Noise Filtering Cancer Mortality Data for a Better Assessment of Health-Environment Relationships: Application to the Picardy RegionMahdi-Salim Saib1,2*, Julien Caudeville1, Florence Carre1, Olivier Ganry3, Alain Trugeon4 and Andre Cicolella1
- *Corresponding Author:
- Mahdi-Salim. Saib
French National Institute for Industrial Environment and Risks
Parc Technologique Alata, BP 2, 60550 Verneuilen- Halatte, France
E-mail: [email protected]
Received date: May 14, 2014; Accepted date: July 09, 2014; Published date: July 14, 2014
Citation: Saib MS, Caudeville J, Carre F, Ganry O, Trugeon A, et al. (2014) Noise Filtering Cancer Mortality Data for a Better Assessment of Health-Environment Relationships: Application to the Picardy Region. J Biomet Biostat 5:200. doi: 10.4172/2155-6180.1000200
Copyright: © 2014 Saib MS, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are are credited.
Cancer is one of the leading causes of mortality. However, it is necessary to analyze this disease from different perspectives. Cancer mortality maps are used by public health officials to identify areas of excess and to guide surveillance and control activities. However, the interpretation of these maps is difficult due to the presence of extremely unreliable rates, which typically occur for sparsely populated areas and/or less frequent cancers. The analysis of the relationships between health data and risk factors is often hindered by the fact that these variables are frequently assessed at different geographical scales. Geostatistical techniques that have enabled the process of filtering noise from the maps of cancer mortality and estimating the risk at different scales were recently developed. This paper presents the application of Poisson kriging for the examination of the spatial distribution of cancer mortality in the "Picardy region, France". The aim of this study is to incorporate the size and shape of administrative units as well as the population density into the filtering of noisy mortality rates and to estimate the corresponding risk at a fine resolution.