Abstract
The association between cardiovascular disease and a pool of demographic and socioeconomic variables is analyzed, for a large Canadian city, by means of multivariate spatial regression analysis. The analysis suggests that the spatial dependence observed in the disease prevalence is driven by the spatial distribution of senior citizens. A spatially autoregressive specification on a pool of solely socio-economic variables produces a model whose main predictors are family status, income, and educational attainments. This model can provide an effective analytical tool to support policy decisions, because it identifies a set of socioeconomic, not simply demographic predictors of disease. These socio-economic variables can be targeted by social policies much more effectively than demographic variables. A further analytical step recombines the significant explanatory variables based on their spatial patterns. Thus the model is used to identify areas of social and economic concern, and to enable the initiation of specifically localized preventative health measures. Owing to its generality, the method can be applied to other conditions and to analyze multivariate relationships involving not only socioeconomic variables, but also environmental factors.
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