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.
摘要：
针对医疗保健干预措施的地理目标区域(TR)的适当优先级对于确保有限医疗保健资源的有效分配至关重要。在划定TR时，两者都“瞄准效率”，即，干预投资的回报，和后勤因素，例如，TR的数量，很重要。然而,现有的划分TR的方法不成比例地优先考虑目标效率。为了解决这个问题，我们探索了在保护规划中发现的一种方法的实用性：软件Marxan和扩展，MinPatch(\'Marxan+MinPatch\'),与我们介绍的一种新方法相比：空间目标算法(STA)。使用模拟和现实世界的数据，我们展示了STA优于MarxanMinPatch的性能，无论是在目标效率方面，还是在充分考虑后勤因素方面。例如,通过设计,和Marxan+MinPatch不同,STA允许用户指定期望数量的TR。更广泛地说,我们发现,虽然Marxan+MinPatch确实考虑了后勤因素，它也有几个限制，包括,但不限于,应用两个单独的软件工具的要求，这是累赘。鉴于这些结果,我们建议可以合理地应用STA,以帮助防止由于使用现有方法的干预措施的针对性而导致的效率低下.
