Health surveys allow exploring health indicators that are of great value from a public health point of view and that cannot normally be studied from regular health registries. These indicators are usually coded as ordinal variables and may depend on covariates associated with individuals. In this paper, we propose a Bayesian individual-level model for small-area estimation of survey-based health indicators. A categorical likelihood is used at the first level of the model hierarchy to describe the ordinal data, and spatial dependence among small areas is taken into account by using a conditional autoregressive (CAR) distribution. Post-stratification of the results of the proposed individual-level model allows extrapolating the results to any administrative areal division, even for small areas. We apply this methodology to the analysis of the Health Survey of the Region of Valencia (Spain) of 2016 to describe the geographical distribution of a self-perceived health indicator of interest in this region.

翻译：健康调查能够揭示从公共卫生角度极具价值且通常无法通过常规健康登记数据获取的健康指标。这些指标通常编码为有序变量，并可能依赖于与个体相关的协变量。本文提出了一种用于调查类健康指标小区域估计的贝叶斯个体层面模型。模型层级的第一层采用分类似然函数描述有序数据，并通过条件自回归（CAR）分布考虑小区域间的空间依赖性。基于该个体层面模型结果的后分层方法，可将其推论扩展至任意行政区域划分（包括小区域）。我们将该方法应用于2016年西班牙巴伦西亚自治区健康调查分析，以描述该地区自报健康指标的地理分布特征。