Overcoming inefficiencies arising due to the impact of the modifiable areal unit problem on single-aggregation disease m
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International Journal of Health Geographics Open Access
METHODOLOGY
Overcoming inefficiencies arising due to the impact of the modifiable areal unit problem on single‑aggregation disease maps Matthew Tuson1,2, Matthew Yap1, Mei Ruu Kok1, Bryan Boruff3,4, Kevin Murray5, Alistair Vickery1, Berwin A. Turlach2 and David Whyatt1*
Abstract Background: 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. Results: 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. Conclusions: 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. Keywords: Disease mapping, Modifiable areal unit problem, Single-aggregation disease maps, Zonationdependence, Resource allocation efficiency Background The practice of disease mapping is fundamental to public health [1], with disease maps currently being produced by healthcare organisations worldwide, including the World Health Organisation [2].
*Correspondence: [email protected] 1 Medical School, University of Western Australia, Perth, Australia Full list of author information is available at the end of the article
A fundamental function of a disease map is to guide geographically-prioritised resource allocation. As such, disease maps have been used to guide the spatial targeting of interventions to address HIV, cholera, Ebola, and malaria [3–11], for example. Disease maps have also been used to guide geographically-targeted responses to the current global COVID-19 pandemic. In Italy, for example, mass COVID-19 testing in the t
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