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Space-Time Variation of Cervical Cancer Mortality in Belgium Using an Hierarchical Bayesian Model

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Space-Time Variation of Cervical Cancer Mortality in Belgium Using an Hierarchical Bayesian Model Europe Against Cancer European Network for Cervical Cancer Screening Grant Agreement n° SPC.2002475 (16 Dec 2002 - 15 Dec 2003) To co-ordinate and link quality assurance, monitoring and new Scientific Institute technologies in cervical cancer screening in the EU of Public Health SPACE-TIME VARIATION OF CERVICAL CANCER MORTALITY IN BELGIUM USING AN HIERARCHICAL BAYESIAN MODEL (BELGIUM, 1969-1994) Marc ARBYN1,2 Francis CAPET2 Arnošt KOMÁREK3 Emmanuel LESAFFRE3 1. European Network for Cervical Cancer Screening 2. Scientific Institute of Public Health, Unit of Epidemiology 3. Centre for Biostatistics, Faculty of Medicine Catholic University of Leuven, Kapucijnenvoer, B-3000 Leuven IPH/EPI REPORTS Nr. 2003 –024 Epidemiology, December 2003; Brussels (Belgium) Scientific Institute of Public Heatlh, SIPH/EPI REPORTS N 2003 - 024 Depotnumber: D/2003/2505/024 Europe Against Cancer European Network for Cervical Cancer Screening Grant Agreement n° SPC.2002475 (16 Dec 2002 - 15 Dec 2003) To co-ordinate and link quality assurance, monitoring and new technologies in cervical cancer screening in the EU SPACE-TIME VARIATION OF CERVICAL CANCER MORTALITY IN BELGIUM USING AN HIERARCHICAL BAYESIAN MODEL (BELGIUM, 1969-1994) Marc ARBYN1,2 Francis CAPET2 Arnošt KOMÁREK3 Emmanuel LESAFFRE3 1. European Network for Cervical Cancer Screening 2. Scientific Institute of Public Health, Unit of Epidemiology 3. Centre for Biostatistics, Faculty of Medicine Catholic University of Leuven, Kapucijnenvoer, B-3000 Leuven Scientific Institute of Public Health J. Wytsmanstr. 14 1050 Brussels Belgium Tel: + 32 2 642 50 21 Fax: +32 2 642 54 10 e-mail: [email protected] http://www.iph.fgov.be/epidemio/ IPH/EPI REPORTS Nr. 2003 – 024 SpaceTimeBayModCvxBelgium3CoverMenIPH.doc 13/11/1999 - 14:01 2 1. Table of contents 1. TABLE OF CONTENTS................................................................................... 3 2. ABSTRACT........................................................................................................ 5 2.1. BACKGROUND ................................................................................................... 5 2.2. MATERIAL AND METHODS ................................................................................. 5 2.3. RESULTS............................................................................................................ 5 2.4. DISCUSSION....................................................................................................... 5 3. KEY WORDS..................................................................................................... 6 4. INTRODUCTION.............................................................................................. 6 5. MATERIALS ..................................................................................................... 7 6. METHODS ......................................................................................................... 7 6.1. INTRODUCTION TO BAYESIAN MODELS ............................................................. 7 6.2. SPACE-COHORT MODEL (SC)............................................................................. 8 6.2.1. Log-linear Poisson age-cohort model ................................................. 8 6.2.2. Bayesian Space-Cohort mode (SC)...................................................... 9 6.2.3. Prior distributions for the SC model.................................................. 10 6.3. SPACE-PERIOD MODEL (SP)............................................................................. 11 6.3.1. Log-linear Poisson age-period model ............................................... 11 6.3.2. Bayesian Space-Period model (SP) ................................................... 12 6.4. CARTOGRAPHIC DISPLAY STYLE...................................................................... 14 7. RESULTS ......................................................................................................... 15 7.1. SPACE-COHORT MODEL ................................................................................... 15 7.1.1. Log-linear Poisson age-cohort model ............................................... 15 7.1.2. Bayesian SC model ............................................................................ 15 7.1.3. Cohort effects estimated from the SC model...................................... 16 7.1.4. Space effects estimated from the SC model ....................................... 18 7.1.5. Local cohort effects (S*C model)....................................................... 20 7.1.6. Contrast between local raw SCMRs and fitted cohort effects estimated from the S*C model ........................................................... 