Resources allocation in healthcare for cancer: a case study using generalised additive mixed models

  • Monica Musio | Dipartimento di Matematica ed Informatica, Università di Cagliari, Cagliari, Italy.
  • Erik A. Sauleau Faculté de Médecine, Université de Strasbourg, Strasbourg; Registre du Cancer de Haut Rhin, Mulhouse, France.
  • Nicole H. Augustin Department of Mathematics, University of Bath, Bath, United Kingdom.


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.



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Original Articles
health policy, generalised additive mixed models, resources allocation, cancer incidence, space-time models.
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How to Cite
Musio, M., Sauleau, E. A., & Augustin, N. H. (2012). Resources allocation in healthcare for cancer: a case study using generalised additive mixed models. Geospatial Health, 7(1), 83-89.