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

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.

Comparing different data sources by examining the associations between surrounding greenspace and children's weight status

“…The MAUP is an issue in spatial analysis for studies in geography. It describes that built environment values may vary with the spatial scale for which data are available and the boundaries between spatial units [ 62 ]. For now, the MAUP is still rarely addressed in practice.…”

Investigation of Relationships Between the Geospatial Distribution of Cancer Incidence and Estimated Pesticide Use in the U.S. West

“…Although the selection of scale depends to a large extent on data availability (S.‐I. Lee et al., 2019), analyses conducted at different spatial scales often produce statistically different results (Manley et al., 2006; Tuson et al., 2020; Wang & Di, 2020). For instance, several environmental epidemiological studies have quantified the impact of the areal unit problem in incidence rate mapping (Nakaya, 2000) by quantifying associations between nitrite and respiratory health analysis (Parenteau & Sawada, 2011), geospatial mapping of cerebrovascular diseases (Ayubi & Safiri, 2018), air pollution and health effects (D. Lee et al., 2020), and geospatial analysis of environmental factors and COVID‐19 death cases (Wang & Di, 2020).…”

Predicting Future Geographic Hotspots of Potentially Preventable Hospitalisations Using All Subset Model Selection and Repeated K-Fold Cross-Validation

“…Future work should generalise our method to address the MAUP; specifically, its “scale” and “zonation” aspects, which describe, respectively, the dependence of a given analysis on the size and configuration of the chosen set of spatial boundaries. A recently proposed method that involves combining information across numerous zonations of fine-resolution data, in order to classify “zonation-independent” hotspots, could potentially be used for this purpose [ 75 ]. However, the choice of scale should also be carefully considered; this should be related to the scale of a proposed intervention [ 75 ].…”

“…A recently proposed method that involves combining information across numerous zonations of fine-resolution data, in order to classify “zonation-independent” hotspots, could potentially be used for this purpose [ 75 ]. However, the choice of scale should also be carefully considered; this should be related to the scale of a proposed intervention [ 75 ]. To facilitate these endeavours, and in order that users have sufficient control over scale, as fine resolution data as possible should be obtained in the first instance; where fine-resolution data exist, but are unavailable, their custodians should be lobbied for access to those data based on a need to address the MAUP.…”