Off-label prescription of drugs is a routine practice among physicians. The Food and Drug Administration (FDA) approves new drugs based on their effectiveness for a particular indication, but once a drug enters the market, physicians can prescribe drugs for other indications that they deem appropriate. Sometimes, such prescribing is based on rigorous randomized trial data that has not (yet) led to formal FDA approval for a given indication;other off-label use is based on less well-established evidence. Because of concerns about cost and/or quality of care, payors in both the private and public sectors have sought to prohibit off-label use, limit its coverage, and/or require patients to assume an increasing fraction of its cost. Off-label drug use thus involves prescriptions that may or may not be sound therapy, that may involve unnecessary risk to patients, and that may be cost-effective or cost-ineffective-or a mix of all three. Though off-label use has been estimated to account for nearly three-quarters of the use of some drugs, the practice remains poorly understood. Few studies have detailed extent to which newly approved drugs are prescribed for off-label uses, or the evidence supporting such utilization. In addition, scant data exist regarding the patient or physician or drug characteristics that predict when an off-label use is more likely to occur. Though promotion of drugs for off-label uses is strictly regulated, there have been several recent high-profile instances of violation of these regulations. No studies have systematically evaluated the impact of marketing or other legal, regulatory, or financial forces on the practice of off-label drug use. We propose to develop a comprehensive scheme for studying off-label drug use to categorize the different types of off-label use and their clinical, economic, and policy implications. We will apply this typology to evaluate the frequency and patterns of off-label drug use in three important categories: oncology drugs, neuropsychiatric drugs, and drugs for other rare diseases. We will identify target drugs and review the medical literature, as well as expert opinion, regarding specific uses of each product to define the quality of evidence supporting each off-label use. We will then evaluate the characteristics of off-label drug use in large population databases of prescriptions and diagnoses, including Medicaid patients, Medicare patients, and those covered by a large health insurer. Using these datasets, we will use multivariable regression to determine predictors of use of specific off-label drugs for particular purposes, and identify characteristics of patients, physicians, and medications that are associated with off-label use. Finally, we will use time-trend analysis to determine the impact of changes in legal, regulatory, or financial factors that impact off-label use. A better understanding of the properties and predictors of off-label use can inform evidence-based prescribing of these products. Studying the benefit-risk-cost relationships associated with such uses can lead to more enlightened approaches to prescribing and policy decisions in this increasingly contentious area.

Public Health Relevance

Many physicians prescribe drugs for non-FDA-approved reasons (off-label uses), but when this practice occurs without sufficient evidence basis, it raises important health policy concerns about patient safety and cost-effectiveness. We propose to study the characteristics of off-label drug use in three select classes-cancer drugs, neuropsychiatry drugs, and drugs for other rare diseases. Using expert interviews and pharmaceutical claims data from a variety of large databases, we will identify the predictors and frequency of off- label use and determine how the practice is affected by certain external scientific, market, and regulatory events.

National Institute of Health (NIH)
Agency for Healthcare Research and Quality (AHRQ)
Clinical Investigator Award (CIA) (K08)
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HSR Health Care Research Training SS (HCRT)
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Anderson, Kay
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Brigham and Women's Hospital
United States
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