title: Elucidating the spatially varying relation between cervical cancer and socio-economic conditions in England.
abstract: BACKGROUND
  Geographically weighted Poisson regression (GWPR) was applied to the relation between cervical cancer disease incidence rates in England and socio-economic deprivation, social status and family structure covariates. Local parameters were estimated which describe the spatial variation in the relations between incidence and socio-economic covariates.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Centre for Geographical Health Research, Geography and Environment, University of Southampton, Highfield, Southampton, UK. m.y.cheng@soton.ac.uk
authors.forename: Edith M Y
authors.initials: EM
authors.lastname: Cheng
authors.forename: Peter M
authors.initials: PM
authors.lastname: Atkinson
authors.forename: Arjan K
authors.initials: AK
authors.lastname: Shahani
mesh_terms: D016000:Cluster Analysis; D004739:England / Q000453:epidemiology; D005260:Female; D040362:Geographic Information Systems; D006801:Humans; D015994:Incidence; D016012:Poisson Distribution; D011204:Poverty Areas; D012044:Regression Analysis; D012923:Social Class; D002583:Uterine Cervical Neoplasms / Q000453:epidemiology*