多个国家编辑的癌症图谱是分析癌症风险地理差异的主要资源。对于流行病学家来说，将观察到的空间模式与已知或假设的风险因素相关联是一项耗时的工作，他们需要分别处理每种癌症，根据性别和种族分解模式。最近的文献提出同时研究一种以上的癌症，寻找共同的空间危险因素。然而，这项先前的工作有两个限制：他们只考虑了极少数（2-4）种先前已知具有共同危险因素的癌症。在本文中，我们提出了一种探索性方法来寻找大量被认为不相关的癌症的潜在空间危险因素。该方法基于奇异值分解和非负矩阵分解，计算效率高，并且可以轻松地根据区域和癌症的数量进行扩展。我们进行了一项模拟研究来评估该方法的性能，并将其应用于来自美国、英国、法国、澳大利亚、西班牙和巴西的癌症图谱。我们得出的结论是，由于潜在地图非常少（可以减少多达 90% 的地图集地图），因此大部分空间变异性都是保守的。通过集中对这几个潜在图谱进行流行病学分析，可以节省大量工作，同时可以得出同时影响多种癌症的高级解释。© 2024 作者。约翰·威利出版的《医学统计》

The cancer atlas edited by several countries is the main resource for the analysis of the geographic variation of cancer risk. Correlating the observed spatial patterns with known or hypothesized risk factors is time-consuming work for epidemiologists who need to deal with each cancer separately, breaking down the patterns according to sex and race. The recent literature has proposed to study more than one cancer simultaneously looking for common spatial risk factors. However, this previous work has two constraints: they consider only a very small (2-4) number of cancers previously known to share risk factors. In this article, we propose an exploratory method to search for latent spatial risk factors of a large number of supposedly unrelated cancers. The method is based on the singular value decomposition and nonnegative matrix factorization, it is computationally efficient, scaling easily with the number of regions and cancers. We carried out a simulation study to evaluate the method's performance and apply it to cancer atlas from the USA, England, France, Australia, Spain, and Brazil. We conclude that with very few latent maps, which can represent a reduction of up to 90% of atlas maps, most of the spatial variability is conserved. By concentrating on the epidemiological analysis of these few latent maps a substantial amount of work is saved and, at the same time, high-level explanations affecting many cancers simultaneously can be reached.© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.