J Epidemiol Community Health

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS REGISTER
[Advanced]

Journal of Epidemiology and Community Health 2007;61:165-171; doi:10.1136/jech.2005.041491
Copyright © 2007 by the BMJ Publishing Group Ltd.

This Article
Right arrow Full Text
Right arrow Full Text (PDF)
Right arrow Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this link to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Add article to my folders
Right arrow Download to citation manager
Right arrowRequest Permissions
Citing Articles
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Ramis-Prieto, R.
Right arrow Articles by López-Abente, G.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Ramis-Prieto, R.
Right arrow Articles by López-Abente, G.
Topic Collections
Right arrowRelevant Article

RESEARCH REPORT

Modelling of municipal mortality due to haematological neoplasias in Spain

Rebeca Ramis-Prieto, Javier García-Pérez, Marina Pollán, Nuria Aragonés, Beatriz Pérez-Gómez, Gonzalo López-Abente

Environmental and Cancer Epidemiology Unit, National Centre for Epidemiology, Carlos III Institute of Health, C/Sinesio Delgado, 6, 28029 Madrid, Spain

Correspondence to:
Correspondence to:
R Ramis-Prieto
Environmental and Cancer Epidemiology Unit, National Centre for Epidemiology, Carlos III Institute of Health, C/Sinesio Delgado, 6, 28029 Madrid, Spain; rramis{at}isciii.es

Objectives: To study the geographical pattern of mortality caused by haematological tumours in Spain at the municipal level using three Bayesian models and to compare their goodness of fit.

Methods: The fitted Bayesian hierarchical models were: (1) the Besag York and Molliè (BYM) model; (2) a model based on zero-inflated Poisson (ZIP) distribution, which allowed a large number of event-free areas; and (3) a mixture of distributions that enabled discontinuities (jumps in the pattern) to be modelled. The three models allow smoothed relative risk maps to be obtained for the all countries. The goodness of fit was evaluated using the deviance information criteria.

Results: The three models yielded similar results. The ZIP model plotted a pattern almost identical with the BYM model. The goodness-of-fit criteria indicate that the mixture model is the one that best fits our data. Haematological tumours display a geographical pattern that could be partly explained by environmental determinants, as many of the highest-risk towns are located in heavily industrialised areas.

Conclusions: The choice of one or another model has scant practical consequences. The pattern of distribution supports the hypothesis that differences in lifestyles, air/industrial pollution and migratory phenomena may determine the pattern of urban mortality due to these tumours.


Abbreviations: BYM, Besag York and Molliè; CAR, conditional autoregressive; DIC, Deviance Information Criterion; NHL, non-Hodgkin’s lymphomas; SMR, standard mortality ratio; ZIP, zero-inflated Poisson


Relevant Article

In this issue
Carlos Alvarez-Dardet and John R Ashton
J Epidemiol Community Health 2007 61: 89. [Extract] [Full Text] [PDF]






HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS REGISTER
Terms and conditions relating to subscriptions purchased online  ¦  Website terms and conditions  ¦  Privacy policy
Copyright © 2007 by the BMJ Publishing Group Ltd.