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Monitoring Suicide Mortality: A Bayesian Approach

  • Peter Congdon
Article

Abstract

A significant fall in suicide mortality relative to England and Wales levels has occurred in London though with wide variation between its 33 constituent boroughs in the extent of mortality reduction. A Bayesian random effects approach is used is to model differential changes in suicide by borough and time over a 16 year period, 1979–94. Of particular concern in such modelling are persistent differences between boroughs in suicide risk (temporal correlation) and spatial clustering in relative risk. It is also important to represent the changing impact on suicide of socio-economic factors such as social deprivation. The data used are defined by deaths through de-jure suicide (ICD9 categories E950-E959) and those through undetermined injury, whether accidental or purposely inflicted (ICD E980-E989).

Keywords

Relative Risk Random Effect Public Finance Bayesian Approach Temporal Correlation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Bailey, T. and Gattrell, A., 1995. Interactive Spatial Data Analysis. Longman, London.Google Scholar
  2. Bernardinelli, L., Clayton, D., Pascutto, C., Montomoli, C., Ghislandi, M. and Songini, M., 1995. 'Bayesian analysis of space-time variation in disease risk'. Statistics in Medicine 14: 2433–2443.Google Scholar
  3. Besag, J. and Newell, J., 1991. 'The detection of clusters in rare diseases'. Journal of the Royal Statistical Society 154(A): 143–155.Google Scholar
  4. Besag, J., York, J. and Mollié, A., 1991. 'Bayesian image restoration, with two applications in spatial statistics'. Ann. Inst. Statist. Math. 43: 1–59.Google Scholar
  5. Best, N., 1999. 'Bayesian Ecological Modelling, chapter 14 in Disease Mapping and Risk Assessment for Public Health', A. Lawson (ed), Wiley.Google Scholar
  6. Best, N., Cowles, M. and Vines, K., 1995. 'CODA: Convergence diagnostics and output analysis software for Gibbs sampling output'. Technical Report MRC Biostatistics Unit, Cambridge.Google Scholar
  7. Breslow, N., 1984. 'Extra-Poisson variation in log-linear models'. Applied Statistics 33: 38–44.Google Scholar
  8. Breslow, N. and Clayton, D., 1993. 'Approximate inference in generalized linear mixed models'. Journal of the American Statistical Association 88: 9–25.Google Scholar
  9. Buglass, D. and Duffy, J., 1978. 'The ecological pattern of suicide and parasuicide in Edinburgh'. Social Science and Medicine 12: 241–253.Google Scholar
  10. Carlin, B. and Chib, S., 1995. 'Bayesian model choice via Markov chain Monte Carlo methods'. J. R. Stat. Soc., Ser. 57(3): 473–484.Google Scholar
  11. Carlin, B. and Louis, T., 1996. 'Bayes and Empirical Bayes Methods for Data Analysis'. Chapman and Hall, London.Google Scholar
  12. Cressie, N., 1993. 'Statistics for Spatial Data', Wiley.Google Scholar
  13. Cressie, N. and Read, T., 1989. 'Spatial Data Analysis of Regional Counts'. Biometrical Journal 6: 699–719.Google Scholar
  14. Department of Health, 1992. The Health of the Nation: a Strategy for Health in England (Cm.1986) HMSO.Google Scholar
  15. Department of Health, 1998. Our Healthier Nation: a Contract for Health (Cm.3854) HMSO.Google Scholar
  16. Durkheim, E., 1897. Le suicide, Felix Alcan, Paris.Google Scholar
  17. Farmer, R., Preston, T. and O'Brien, S., 1977. 'Suicide mortality in Greater London: changes during the past 25 years'. British Journal of Preventive and Social Medicine 31: 171–177.Google Scholar
  18. Freeman, H., 1994. 'Schizophrenia and city residence'. British Journal of Psychiatry 164(suppl. 23): 39–50.Google Scholar
  19. Geisser, S. and Eddy, W., 1979. 'A predictive approach to model selection'. J. Am. Stat. Assoc. 74: 153–160.Google Scholar
  20. Gelfand, A., Dey, D. and Chang, H., 1992. 'Model determination using predictive distributions with implementations via sampling-based methods', in J. Bernardo et al. (eds), Bayesian Statistics 4, Oxford Univ Press, 147–168.Google Scholar
  21. Gelfand, A. and Dey, D., 1994. 'Bayesian model choice: Asymptotics and exact calculations'. J. R. Stat. Soc., Ser. B 56(3): 501–514.Google Scholar
  22. Gelman, A., Carlin, J., Stern, H. and Rubin, D., 1995. Bayesian Data Analysis, Chapman and Hall.Google Scholar
  23. Gibbins, R., Clark, D. and Fawcett, J., 1990. 'A statistical method for evaluation of suicide clusters and implementing cluster surveillance'. American Journal of Epidemiology 132(Supp 1): 5183–5191.Google Scholar
