Social Networks, Physician Characteristics, and Inappropriate Prescribing of Commonly Misused Prescription Drugs

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PROJECT SUMMARY Opioid and benzodiazepine misuse and abuse has increased dramatically in recent years. Despite precipitous increases in overdoses and rising misuse, the role that physicians play in inappropriate prescribing remains understudied. However, 34% of individuals who misused prescription medications obtained them directly from a single doctor. Network-based models, which analyze how the structure of social relations shape norms and behaviors, have successfully been used to understand physician behavior and change physician practices. Applying network methods to inappropriate prescribing will move surveillance beyond patient-based algorithms to physician prescribing patterns and norms. This reorientation has the potential to help identify physicians who are at risk of inappropriate prescribing, understand how these tendencies correlate with their network positions, and design network-based interventions to reduce the spread of prescription drug abuse. In this study, we aim to: (1) Examine the association between social network position, physician characteristics, and different forms of inappropriate prescribing and co-prescribing; (2) Use network and behavior co-evolution models to analyze the role of social influence in the diffusion of inappropriate prescribing practices; (3) Compare the relative effectiveness of insurance network optimization to eliminate inappropriate prescribers, Prescription Drug Monitoring Programs, and insurance formulary restrictions. We will draw on prescriptions of benzodiazepines and opioids from IMS Health's LRx database between January 1, 2005 and December 31, 2016. The 2009 data covered 224,140,604 unique patients, 916,338 prescribers, encompassing 135 million opioid prescriptions and 46 million benzodiazepine prescriptions. We will augment the LRx data with Medicaid Managed Care claims from three states. Using IMS and Medicaid data, we will construct longitudinal physician referral networks. The proposed analyses using this data will provide greater insight into how social network position and social influence can be leveraged to combat inappropriate prescribing of controlled substances. Physicians are uniquely positioned to help end the prescription drug epidemic. Past research indicates that changing prescribing behavior will require interventions targeted at groups of physicians, rather than individuals, since prescribing norms are reinforced in communities of practice. Our study will strengthen efforts to better understand prescription drug abuse and lead to actionable recommendations that public and private stakeholders, as well as payers can take to reduce the rate of inappropriate prescribing through improved detection algorithms, more efficient targeting of policies and educational efforts, and new policies.