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.

翻译：劣质及伪造药品在中低收入国家普遍存在，显著增加了发病率、死亡率和耐药性水平。监管机构通过上市后监测应对这一问题，即在消费者购药环节收集并检测样品。现有上市后监测数据分析工具主要关注阳性样本的地理位置。本文旨在利用未被充分开发的供应链信息扩展此类分析，以推断劣质及伪造产品的来源。我们首先发现，将供应链信息与监测数据整合时存在不可识别性问题。随后开发了一种贝叶斯方法，该方法能从供应链信息中提取效用，缓解不可识别性问题，并同时考虑多重不确定性来源。基于去标识化的监测数据，我们证明了所提出方法能有效提供有价值的推断结论。