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Social Network Influence on New Drug Diffusion: Can the Data-driven Approach Provide Practical Benefits?

Lublóy, Ágnes and Keresztúri, Judit Lilla and Benedek, Gábor (2018) Social Network Influence on New Drug Diffusion: Can the Data-driven Approach Provide Practical Benefits? Society and Economy, 40 (2). pp. 227-243. DOI https://doi.org/10.1556/204.2018.40.2.4

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Official URL: https://doi.org/10.1556/204.2018.40.2.4


Abstract

This article studies the determinants of pharmaceutical innovation diffusion among specialists. To this end, it investigates the influences of six categories of factors—social embeddedness, socio-demography, scientific orientation, prescribing patterns, practice characteristics, and patient panel composition—on the use of 11 new drugs for the treatment of type 2 diabetes mellitus in Hungary. The Cox proportional hazards model identifies three determinants—social contagion (in the social embeddedness category) and prescribing portfolio and insulin prescribing ratio (in the prescribing pattern category). First, social contagion has a positive effect among geographically close colleagues—the higher the adoption ratio, the higher the likelihood of early adoption—but no influence among former classmates and scientific collaborators. Second, the wider the prescribing portfolio, the earlier the new drug uptake. Third, the lower the insulin prescribing ratio, the earlier the new drug uptake—physicians’ therapeutic convictions and patients’ socioeconomic statuses act as underlying influencers. However, this finding does not extend to opinion-leading physicians such as scientific leaders and hospital department and outpatient center managers. This article concludes by arguing that healthcare policy strategists and pharmaceutical companies may rely exclusively on practice location and prescription data to perfect interventions and optimize budgets.

Item Type:Article
Uncontrolled Keywords:pharmaceutical innovations, Cox proportional hazards model, diffusion, social contagion, pharmaceutical innovations
JEL classification:C14 - Semiparametric and Nonparametric Methods: General
C34 - Multiple or Simultaneous Equation Models: Truncated and Censored Models; Switching Regression Models
I19 - Health: Other
Subjects:Knowledge economy, innovation
Social welfare, insurance, health care
Projects:1783-3/2018/FEKUTSTRAT
DOI:https://doi.org/10.1556/204.2018.40.2.4
ID Code:3556
Deposited By: Ádám Hoffmann
Deposited On:19 Jun 2018 15:02
Last Modified:19 Jun 2018 15:02

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