23 7.2. SPACE-PERIOD MODEL..................................................................................... 26 7.2.1. Loglinear Poisson age-period model................................................. 26 7.2.2. Bayesian SP model............................................................................. 26 7.2.3. Period effects estimated from the SP model ...................................... 27 7.2.4. Space effects estimated from the SP model........................................ 29 7.2.5. Local period effects (S*P model)....................................................... 30 7.2.6. Contrast between local raw SMRs and fitted period effects estimated from the S*P model............................................................ 33 8. DISCUSSION ................................................................................................... 37 9. LIST OF ABBREVIATIONS ......................................................................... 42 SpaceTimeBayModCvxBelgium3CoverMenIPH.doc 13/11/1999 - 14:01 3 10. BIBLIOGRAPHY........................................................................................ 43 11. ANNEXES .................................................................................................... 46 11.1. WINBUGS PROGRAMMES, INITIALS, DATA .................................................. 46 11.2. LOCATION OF FILES ....................................................................................... 62 11.3. ALTERNATIVE CARTOGRAPHIC DISPLAY FOR THE SPATIAL DISTRIBUTION OF DISTRICT EFFECTS. ................................................................................... 63 11.4. SELECTED OUTPUT ........................................................................................ 65 11.4.1. Space-cohort model without interactions .......................................... 65 11.4.2. Space-cohort model with interactions ............................................... 70 11.4.3. Space-period model without interactions .......................................... 75 11.4.4. Space-period model with interactions ............................................... 80 SpaceTimeBayModCvxBelgium3CoverMenIPH.doc 13/11/1999 - 14:01 4 2. Abstract 2.1. Background The variation of cervical cancer mortality in Belgium over time and place was analysed in separate previous studies. In this report we study changes over time and district (arrondissement) simultaneously using Bayesian hierarchical models. 2.2. Material and methods Data on mortality from cervical cancer, coded as 180, according to the 8th and 9th ICD classification, in the 43 Belgian districts, were obtained from the National Institute of Statistics. Variation over two time dimensions (cohort and period) and over place (districts), after controlling for age, was modelled using Bayesian models, following the approach described by Lagazo et al, 2001. Spatial components were defined as a Gaussian convolution of structured and unstructured components (CAR models). The Deviance Information Criterion (DIC) was used to compare models with and without space*time interactions. 2.3. Results Mortality changed substantially over successive cohorts. In global, mortality was higher than average in the older cohorts, women born before the 1920s, and lower than average in the younger cohorts born later. Two upward peaks could be discerned for the 1890-1899 and the 1915-1924 cohort. Since cohort 1930-39, mortality did not further decline. Even a discrete increase could be discerned in the youngest cohorts. In spite of the complex change over cohorts, mortality declined rather monotonously over calendar periods. The spatial effects relative to the average mortality varied between 0.73 and 1.52. Increased mortality was found in 5 neighbouring districts in the mid-west of Belgium: Charleroi, Soignies, Thuin, Namur and Philippeville, and further in the districts of Antwerp, Gent and Eeklo in the North of Belgium. A lower mortality risk (relative risk between 0.6 and 0.8) was observed in four districts: Tunhout, Tielt and the two Flemish-Brabant districts. The DIC was marginally smaller for models including interactions. Nevertheless their impact was practically ignorable. 2.4. Discussion Hierarchical Bayesian modelling yielded similar common time trend effects as obtained from previous ACP-Poisson regression models. But this time spatial effects could be modelled simultaneously, where ACP models would get into problems because of redundancy and over-saturation. Robust district and cohort effects could be estimated that were not influenced by space*time interaction. The risk of mortality is higher than average in districts were large agglomerations are localised, probably because of increased transmission in cities of the sexually transmittable HPV-virus, which is the main etiological risk factor for cervical
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