Deprivation and mortality: the implications of spatial autocorrelation for health resources allocation.

This paper aims at investigating whether the relationship between mortality and socio-economic deprivation is affected by the spatial autocorrelation of ecological data. A simple model is used in which mortality (all-ages and premature) is the dependent variable, and deprivation, morbidity and other socio-economic indicators are the explanatory variables. Deprivation is measured by the Townsend index; the other socio-economic variables are the median income, unequal income distribution (Gini coefficient) and population density. Morbidity is estimated on the basis of hospital admission rates and overweight prevalence. Spatial autocorrelation is measured by the Moran's I coefficient. All mortality and morbidity variables have significant, positive, and moderate-to-high spatial autocorrelation. Two multivariate models are explored: a weighted least-squares model ignoring spatial autocorrelation and a simultaneous autoregressive model. The paper concludes that spatial autocorrelation has a significant impact on the relationship between mortality and socio-economic variables. Future ecological models intended to inform health resources allocation need to pay greater attention to the spatial dimension of the data used.