%0 Journal Article
%T Elucidating the spatially varying relation between cervical cancer and socio-economic conditions in England
%A Edith MY Cheng
%A Peter M Atkinson
%A Arjan K Shahani
%J International Journal of Health Geographics
%D 2011
%I BioMed Central
%R 10.1186/1476-072x-10-51
%X A global (stationary) regression model revealed a significant correlation between cervical cancer incidence rates and social status. However, a local (non-stationary) GWPR model provided a better fit with less spatial correlation (positive autocorrelation) in the residuals. Moreover, the GWPR model was able to represent local variation in the relations between cervical cancer incidence and socio-economic covariates across space, whereas the global model represented only the overall (or average) relation for the whole of England. The global model could lead to misinterpretation of the relations between cervical cancer incidence and socio-economic covariates locally.Cervical cancer incidence was shown to have a non-stationary relationship with spatially varying covariates that are available through national datasets. As a result, it was shown that if low social status sectors of the population are to be targeted preferentially, this targeting should be done on a region-by-region basis such as to optimize health outcomes. While such a strategy may be difficult to implement in practice, the research does highlight the inequalities inherent in a uniform intervention approach.Regression is a well known statistical tool for exploring the relationship between target and explanatory variables [1]. Different types of regression models are used widely in ecological and disease research, for example, global regression modelling, multi-level modelling and Bayesian modelling for small area studies [2]. For example, regression has been used to explore the relations between limiting long-term illness, ethnicity and income in London [3]. However, global regression models are stationary in the parameters and, thus, geographical variation in the relations is ignored. Geographically weighted regression (GWR) is a well established technique that relaxes the stationarity decision implicit in global models, thereby allowing parameters to vary spatially [4-6]. This amounts to a non-station
%K Geographically weighted regression
%K cervical cancer
%K screening
%K disease mapping
%U http://www.ij-healthgeographics.com/content/10/1/51