Sage Journals: Discover world-class research

Abstract

In this paper we propose a hierarchical Bayesian model to study the variation in space and time of disease risk. We represent spatial effects following the usual Bayesian specification of a Gaussian convolution of unstructured and structured components, while we adopt the birth cohort (instead of the commonly used period of death) as the main time scale. The model also includes space-time interaction terms to take into account structured inseparable space-time variability. The model is applied to lung cancer death certificate data in Tuscany, for males during the period 1971-94. While a calendar period analysis points out a general increase of mortality levelling off in the last period (1990-94), the cohort model shows a general and substantial decrease of the relative risk for the youngest cohorts born after 1930. Moreover, the pattern of the epidemic by birth cohort presents a maximum which varies by municipalities, with a strong north-west/south-east gradient.

Keywords

cohort effects hierarchiical Bayesian model space-time analysis

Get full access to this article

View all access options for this article.

References

Assunçao RM , Reis IA , Di Lorenzo Oliveira C (2000) Diffusion and prediction of Leishmaniasis in a large metropolitan area in Brazil with a Bayesian space-time model. Web page http://www.est.ufmg.br/%7Eassuncao/art/art.html, e-mail: assuncao@est.ufmg.br

Bernardinelli L , Clayton D , Pascutto C , Montomoli C et al. (1995) Bayesian analysis of space-time variation in disease risk . Statistics in Medicine 14, 2433-2443 .

Besag J , York J , Mollie A (1991) Bayesian image restoration, with two applications in spatial statistics (with discussion) . Annals of the Institute of Statistical Mathematics 43, 1-59 .

Clayton D , Schifflers E (1987a) Models for temporal variation in cancer rates. I: age-period and age-cohort models . Statistics in Medicine 6, 449-467 .

Clayton D , Schifflers E (1987b) Models for temporal variation in cancer rates. II: age-period-cohort models . Statistics in Medicine 6, 469-481 .

Clayton D (1996) Generalized linear mixed models. In: Gilks WR , Richardson S , Spiegelhalter DJ , eds. Markov Chain Monte Carlo in practice. London: Chapman and Hall , 275-301.

Gelman A , Rubin DR (1992) Inference from iterative simulation using multiple sequences (with discussion) . Statistical Science 7, 457-511 .

Kass RE , Raftery AE (1995) Bayes factors . Journal of the American Statistical Association 90, 773-795 .

Knorr-Held L , Besag J (1998) Modelling risk from a disease in time and space . Statistics in Medicine 17, 2045-2060 .

10.

Knorr-Held L (2000a) Bayesian modelling of inseparable space-time variation in disease risk . Statistics in Medicine 19, 2555-2568 .

11.

Knorr-Held L (2000b) Prognosis of lung cancer mortality in West Germany. A case study in Bayesian prediction . Biostatistics (to appear).

12.

Lilienfeld AM , Lilienfeld DE (1980) Foundations of epidemiology. New York: Oxford University Press .

13.

Lopez-Abente G , Pollan M , de la Iglesia P , Ruiz M (1995) Characterization of the lung cancer epidemic in the European Union (1970-1990) . Cancer Epidemiology Biomarkers and Prevention 4(8), 813-820 .

14.

Mason TJ , McKay FW , Hoover R , Blot WJ et al. (1975) Atlas of cancer mortality for U.S. Counties: 1950-1969, Washington: USGPO .

15.

Mayer JD (1983) The role of spatial analysis and geographic data in the detection of disease causation . Social Science and Medicine 17(16), 1213-1221 .

16.

Spiegelhalter DJ , Best NG , Carlin BP (1998) Bayesian deviance, the effective number of parameters, and the comparison of arbitrarily complex models. Research Report 98-009, Division of Biostatistics, University of Minnesota .

17.

Spiegelhalter DJ , Thomas A , Best NG , Gilks WR (2000) WinBUGS. Medical Research Council Biostatistics Unit, Cambridge .

18.

Sun D , Tsutakawa RK , Kim H , He Z (2000) Spatio-temporal interaction with disease mapping . Statistics in Medicine 19, 2015-2035 .

19.

Vigotti MA , Biggeri A , Dreassi E , Baccini M et al. (1999) Atlas of mortality in Tuscany 1971-94 . Proceedings of the XV international scientific meeting of the international epidemiological association ‘Epidemiology for sustainable health’, vol. 1, p 200-200 .

20.

Waller LA , Carlin BP , Xia H , Gelfand AE (1997) Hierarchical spatio-temporal mapping of disease rates . Journal of the American Statistical Association 92, 607-617 .

A hierarchical Bayesian model for space-time variation of disease risk

Abstract

Keywords

Get full access to this article

References