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<p>Because of the methods by which they are produced, drugs embody technical knowledge and are the subject of technology transfer strategies among biopharmaceutical companies. Additionally, drug diffusion shapes the structure of the biopharmaceutical industry, the sustainability of pharmaceutical markets, and patient outcomes. This article provides a practical guide to using Medicare Part D Prescription Drug Event (PDE) data to model and analyze the factors and dynamics that drive drug diffusion. Medicare accounts for approximately a third of total U.S. prescription drug spending and covers more than 56&#xa0;million beneficiaries, making it the most representative dataset of the U.S. older adult population and people with disabilities. The PDE’s strengths include national representativeness, detailed transactional data, and the capacity for longitudinal and multilevel analyses. Its limitations include the absence of diagnostic detail, the reporting of list rather than net prices, and limited generalizability to non-Medicare populations. The complementary use of other data sources can mitigate these weaknesses. Hence, we describe linkage strategies that integrate PDE claims with the Medicare Beneficiary Summary File, the Prescriber Characteristics File, outpatient claims, and external data sources, including FDA approval data and U.S. Census indicators. Particular attention is given to handling many-to-many linkages and to constructing analytic datasets that preserve contextual validity. We demonstrate how researchers can evaluate the roles of patient demographics, comorbidities, prescriber attributes, and formulary design in shaping drug diffusion. We argue that Medicare Part D claims data provide a uniquely powerful platform for studying technology transfer and innovation diffusion in an industrial sector that accounts for 20% of the U.S. economy.</p>

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Factors influencing drug diffusion: a practical guide to the use of medicare part D claims

  • 期刊论文

  • 发表日期：

    2026年1月13日

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  • Yuchen Wang,
  • Giedre Kvedaraviciene,
  • Phillip Phan

摘要

Because of the methods by which they are produced, drugs embody technical knowledge and are the subject of technology transfer strategies among biopharmaceutical companies. Additionally, drug diffusion shapes the structure of the biopharmaceutical industry, the sustainability of pharmaceutical markets, and patient outcomes. This article provides a practical guide to using Medicare Part D Prescription Drug Event (PDE) data to model and analyze the factors and dynamics that drive drug diffusion. Medicare accounts for approximately a third of total U.S. prescription drug spending and covers more than 56 million beneficiaries, making it the most representative dataset of the U.S. older adult population and people with disabilities. The PDE’s strengths include national representativeness, detailed transactional data, and the capacity for longitudinal and multilevel analyses. Its limitations include the absence of diagnostic detail, the reporting of list rather than net prices, and limited generalizability to non-Medicare populations. The complementary use of other data sources can mitigate these weaknesses. Hence, we describe linkage strategies that integrate PDE claims with the Medicare Beneficiary Summary File, the Prescriber Characteristics File, outpatient claims, and external data sources, including FDA approval data and U.S. Census indicators. Particular attention is given to handling many-to-many linkages and to constructing analytic datasets that preserve contextual validity. We demonstrate how researchers can evaluate the roles of patient demographics, comorbidities, prescriber attributes, and formulary design in shaping drug diffusion. We argue that Medicare Part D claims data provide a uniquely powerful platform for studying technology transfer and innovation diffusion in an industrial sector that accounts for 20% of the U.S. economy.