In disease mapping, fine-resolution spatial health data are routinely aggregated for various reasons, for example to protect privacy. Usually, such aggregation occurs only once, resulting in ‘single-aggregation disease maps’ whose representation of the underlying data depends on the chosen set of aggregation units. This dependence is described by the modifiable areal unit problem (MAUP). Despite an extensive literature, in practice, the MAUP is rarely acknowledged, including in disease mapping. Further, despite single-aggregation disease maps being widely relied upon to guide distribution of healthcare resources, potential inefficiencies arising due to the impact of the MAUP on such maps have not previously been investigated. We introduce the overlay aggregation method (OAM) for disease mapping. This method avoids dependence on any single set of aggregate-level mapping units through incorporating information from many different sets. We characterise OAM as a novel smoothing technique and show how its use results in potentially dramatic improvements in resource allocation efficiency over single-aggregation maps. We demonstrate these findings in a simulation context and through applying OAM to a real-world dataset: ischaemic stroke hospital admissions in Perth, Western Australia, in 2016. The ongoing, widespread lack of acknowledgement of the MAUP in disease mapping suggests that unawareness of its impact is extensive or that impact is underestimated. Routine implementation of OAM can help avoid resource allocation inefficiencies associated with this phenomenon. Our findings have immediate worldwide implications wherever single-aggregation disease maps are used to guide health policy planning and service delivery.

中文翻译：

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克服由于可修改的面积单位问题对单聚集疾病图的影响而导致的效率低下

在疾病制图中，出于各种原因，例如为了保护隐私，通常会汇总高分辨率的空间健康数据。通常，此类聚合仅发生一次，从而导致“单一聚合疾病图”，其基础数据的表示取决于所选的聚合单元集。这种依赖性由可修改的面积单位问题（MAUP）描述。尽管有大量文献，但在实践中，MAUP很少得到承认，包括在疾病作图中。此外，尽管广泛使用单一聚集疾病图来指导医疗资源的分配，但先前尚未研究过由于MAUP对此类图的影响而导致的潜在效率低下。我们介绍了用于疾病映射的叠加聚合方法（OAM）。通过合并来自许多不同集合的信息，此方法避免了对聚合级别映射单元的任何单个集合的依赖。我们将OAM表征为一种新颖的平滑技术，并展示了OAM的使用如何导致单聚合图潜在地显着提高资源分配效率。我们在模拟环境中并通过将OAM应用于现实世界数据集来证明这些发现：2016年在西澳大利亚州珀斯的缺血性中风医院入院。疾病谱图中对MAUP的认识持续缺乏，这表明对它的认识不足影响广泛或影响被低估。OAM的常规实施可以帮助避免与此现象相关的资源分配效率低下的情况。