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Improving the Efficiency of Geographic Target Regions for Healthcare Interventions.
Tuson, Matthew; Yap, Matthew; Kok, Mei Ruu; Boruff, Bryan; Murray, Kevin; Vickery, Alistair; Turlach, Berwin A; Whyatt, David.
  • Tuson M; Department of Mathematics and Statistics, The University of Western Australia, Crawley, WA 6009, Australia.
  • Yap M; Medical School, The University of Western Australia, Crawley, WA 6009, Australia.
  • Kok MR; Medical School, The University of Western Australia, Crawley, WA 6009, Australia.
  • Boruff B; UWA School of Agriculture and Environment, The University of Western Australia, Crawley, WA 6009, Australia.
  • Murray K; Department of Geography, The University of Western Australia, Crawley, WA 6009, Australia.
  • Vickery A; School of Population and Global Health, The University of Western Australia, Crawley, WA 6009, Australia.
  • Turlach BA; Medical School, The University of Western Australia, Crawley, WA 6009, Australia.
  • Whyatt D; Department of Mathematics and Statistics, The University of Western Australia, Crawley, WA 6009, Australia.
Int J Environ Res Public Health ; 19(22)2022 Nov 09.
Article in English | MEDLINE | ID: covidwho-2143082
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Conservation of Natural Resources / Health Facilities Type of study: Experimental Studies / Prognostic study Language: English Year: 2022 Document Type: Article Affiliation country: Ijerph192214721

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Conservation of Natural Resources / Health Facilities Type of study: Experimental Studies / Prognostic study Language: English Year: 2022 Document Type: Article Affiliation country: Ijerph192214721