Inferring sources of substandard and falsified products in pharmaceutical supply chains
Inferring sources of substandard and falsified products in pharmaceutical supply chains
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DOI：
10.1080/24725854.2023.2174277
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发表时间：
2023-02-23
影响因子：
2.6
通讯作者：
Pribluda，Victor
中科院分区：
文献类型：
--
作者：
Wickett，Eugene;Plumlee，Matthew;Pribluda，Victor
低收入和中等收入国家普遍存在的不合标准和伪造药品大大增加了发病率、死亡率和抗药性。监管机构通过在消费者购买产品时收集和检测样品，利用上市后监督来解决这一问题。现有的上市后监督数据分析工具主要关注阳性样本的位置。本文希望通过未充分利用的供应链信息来扩展这种分析，以提供对不合格和伪造产品来源的推断。我们首先建立的不可识别性问题的存在时，将此供应链信息与监控数据。然后，我们开发了一种贝叶斯方法来评估不合格和伪造的来源，从供应链信息中提取效用，并在考虑多个不确定性来源的同时减轻不可识别性。使用去识别的监测数据，我们表明所提出的方法是有效的，提供有价值的推理。
Substandard and falsified pharmaceuticals, prevalent in low- and middle-income countries, substantially increase levels of morbidity, mortality and drug resistance. Regulatory agencies combat this problem using post-market surveillance by collecting and testing samples where consumers purchase products. Existing analysis tools for post-market surveillance data focus attention on the locations of positive samples. This article looks to expand such analysis through underutilized supply-chain information to provide inference on sources of substandard and falsified products. We first establish the presence of unidentifiability issues when integrating this supply-chain information with surveillance data. We then develop a Bayesian methodology for evaluating substandard and falsified sources that extracts utility from supply-chain information and mitigates unidentifiability while accounting for multiple sources of uncertainty. Using de-identified surveillance data, we show the proposed methodology to be effective in providing valuable inference.