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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.

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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.