Impact of the modifiable areal unit problem in assessing determinants of emergency department demand. (31st January 2021) - Kok, Mei Ruu; Tuson, Matthew; Yap, Matthew; Turlach, Berwin; Boruff, Bryan; Vickery, Alistair; Whyatt, David
Abstract: Objective: To examine the impact of the modifiable areal unit problem (MAUP) in an investigation of factors associated with ED demand in Perth, Western Australia, in 2016. Furthermore, to advocate a means of avoiding this impact. Methods: ED presentations were classified as: urgent medical, non‐urgent medical, urgent trauma or non‐urgent trauma. In each group, sex‐stratified, age‐adjusted multivariate associations with socio‐economic status and distance to the nearest ED and general practitioner (GP) were estimated. Modelling was undertaken using different sets of spatial units: Australian Bureau of Statistics (ABS) Statistical Areas Level 1 (SA1s) and numerous aggregate‐level zonations of SA1s (ABS SA2s and others). Results: Estimates obtained using the different units often varied widely: for seven (30%) of 24 strata defined by combinations of sex, ED type and covariate, the smallest and largest effect sizes differed in terms of direction; further, for 11 (65%) of the remaining 17 strata, the largest effect size was at least twice as high as the smallest. This demonstrates the MAUP's impact and that analyses based on a single set of spatial units are unreliable. To resolve the observed variation, we highlight the SA1‐level estimates. Conclusions: When formulating interventions targeting reduced ED utilisation, policy planners should be guided by evidence based on analysis of appropriate spatial units. This ideal is undermined by the widespread lack of acknowledgement of the MAUP in studies examining drivers of ED demand using spatially aggregated data. To avoid the MAUP, only estimates obtained through examining a minimal geographic unit should be relied upon. Abstract : Estimates obtained using the different units often varied widely, demonstrating the MAUP's impact and that results obtained using a single set of aggregated units are unreliable. To resolve this variation, we highlight the SA1‐level estimates.