Journal of Geographical Systems

, Volume 15, Issue 2, pp 149–167 | Cite as

Modelling small-area inequality in premature mortality using years of life lost rates

  • Peter CongdonEmail author
Original Article


Analysis of premature mortality variations via standardized expected years of life lost (SEYLL) measures raises questions about suitable modelling for mortality data, especially when developing SEYLL profiles for areas with small populations. Existing fixed effects estimation methods take no account of correlations in mortality levels over ages, causes, socio-ethnic groups or areas. They also do not specify an underlying data generating process, or a likelihood model that can include trends or correlations, and are likely to produce unstable estimates for small-areas. An alternative strategy involves a fully specified data generation process, and a random effects model which “borrows strength” to produce stable SEYLL estimates, allowing for correlations between ages, areas and socio-ethnic groups. The resulting modelling strategy is applied to gender-specific differences in SEYLL rates in small-areas in NE London, and to cause-specific mortality for leading causes of premature mortality in these areas.


Premature mortality Small-area mortality Standardized expected years of life lost Spatial Bayesian 

JEL Classification

I14 C11 C21 C51 

Supplementary material

10109_2012_167_MOESM1_ESM.pdf (29 kb)
PDF (28 KB)


  1. Alho J, Spencer B (2005) Statistical demography and forecasting. Springer, New YorkGoogle Scholar
  2. Aragón T, Lichtensztajn D, Katcher B, Reiter R, Katz M (2008) Calculating expected years of life lost for assessing local ethnic disparities in causes of premature death. BMC Public Health 8:116CrossRefGoogle Scholar
  3. Besag J, York J, Mollié A (1991) Bayesian image restoration, with two applications in spatial statistics. Annal Inst Stat Math 43(1):1–20CrossRefGoogle Scholar
  4. Besag J, Green P, Higdon D, Mengersen K (1995) Bayesian computation and stochastic systems. Stat Sci 10(1):3–41CrossRefGoogle Scholar
  5. Brooks S, Gelman A (1998) General methods for monitoring convergence of iterative simulations. J Comp Graph Stat 7(4):434–445Google Scholar
  6. Centers for Disease Control and Prevention (CDC) (2004) Disparities in premature deaths from heart disease. MMWR Morb Mortal Wkly Rep 53(6):121–125Google Scholar
  7. Chib S, Winkelmann R (2001) Markov chain Monte Carlo analysis of correlated count data. J Bus Econ Stat 19(4):428–435CrossRefGoogle Scholar
  8. Gènova-Maleras R, Catalá-López F, de Larrea-Baz N, Alvarez-Martín E, Morant-Ginestar C (2011) The burden of premature mortality in Spain using standard expected years of life lost: a population-based study. BMC Public Health 11:787CrossRefGoogle Scholar
  9. Gilks W, Richardson S, Spiegelhalter D (1996) Introducing Markov chain Monte Carlo. In: Gilks W, Richardson S, Spiegelhalter DJ (eds) Markov Chain Monte Carlo in practice. Chapman and Hall, Boca Raton, pp 1–16Google Scholar
  10. Graham H (2006) Socioeconomic inequalities in health: evidence on patterns and determinants. Benefits 14(2):77–90Google Scholar
  11. Higdon D (2007) A primer on space-time modelling from a Bayesian perspective. In: Finkenstadt B, Held L, Isham V (eds) Statistics of Spatio-Temporal Systems. Chapman & Hall/CRC, Boca Raton, pp 217–279Google Scholar
  12. James P, Wilkins R, Detsky A, Tugwell P, Manuel D (2007) Avoidable mortality by neighbourhood income in Canada: 25 years after the establishment of universal health insurance. J Epidemiol Community Health 61(4):287–296CrossRefGoogle Scholar
  13. Knorr-Held L (2000) Bayesian modelling of inseparable space-time variation in disease risk. Stat Med 19(17–18):2555–2567CrossRefGoogle Scholar
  14. Knorr-Held L, Rainer E (2001) Projections of lung cancer mortality in West Germany: a case study in Bayesian prediction. Biostatistics 2(1):109–129CrossRefGoogle Scholar
  15. Lee W, Liaw Y (1999) Optimal weighting systems for direct age-adjustment of vital rates. Stat Med 18(19):2645–2654CrossRefGoogle Scholar
  16. Lunn D, Thomas A, Best N (2009) The BUGS project: evolution, critique and future directions. Stat Med 28(25):3049–3067CrossRefGoogle Scholar
