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Using data envelopment analysis and the bootstrap method to evaluate organ transplantation efficiency in Brazil

机译:使用数据包络分析和 bootstrap 方法评估巴西的器官移植效率

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摘要

Brazil has the most extensive public program for organ transplantation in the world, and the Brazilian National Health System (SUS) provides full coverage of all costs involved in organ donation, transplants, and post-transplant. Despite the relevance of the subject and the shortage of organs for transplants, transplantation process efficiency assessments are still uncommon in Brazil and abroad. This study aims to evaluate the efficiency of the Brazilian states and the Federal District in transforming potential organ donors into actual donations. We applied data envelopment analysis (DEA) in conjunction with the bootstrap technique, using organ transplantation data from 2018. The bootstrap methods applied (bootstrap technique, the bootstrap-biased scores of efficiency, and the bootstrap bias-corrected scores of efficiency) allow to obtain a confidence interval for DEA scores and provide greater robustness to studies based on DEA methodology. The bootstrap bias-corrected model indicates that there is significant room for improvement in terms of converting potential donors into actual donors. The mean corrected score is 0.55, signalizing that altogether the Brazilian states could maximize in 45 the number of transplanted organs without necessarily increasing the pool of potential donors. The study provides insights into the Brazilian processes of organ donation and transplantation, helping to identify locations in need of resource allocation improvements. Given the scarcity of studies with a joint application of DEA and bootstrap techniques in this crucial health activity, we also intend to methodologically contribute to this type of benchmark analysis, emphasizing the importance of considering measurement errors, randomness, and bias at DEA models.
机译:巴西拥有世界上最广泛的器官移植公共计划,巴西国家卫生系统 (SUS) 全面涵盖器官捐献、移植和移植后所涉及的所有费用。尽管该主题具有相关性并且移植器官短缺,但移植过程效率评估在巴西和国外仍然不常见。本研究旨在评估巴西各州和联邦区在将潜在器官捐献者转化为实际捐献方面的效率。我们使用 2018 年的器官移植数据,将数据包络分析 (DEA) 与 bootstrap 技术相结合。应用的 bootstrap 方法(bootstrap 技术、bootstrap 偏向效率分数和 bootstrap 偏差校正效率分数)允许获得 DEA 分数的置信区间,并为基于 DEA 方法的研究提供更大的稳健性。引导偏差校正模型表明,在将潜在捐赠者转化为实际捐赠者方面,还有很大的改进空间。平均校正分数为0.55,表明巴西各州可以在不增加潜在捐赠者数量的情况下,将移植器官的数量最大化45%。该研究提供了对巴西器官捐献和移植过程的见解,有助于确定需要改进资源分配的地点。鉴于在这项关键的健康活动中联合应用DEA和bootstrap技术的研究稀缺,我们还打算在方法论上为这种类型的基准分析做出贡献,强调在DEA模型中考虑测量误差、随机性和偏差的重要性。

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