Data on neighbourhood characteristics are not typically collected in epidemiological studies. They are however useful in the study of small-area health inequalities. Neighbourhood characteristics are collected in some surveys and could be linked to the data of other studies. We propose to use kriging based on semi-variogram models to predict values at non-observed locations with the aim of constructing bespoke indices of neighbourhood characteristics to be linked to data from epidemiological studies. We perform a simulation study to assess the feasibility of the method as well as a case study using data from the RECORD study. Apart from having enough observed data at small distances to the non-observed locations, a good fitting semi-variogram, a larger range and the absence of nugget effects for the semi-variogram models are factors leading to a higher reliability.

翻译：流行病学研究通常不收集邻里特征数据，但这些数据在局部区域健康不平等的研究中具有重要价值。部分调查中收集的邻里特征数据可与其他研究的数据关联。我们提出基于半变异函数模型的克里金法，通过预测未观测位置的数值，构建可关联至流行病学数据的定制化邻里特征指数。通过模拟研究评估该方法的可行性，并利用RECORD研究数据进行案例验证。除需在未观测位置的近距离范围内拥有充足观测数据外，合适的半变异函数拟合、较大的变程范围以及避免模型中的块金效应，是提高预测可靠性的关键因素。