  17. McCullagh P, Nelder J (1989) Generalized linear models, 2nd edn. Chapman & Hall, LondonGoogle Scholar
  18. McLaughlin D, Shannon Stokes C, Johnelle Smith P, Nonoyama A (2007) Differential mortality across the U.S.: the influence of place-based inequality. In: Lobao L, Hooks G, Tickamyer A (eds) The sociology of spatial inequality. SUNY Press, Albany, pp 141–162Google Scholar
  19. Mariotti S, D’Errigo P, Mastroeni S, Freeman K (2003) Years of life lost due to premature mortality in Italy. Eur J Epidemiol 18(6):513–21CrossRefGoogle Scholar
  20. Marshall R (2004) Standard expected years of life lost as a measure of mortality: norms and reference to New Zealand data. Aust N Z J Public Health 28(5):452–457Google Scholar
  21. Marshall E, Spiegelhalter D (2007) Identifying outliers in Bayesian hierarchical models: a simulation-based approach. Bayesian Anal 2(2):409–444CrossRefGoogle Scholar
  22. Mathers C, Vos T, Lopez A, Salomon J, Ezzati M (eds) (2001) National burden of disease studies: a practical guide. Edition 2.0. Available at
  23. Mollié A (2000) Bayesian mapping of Hodgkin’s disease in France. In: Elliott P, Wakefield J, Best N, Briggs D (eds) Spatial epidemiology: methods and applications. Oxford University Press, Oxford, pp 267–285Google Scholar
  24. Nolte E, McKee M (2003) Measuring the health of nations: analysis of mortality amenable to health care. Br Med J 327(7424):1129–1132CrossRefGoogle Scholar
  25. Penner D, Pinheiro P, Krämer A (2010) Measuring the burden of disease due to premature mortality using standard expected years of life lost (SEYLL) in North Rhine-Westphalia, a Federal State of Germany, in 2005. J Public Health 18(4):319–325CrossRefGoogle Scholar
  26. Perloff J, LeBailly S, Kletke P, Budetti P, Connelly J (1984) Premature death in the United States: years of life lost and health priorities. J Public Health Policy 5(2):167–184CrossRefGoogle Scholar
  27. Plummer M (2008) Penalized loss functions for Bayesian model comparison. Biostatistics 9(3):523–539CrossRefGoogle Scholar
  28. Rees P, Wohland P (2008) Estimates of ethnic mortality in the UK. Working paper 08/04, School of Geography, University of Leeds (
  29. Richardson S, Monfort C (2000) Ecological correlation studies. In: Elliott P, Wakefield J, Best N, Briggs D (eds) Spatial epidemiology: methods and applications. Oxford University Press, Oxford, pp 104–127Google Scholar
  30. Riggan W, Manton K, Creason J, Woodbury M, Stallard E (1991) Assessment of spatial variation of risks in small populations. Environ Health Persp 96:223–238CrossRefGoogle Scholar
  31. Rue H, Held L (2005) Gaussian Markov random fields. Chapman & Hall/CRC, Boca RatonCrossRefGoogle Scholar
  32. Schopper D, Pereira J, Torres A, Cuende N, Alonso M, Baylin A, Ammon C, Rougemont A (2000) Estimating the burden of disease in one Swiss canton: what do disability adjusted life years (DALY) tell us? Int J Epidemiol 29(5):871–877CrossRefGoogle Scholar
  33. SISA (2011) Discounting and mortality adjusting years of potential life lost (YPLL). Available at
  34. Smith J, Harding S (1997) Mortality of women and men using alternative social classifications. In: Drever F, Whitehead M (eds) Health inequalities. Office for National Statistics, London, pp 168–182Google Scholar
  35. Spiegelhalter D, Best N, Carlin B, van der Linde A (2002) Bayesian measures of model complexity and fit. J R Stat Soc B 64(4):583–639CrossRefGoogle Scholar
  36. Torgerson D, Raftery J (1999) Economic notes: discounting. Br Med J 319(7214):914–915CrossRefGoogle Scholar
  37. Train K (2009) Discrete choice methods with simulation, 2nd edn. Cambridge Univ Press, CambridgeCrossRefGoogle Scholar
  38. White A, Thomson C, Forman D, Meryn S (2010) Men’s health and the excess burden of cancer in men. Eur Urol Suppl 9(3):467–470CrossRefGoogle Scholar
  39. Zhu L, Gorman D, Horel S (2006) Hierarchical Bayesian spatial models for alcohol availability, drug “hot spots” and violent crime. Int J Health Geogr 5:54CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Geography, Centre for StatisticsQueen Mary University of LondonLondonUK

Personalised recommendations