Overcoming inefficiencies arising due to the impact of the modifiable areal unit problem on single-aggregation disease maps

International Journal of Health Geographics

Berwin A. Turlach¹  Matthew Yap²  Alistair Vickery²  David Whyatt²  Mei Ruu Kok²  Matthew Tuson³  Kevin Murray⁴  Bryan Boruff⁵ 

[1] Department of Mathematics and Statistics, University of Western Australia, Perth, Australia;Medical School, University of Western Australia, Perth, Australia;Medical School, University of Western Australia, Perth, Australia;Department of Mathematics and Statistics, University of Western Australia, Perth, Australia;School of Population and Global Health, University of Western Australia, Perth, Australia;UWA School of Agriculture and Environment, University of Western Australia, Perth, Australia;Department of Geography, University of Western Australia, Perth, Australia;

关键词: Disease mapping;    Modifiable areal unit problem;    Single-aggregation disease maps;    Zonation-dependence;    Resource allocation efficiency;   

DOI  :  10.1186/s12942-020-00236-y

来源: Springer

【 摘 要 】

BackgroundIn 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.ResultsWe 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.ConclusionsThe 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.

【 授权许可】

CC BY