Inferring sources of substandard and falsified products in pharmaceutical supply chains

TL;DR

This paper introduces a Bayesian approach to identify sources of substandard and falsified pharmaceuticals by integrating supply-chain data with surveillance results, addressing unidentifiability issues and improving inference accuracy.

Contribution

It develops a novel Bayesian methodology that leverages supply-chain information to infer sources of falsified drugs, overcoming unidentifiability challenges present in existing methods.

Findings

Method effectively infers sources using real surveillance data.

Supply-chain information enhances source identification accuracy.

Approach accounts for multiple uncertainties in the data.

Abstract

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.