  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.
