ArticleScientific data2024
Tracing Opioids Across the US: A High-Resolution Pharmaceutical Distribution Dataset.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Article
- Adapting to disruptions: Managing supply chain resilience through product rerouting.Science advances · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the field of pharmaceutical supply chains, there is a lack of comprehensive historical data, representing a significant barrier to advancing research. To address this gap, we introduce a high-resolution dataset comprising drug packages distributed to approximately 300,000 pharmacies, hospitals, and practitioners across the US. We reconstruct 375 million distribution paths from ARCOS, a DEA-maintained database comprising half a billion shipping records between 2006 and 2014. While ARCOS tracks dyadic shipments, it does not provide information on the complete journey of single packages from manufacturers to final destinations. Our algorithm is able to reconstruct complete distribution paths from these dyadic records. The reconstructed dataset, with its high temporal and spatial resolution, offers an unprecedented view of US pharmaceutical distribution and is a valuable resource for investigating supply and distribution networks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.