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Tracing Opioids Across the US: A High-Resolution Pharmaceutical Distribution Dataset
Ambra Amico1, Luca Verginer2, Frank Schweitzer2
1Chair of Systems Design, ETH Zurich, Weinbergstrasse 56/58, 8092, Zurich, Switzerland. ambra.amico@smart.mit.edu.
Scientific Data
|July 13, 2024
Summary
Researchers created a high-resolution dataset of pharmaceutical distribution paths across the US. This novel dataset reconstructs 375 million drug package journeys, offering an unprecedented view of supply networks.
Area of Science:
- Pharmaceutical supply chain analysis
- Health informatics
- Network science
Background:
- Lack of comprehensive historical data hinders pharmaceutical supply chain research.
- Existing datasets like ARCOS (Automation of Reports and Collect of Controlled Substances) provide dyadic shipment records but not complete package journeys.
- Significant barrier to advancing research in pharmaceutical distribution.
Purpose of the Study:
- To address the data gap in pharmaceutical supply chains.
- To introduce a high-resolution dataset of drug package distribution paths across the US.
- To enable investigation of supply and distribution networks.
Main Methods:
- Reconstruction of 375 million distribution paths using a novel algorithm.
- Utilized the DEA-maintained ARCOS database (half a billion shipping records, 2006-2014).
- Algorithm reconstructs complete package journeys from dyadic shipment records.
Main Results:
- Developed a high-resolution dataset of pharmaceutical distribution.
- Dataset covers drug packages distributed to ~300,000 US pharmacies, hospitals, and practitioners.
- Reconstructed paths offer unprecedented temporal and spatial resolution.
Conclusions:
- The reconstructed dataset is a valuable resource for pharmaceutical supply chain research.
- Offers an unprecedented view of US pharmaceutical distribution.
- Facilitates investigation into complex supply and distribution networks.
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