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International Biometric Society
APPLICATION OF SPATIAL BAYESIAN POISSON REGRESSION TO IDENTIFY ASSOCIATIONS
BETWEEN EHEC INCIDENCE AND SOCIO-ECONOMIC FACTORS
Maike Tahden (1), Juliane Manitz (2), Thomas Kneib (2), Guido Hegasy (3)
(1) Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen,
Germany
(2) University of Göttingen, Göttingen, Germany
(3) Institute for Hygiene and Environment, Hamburg, Germany
From May to July 2011, a large outbreak of gastroenteritis and hemolytic-uremic syndrome
(HUS) caused by EHEC O104:H4 occured in Germany with almost 4000 persons infected
and 53 fatalities. Consumption of contaminated fenugreek sprouts was demonstrated by
epidemiological studies as the cause of the outbreak [1]. Although cases were reported from
all Federal States, highest incidences were recorded for the northern part of Germany with
the City of Hamburg as a focus of the outbreak. It has been an early observation in Hamburg
that particularly middle-aged women were affected [2]. This prompted us to analyze risk
factors for contraction of EHEC infection in this outbreak based on socio-economic
parameters and EHEC incidences in the 100 districts of Hamburg. For statistical analysis, we
performed similar to Schrödle and Held [3] an ecological regression, specifically a Bayesian
Poisson model with covariates as well as structured and unstructured spatial effects. For fast
and efficient inference, we applied the software R-INLA using integrated nested Laplace
approximations (INLA) for posteriors [4]. Moreover, missing data imputation and variable
selection was necessary for meaningful analysis. For this purpose, we applied three different
methods of imputation and implemented a stepwise regression algorithm by using the
Deviance Information Criterion (DIC) for variable selection. The final model possessed high
explanatory performance; however, the resulting covariates differed from our expectations
and did not exhibit high Pearson correlation with the target variable. Therefore, we developed
a simulation study to assess the relation between model effect strength and correlation. We
could demonstrate that only for large sample sizes increasing model effects result in stronger
correlation.
References:
[1] Buchholz U, Bernard H, Werber D, Böhmer MM, Remschmidt C, Wilking H, Deleré Y, an der
Heiden M, Adlhoch C, Dreesman J, Ehlers J, Ethelberg S, Faber M, Frank C, Fricke G, Greiner M,
Höhle M, Ivarsson S, Jark U, Kirchner M, Koch J, Krause G, Luber P, Rosner B, Stark K, Kühne M
(2011). German outbreak of Escherichia coli O104:H4 associated with sprouts. The New England
Journal of Medicine, 365(19):1763-1770.
[2] Askar M, Faber MS, Frank C, Bernard H, Gilsdorf A, Fruth A, Prager R, Höhle M, Suess T, Wadl M,
Krause G, Stark K, Werber D (2011). Update on the ongoing outbreak of haemolytic uraemic
syndrome due to Shiga toxin-producing Escherichia coli (STEC) serotype O104. Euro Surveillance,
16(22): 19883.
[3] Schrödle B, Held L (2011). A primer on disease mapping and ecological regression using INLA.
Computational Statistics, 26(2): 241-258.
[4] Rue H, Martino S, Chopin N (2009). Approximate Bayesian inference for latent Gaussian models
using integrated nested Laplace approximations. Journal of the Royal Statistical Society, Series B, 71:
319-392.
International Biometric Conference, Florence, ITALY, 6 – 11July 2014