Exploring spatial patterns in the context of disease mapping is a decisive approach to bring evidence of geographical tendencies in assessing disease status and progression. In most cases, multiple count responses (corresponding to disease incidences of multiple types, such as cancer in men and women) are recorded at each spatial location, which may exhibit similar spatial patterns in addition to disease-specific patterns. These are typically modeled using multivariate shared component models, where the spatial (random) effects may be shared between the disease types to model their association. However, this framework is not immune to spatial confounding, where the latent correlation between the spatial random effects and the fixed effects often leads to misleading interpretation. A recent approach to attenuate spatial confounding is the “SPatial Orthogonal Centroid ‘K’orrection”, aka SPOCK, which displaces the geographical centroids, ensuring orthogonality of the spatial random effects and the fixed effects. In this paper, we introduce MSPOCK, or Multiple SPOCK, to tackle spatial confounding for the multiple counts scenario. The methodology is evaluated on synthetic data, and illustrated via an application to new cases of respiratory system cancer for men and women for the US state of California in 2016. Our studies show that the MSPOCK correction leads to a reduction of the posterior variance estimates of model parameters, while maintaining the interpretation of the model.

在疾病制图的背景下探索空间格局是一种决定性的方法，可以为评估疾病状况和进展提供地理趋势的证据。在大多数情况下，在每个空间位置都记录了多个计数响应（对应于多种类型的疾病发生率，例如男性和女性的癌症），除了疾病特定的模式外，还可能显示相似的空间模式。这些通常使用多元共享成分模型进行建模，其中空间（随机）效应可在疾病类型之间共享以建模其关联。但是，这种框架不能避免空间混淆，因为空间随机效应和固定效应之间的潜在相关性常常会导致误解。减少空间混淆的一种最新方法是“空间正交质心'K'矫正”，又名SPOCK，它置换了地理质心，从而确保了空间随机效应和固定效应的正交性。在本文中，我们介绍了MSPOCK或Multiple SPOCK，以解决多重计数方案中的空间混淆问题。该方法论是根据综合数据进行评估的，并通过在新案例中的应用进行了说明。2016年美国加利福尼亚州男性和女性呼吸道系统癌症。我们的研究表明，MSPOCK校正可减少模型参数的后验方差估计，同时保持对模型的解释。