Inferring sources of substandard and falsified products in pharmaceutical supply chains
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866910807592271872 |
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| author | Wickett, Eugene Plumlee, Matthew Smilowitz, Karen Phanouvong, Souly Pribluda, Victor |
| author_facet | Wickett, Eugene Plumlee, Matthew Smilowitz, Karen Phanouvong, Souly Pribluda, Victor |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2207_05671 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Inferring sources of substandard and falsified products in pharmaceutical supply chains Wickett, Eugene Plumlee, Matthew Smilowitz, Karen Phanouvong, Souly Pribluda, Victor Applications 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. |
| title | Inferring sources of substandard and falsified products in pharmaceutical supply chains |
| topic | Applications |
| url | https://arxiv.org/abs/2207.05671 |