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Despite advances in sanitation and immunization, vaccine-preventable diseases remain a significant cause of morbidity and mortality worldwide. In high-income countries such as the United States, coverage rates for vaccination against childhood infections remains high. However, the phenomenon of vaccine hesitancy makes maintenance of herd immunity difficult, impeding global disease eradication efforts. Reaching the ‘last mile’ will require early detection of vaccine hesitancy (driven by philosophical or religious choices), identifying pockets of susceptibility due to underimmunization (driven by vaccine unavailability, costs ineligibility), determining the factors associated with the behavior and developing targeted strategies to ameliorate the concerns. Towards this goal, we harness high-resolution medical claims data to geographically localize vaccine refusal and underimmunization (collectively, ‘underutilization’) in the United States and identify the socio-economic determinants of the behaviors. Our study represents the first large-scale effort for vaccination behavior surveillance and has the potential to aid in the development of targeted public health strategies for optimizing vaccine uptake.

PurposeTobler's law of geography states that things that are close to one another tend to be more alike than things that are far apart. In this regard, the spatial pattern of price distribution is defined by the arrangement of individual entities in space and the geographic relationships among them. The purpose of this paper is to provide emerging findings of research analysing the salient factors which impact on the sale price of residential properties using a spatial regression approach.Design/methodology/approachThe research develops and formulates a geographically weighted regression (GWR) model to incorporate residential sales transactions within the Belfast Metropolitan Area over the course of 2010. Transaction data were sourced from the University of Ulster House Price Index survey (2010, Q1‐Q4). The GWR approach was then evaluated relative to a standard hedonic model to determine the spatial heterogeneity of residential property price within the Belfast Metropolitan Area.FindingsThis investigation finds that the GWR technique provides increased accuracy in predicting marginal price estimates, in comparison with traditional hedonic modelling, within the Belfast housing market.Originality/valueThis study is one of only a few investigations of spatial house price variation applying the GWR methodology within the confines of a UK housing market. In this respect it enhances applied based knowledge and understanding of geographically weighted regression.