Analysis of spatial patterns of disease is a significant field of research. However, access to unit-level disease data can be difficult for privacy and other reasons. As a consequence, estimates of interest are often published at the small area level as disease maps. This motivates the development of methods for analysis of these ecological estimates directly. Such analyses can widen the scope of research by drawing more insights from published disease maps or atlases. The present study proposes a hierarchical Bayesian meta-analysis model that analyses the point and interval estimates from an online atlas. The proposed model is illustrated by modelling the published cancer incidence estimates available as part of the online Australian Cancer Atlas (ACA). The proposed model aims to reveal patterns of cancer incidence for the 20 cancers included in ACA in major cities, regional and remote areas. The model results are validated using the observed areal data created from unit-level data on cancer incidence in each of 2148 small areas. It is found that the meta-analysis models can generate similar patterns of cancer incidence based on urban/rural status of small areas compared with those already known or revealed by the analysis of observed data. The proposed approach can be generalized to other online disease maps and atlases.

疾病的空间格局分析是一个重要的研究领域。但是，由于隐私和其他原因，访问单位级疾病数据可能很困难。结果，感兴趣的估计通常在小区域级别作为疾病图发布。这激发了直接分析这些生态估算方法的发展。通过从已发布的疾病图谱或地图册中获取更多见解，此类分析可以扩大研究范围。本研究提出了一种分级贝叶斯元分析模型，该模型分析了在线地图集的点和区间估计。通过对已发布的癌症发病率估算值进行建模来说明该模型，该估算值可作为在线澳大利亚癌症图集（ACA）的一部分获得。提出的模型旨在揭示主要城市，区域和偏远地区ACA中包括的20种癌症的癌症发病率模式。使用从2148个小区域中每个区域的癌症发病率的单位水平数据创建的观测面积数据验证模型结果。已经发现，与通过观察数据分析已经知道或揭示的那些相比，基于小区域的城乡状况，荟萃分析模型可以生成相似的癌症发病模式。所提出的方法可以推广到其他在线疾病图谱和地图集。已经发现，与通过观察数据分析已经知道或揭示的那些相比，基于小区域的城乡状况，荟萃分析模型可以生成相似的癌症发病模式。所提出的方法可以推广到其他在线疾病图谱和地图集。已经发现，与通过观察数据分析已经知道或揭示的那些相比，基于小区域的城市/农村状况的荟萃分析模型可以生成相似的癌症发病模式。所提出的方法可以推广到其他在线疾病图谱和地图集。