Studies have shown that stunting and wasting indicators are strongly correlated among children, with the potential of concurrently affecting their physical and cognitive development. However, the identification of subpopulations of children with varying risks of stunting and wasting could be valuable for targeted intervention. This work proposed a bivariate spatio-temporal mixture model within a Bayesian framework to describe the spatial behavior of subpopulations of the children within the wider population of children under five years of age in Nigeria. The model assumes that each sub-population follows a Gaussian distribution, and therefore, the overall population is modeled by combining Gaussian sub-spatial models probabilistically. Inferences were based on the Markov chain Monte Carlo algorithm, that draw samples from the joint posterior distribution. The model was applied to data from four waves of the Nigerian Demographic and Health Survey. We identified a significant negative correlation between stunting and wasting among subpopulations with a negative spatial correlation between the spatial patterns of both illnesses. The findings demonstrate varying risk factors between the subpopulations with an evidence of spatio-temporal disparity in the likelihood of stunting and wasting. The findings underscore the need for a comprehensive national intervention program with attention given to high-burden states in a manner that involves communities and subpopulations. The maps could serve as a valuable tool for intervention planning.

研究表明，儿童的发育迟缓和消瘦指标密切相关，有可能同时影响他们的身体和认知发展。然而，识别具有不同发育迟缓和消瘦风险的儿童亚群对于有针对性的干预可能很有价值。这项工作提出了贝叶斯框架内的双变量时空混合模型，以描述尼日利亚更广泛的五岁以下儿童群体中儿童亚群体的空间行为。该模型假设每个子总体服从高斯分布，因此，总体总体是通过概率上组合高斯子空间模型来建模的。推论基于马尔可夫链蒙特卡罗算法，该算法从联合后验分布中抽取样本。该模型应用于尼日利亚人口和健康调查四波的数据。我们发现亚人群中发育迟缓和消瘦之间存在显着的负相关性，两种疾病的空间模式之间存在负空间相关性。研究结果表明，亚人群之间存在不同的危险因素，并有证据表明发育迟缓和消瘦的可能性存在时空差异。研究结果强调需要制定一项全面的国家干预计划，并以社区和亚人群参与的方式关注高负担国家。这些地图可以作为干预计划的宝贵工具。