  24. Gunnell, D., Peters, T., Kammerling, R. and Brooks, J., 1995. 'Relation between parasuicide, suicide, psychiatric admissions and socio-economic deprivation'. British Medical Journal 311: 226–230.Google Scholar
  25. Haining, R., 1991. 'Estimation with heteroscedastic and correlated errors: a spatial analysis of intraurban mortality data'. Papers in Regional Science 70(3): 223–241.Google Scholar
  26. Hamm, J., Mordan, D., Jacobson, B. and Bardsley, M., 1997. Will London meet Health of the Nation targets?. The Health of Londoners Project Working Paper, East London and the City Health Authority, London E3 2SE.Google Scholar
  27. Hamnett, C., 1987. 'A tale of two cities: sociotenurial polarisation in London and the South East, 1966-81'. Environment and Planning A19, 537–556.Google Scholar
  28. Hsiao, C. and Tahmiscioglu, A., 1997. 'A panel analysis of liquidity constraints and firm investments'. J. Am Stat. Ass, 455-465.Google Scholar
  29. Isaaks, E. and Srivastava, R., 1989. Applied Geostatistics. Oxford University Press, New York.Google Scholar
  30. Jarvis, G., Ferrence, R., Whitehead, P. and Gordon Johnson, F., 1978. 'The ecology of self-injury: a multivariate approach'. Suicide and Life-Threatening Behaviour 12: 90–102.Google Scholar
  31. Kass, R. and Raftery, A., 1993. 'Approximate Bayes Factors and Accounting for Model Uncertainty in Generalized Linear Models'. Technical Report no. 255, Statistics Dept, Univ of Washington.Google Scholar
  32. Kerkhof, A. and Kunst, A., 1994. A European perspective on suicidal behaviour. Chapter 3 in The Prevention of Suicide, Department of Health, HMSO.Google Scholar
  33. Maddala, G., 1979. Econometrics, McGraw-Hill.Google Scholar
  34. Manton, K., Stallard, E., Woodbury, M., Riggan, W., Creason, J. and Mason, T., 1987. 'Statistically adjusted estimates of geographic mortality profiles'. Journal of the National Cancer Institute 78: 805–815.Google Scholar
  35. Moens, G., Haenen, W. and van der Voorde, H., 1988. 'Epidemiological aspects of suicide among the young in selected European countries'. J Epid. Comm. Health. 42: 279–285.Google Scholar
  36. Moksony, F., 1990. 'Ecological Analysis of Suicide: Problems and Prospects, Chapter 8 in Current Concepts in Suicide', in D. Lester (ed.), The Charles Press, Philadelphia.Google Scholar
  37. Mortensen, P. B., Agerbo, E. and Erikson, T., 2000. 'Psychiatric illness and risk factors for suicide in Denmark'. The Lancet 355: 9–12.Google Scholar
  38. Mollié, A., 1996. 'Bayesian mapping of disease, chapter 20 in Markov Chain Monte Carlo in Practice', in W. Gilks, S. Richardson and D. Spiegelhalter (eds), Chapman and Hall, London.Google Scholar
  39. Newton, M. and Raftery, A., 1994. 'Approximate Bayesian inference with the weighted likelihood bootstrap'. J. R. Stat. Soc., Ser. B 56(1): 3–48.Google Scholar
  40. Ovenstone, I., 1973. 'Spectrum of suicidal behaviours in Edinburgh'. British Journal of Preventive and Social Medicine 27: 27–35.Google Scholar
  41. Phillimore, P. and Reading, R., 1992. 'A rural advantage? Urban-rural health differences in Northern England'. Journal of Public Health Medicine 14(3): 290–299.Google Scholar
  42. Raftery, A., 1996. 'Hypothesis testing and model selection', in W. R. Gilks et al. (eds), Markov Chain Monte Carlo in Practice. Chapman &; Hall, London, 163–187.Google Scholar
  43. Sainsbury, P., 1980. 'The social correlates of suicide in Europe', in R. Farmer and S. Hirsch (eds), The Suicide Syndrome. Croom Helm. London, 38–53.Google Scholar
  44. Spiegelhalter, D., Thomas, A., Best, N. and Gilks,W., 1996. BUGS: Bayesian Inference using Gibbs sampling, version 0.50. MRC Biostatistics Unit, Cambridge.Google Scholar
  45. Spiegelhalter, D., Best, N. and Carlin, B., 1999. Bayesian deviance, the effective number of parameters, and the comparison of arbitrarily complex models, manuscript, MRC Biostatistics Unit, Cambridge CB2 2SR.Google Scholar
  46. West, M. and Harrison, P., 1989. 'Bayesian Forecasting and DynamicModels'. Springer-Verlag, New York.Google Scholar
  47. Zellner, A., 1996. 'An Introduction to Bayesian Inference in Econometrics', Wiley.Google Scholar
  48. Weakliem, D., 1999. A Critique of the Bayesian Information Criterion for Model Selection Sociological Methods and Research, 1999, 27, 3, Feb, 359–397.Google Scholar
  49. Yang, Y., 1999. 'Model selection for nonparametric regression'. Statistica Sinica 9(2): 475–499.Google Scholar

Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • Peter Congdon
    • 1
    • 2
  1. 1.Department of GeographyQueen Mary and Westfield CollegeLondon
  2. 2.Department of Public Health, Barking and Havering Health AuthorityBarking

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