Improving the Efficiency of Geographic Target Regions for Healthcare Interventions

Matthew Tuson (), Matthew Yap, Mei Ruu Kok, Bryan Boruff, Kevin Murray, Alistair Vickery, Berwin A. Turlach and David Whyatt
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Matthew Tuson: Department of Mathematics and Statistics, The University of Western Australia, Crawley, WA 6009, Australia
Matthew Yap: Medical School, The University of Western Australia, Crawley, WA 6009, Australia
Mei Ruu Kok: Medical School, The University of Western Australia, Crawley, WA 6009, Australia
Bryan Boruff: UWA School of Agriculture and Environment, The University of Western Australia, Crawley, WA 6009, Australia
Kevin Murray: School of Population and Global Health, The University of Western Australia, Crawley, WA 6009, Australia
Alistair Vickery: Medical School, The University of Western Australia, Crawley, WA 6009, Australia
Berwin A. Turlach: Department of Mathematics and Statistics, The University of Western Australia, Crawley, WA 6009, Australia
David Whyatt: Medical School, The University of Western Australia, Crawley, WA 6009, Australia

IJERPH, 2022, vol. 19, issue 22, 1-22

Abstract: Appropriate prioritisation of geographic target regions (TRs) for healthcare interventions is critical to ensure the efficient distribution of finite healthcare resources. In delineating TRs, both ‘targeting efficiency’, i.e., the return on intervention investment, and logistical factors, e.g., the number of TRs, are important. However, existing approaches to delineate TRs disproportionately prioritise targeting efficiency. To address this, we explored the utility of a method found within conservation planning: the software Marxan and an extension, MinPatch (‘Marxan + MinPatch’), with comparison to a new method we introduce: the Spatial Targeting Algorithm (STA). Using both simulated and real-world data, we demonstrate superior performance of the STA over Marxan + MinPatch, both with respect to targeting efficiency and with respect to adequate consideration of logistical factors. For example, by design, and unlike Marxan + MinPatch, the STA allows for user-specification of a desired number of TRs. More broadly, we find that, while Marxan + MinPatch does consider logistical factors, it also suffers from several limitations, including, but not limited to, the requirement to apply two separate software tools, which is burdensome. Given these results, we suggest that the STA could reasonably be applied to help prevent inefficiencies arising due to targeting of interventions using currently available approaches.

Keywords: geographic target regions; healthcare interventions; targeting efficiency; logistical factors; Marxan; MinPatch; Spatial Targeting Algorithm (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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