@article{Musio_Sauleau_Augustin_2012, title={Resources allocation in healthcare for cancer: a case study using generalised additive mixed models}, volume={7}, url={https://www.geospatialhealth.net/gh/article/view/107}, DOI={10.4081/gh.2012.107}, abstractNote={Our aim is to develop a method for helping resources re-allocation in healthcare linked to cancer, in order to replan the allocation of providers. Ageing of the population has a considerable impact on the use of health resources because aged people require more specialised medical care due notably to cancer. We propose a method useful to monitor changes of cancer incidence in space and time taking into account two age categories, according to healthcar general organisation. We use generalised additive mixed models with a Poisson response, according to the methodology presented in Wood, Generalised additive models: an introduction with R. Chapman and Hall/CRC, 2006. Besides one-dimensional smooth functions accounting for non-linear effects of covariates, the space-time interaction can be modelled using scale invariant smoothers. Incidence data collected by a general cancer registry between 1992 and 2007 in a specific area of France is studied. Our best model exhibits a strong increase of the incidence of cancer along time and an obvious spatial pattern for people more than 70 years with a higher incidence in the central band of the region. This is a strong argument for re-allocating resources for old people cancer care in this sub-region.}, number={1}, journal={Geospatial Health}, author={Musio, Monica and Sauleau, Erik A. and Augustin, Nicole H.}, year={2012}, month={Nov.}, pages={83–89} }