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Related Experiment Videos

Managing Illicit Online Pharmacies: Web Analytics and Predictive Models Study.

Hui Zhao1, Sowmyasri Muthupandi2, Soundar Kumara3

  • 1Smeal College of Business, Pennsylvania State University, University Park, PA, United States.

Journal of Medical Internet Research
|August 26, 2020
PubMed
Summary
This summary is machine-generated.

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This study introduces novel prediction models to identify illicit online pharmacies (IOPs) using website referral links, achieving over 95% accuracy. These tools help combat the growing threat of unregulated online drug sales and protect patient safety.

Area of Science:

  • Pharmacology and Toxicology
  • Health Informatics
  • Cybersecurity

Background:

  • The global online pharmacy market is rapidly expanding, presenting convenience but also risks from illicit online pharmacies (IOPs).
  • IOPs facilitate access to prescription drugs, controlled substances, and counterfeit medications, jeopardizing the drug supply chain and public health.
  • Identifying and monitoring the vast and dynamic landscape of IOPs remains a significant challenge.

Purpose of the Study:

  • To enhance understanding of IOPs through web traffic data analysis.
  • To propose and validate a novel framework for predicting and identifying IOPs using website referral links.

Main Methods:

  • Collected and analyzed web traffic and engagement data for both legitimate online pharmacies (LOPs) and IOPs.
Keywords:
classificationillicit online pharmaciesonline pharmacyonline traffic analysisweb analytics

Related Experiment Videos

  • Developed two prediction models: Reference Rating Prediction Method (RRPM) and Reference-based K-nearest Neighbor (R2NN), utilizing referral link data.
  • Tested model performance on a dataset of 763 online pharmacies and applied them to search results for popular medications.

    • Main Results:

    • Direct traffic (URL entry) constitutes a significant portion (34.32%) of access to IOPs.
    • Both RRPM and R2NN models demonstrated high predictive accuracy (above 95%) for classifying online pharmacies.
    • R2NN achieved superior performance across key metrics, and both models showed low error rates when applied to search engine results for controlled substances.

    Conclusions:

    • The developed prediction models effectively leverage referral link data to identify IOPs, addressing a critical knowledge gap.
    • These models offer versatile applications for various stakeholders, including patients, policymakers, and drug manufacturers, in combating illicit online pharmacies.
    • The research provides a foundation for future work in drug safety and contributes to addressing the opioid crisis.