scholar
  15. Hoeting J (2009) The importance of accounting for spatial and temporal correlation in analyses of ecological data. Ecol Appl 19(3):574–577CrossRefGoogle Scholar
  16. Kessler R, Essex M (1982) Marital status and depression: the importance of coping resources. Soc Forces 61:484–507CrossRefGoogle Scholar
  17. Lee S-Y (2007) Structural equation modeling: a Bayesian approach. Wiley, ChichesterCrossRefGoogle Scholar
  18. Legler J, Ryan L (1997) Latent variable models for teratogenesis using multiple binary outcomes. J Am Stat Assoc 92(437):13–20CrossRefGoogle Scholar
  19. Lunn D, Thomas A, Best N, Spiegelhalter D (2000) WinBUGS-a Bayesian modelling framework: concepts, structure, and extensibility. Stat Comput 10(4):325–337CrossRefGoogle Scholar
  20. Macintyre S, Ellaway A, Cummins S (2002) Place effects on health: how can we conceptualise, operationalise and measure them? Soc Sci Med 55(1):125–139CrossRefGoogle Scholar
  21. Macintyre S, Ellaway A, Hiscock R, Kearns A, Der G, McKay L (2003) What features of the home and the area might help to explain observed relationships between housing tenure and health? Evidence from the west of Scotland. Health Place 9(3):207–218CrossRefGoogle Scholar
  22. Macinko J, Starfield B (2001) The utility of social capital in research on health determinants. Milbank Q 79(3):387–427CrossRefGoogle Scholar
  23. Martin A, Rief W, Klaiberg A, Braehler E (2006) Validity of the brief patient health questionnaire mood scale (PHQ-9) in the general population. Gen Hosp Psychiatry 28(1):71–77CrossRefGoogle Scholar
  24. Oud J, Folmer H (2008) A structural equation approach to models with spatial dependence. Geogr Anal 40(2):152–166CrossRefGoogle Scholar
  25. Petrou S, Kupek E (2008) Social capital and its relationship with measures of health status: evidence from the Health Survey for England 2003. Health Econ 17(1):127–143CrossRefGoogle Scholar
  26. Plummer M (2008) Penalized loss functions for Bayesian model comparison. Biostatistics 9(3):523–539CrossRefGoogle Scholar
  27. Poortinga W (2006) Do health behaviors mediate the association between social capital and health? Prev Med 4(6):488–493CrossRefGoogle Scholar
  28. Radloff L (1977) The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Meas 1:385–401CrossRefGoogle Scholar
  29. Robbins A (2002) What’s the relative risk? A method to directly estimate risk ratios in cohort studies of common outcomes. Ann Epidemiol 12(7):452–454CrossRefGoogle Scholar
  30. Sammel M, Ryan L, Legler, J (1997) Latent variable models for mixed discrete and continuous outcomes. J R Stat Soc Series B 59(3):667–678CrossRefGoogle Scholar
  31. Schumacker R, Lomax R (2004) A Beginner’s guide to structural equation modeling, 2nd edn. Psychology Press, LondonGoogle Scholar
  32. Shields M, Price S (2005) Exploring the economic and social determinants of psychological well-being and perceived social support in England. J R Stat Soc A 168(3):513–537CrossRefGoogle Scholar
  33. Spiegelhalter D, Best N, Carlin B, van der Linde A (2002) Byesian measures of model complexity and fit. J R Stat Soc B 64(4):583–640CrossRefGoogle Scholar
  34. van Jaarsveld C, Miles A, Wardle J (2007) Pathways from deprivation to health differed between individual and neighborhood-based indices. J Clin Epidemiol 60(7):712–719CrossRefGoogle Scholar
  35. Veenstra G (2005) Location, location, location: contextual and compositional health effects of social capital in British Columbia, Canada. Soc Sci Med 60(9):2059–2071CrossRefGoogle Scholar
  36. Veenstra G, Luginaah I, Wakefield S, Birch S, Eyles J, Elliott S (2005) Who you know, where you live: social capital, neighbourhood and health. Soc Sci Med 60(12):2799–2818CrossRefGoogle Scholar
  37. Vines S, Gilks W, Wild P (1996) Fitting Bayesian multiple random effects models. Stat Comput 6(4):337–346CrossRefGoogle Scholar
  38. Wedel M, Kamakura W (2001) Factor models with (mixed) observed and latent variables in the exponential family. Psychometrika 66(4):515–553CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  1. 1.Department of Geography and Center for StatisticsQueen Mary University of LondonLondonUK

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