论文标题
推断药物供应链中不合标准和伪造产品的来源
Inferring sources of substandard and falsified products in pharmaceutical supply chains
论文作者
论文摘要
在低收入和中等收入国家中普遍存在的不合格和伪造的药物，大大提高了发病率，死亡率和耐药性的水平。监管机构通过收集和测试消费者购买产品的样品来解决此问题。市场后监视数据的现有分析工具将注意力集中在阳性样品的位置上。本文旨在通过未充分利用的供应链信息扩展这种分析，以推断不合格和伪造产品的来源。我们首先在将此供应链信息与监视数据集成时，确定了无法识别性问题的存在。然后，我们开发了一种贝叶斯方法，用于评估不合标准和伪造的来源，该方法从供应链信息中提取效用，并减轻对多种不确定性来源的不可识别性。使用识别的监视数据，我们表明所提出的方法可以有效地提供有价值的推断。
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 paper 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.
