Off-Label Antidepressant Use in Treatment of Insomnia

Grant Winners

Deans

Abstract

Need/Background: Off-label drug use attributed to 21% of prescriptions written annually. Of all off-label drug use, antidepressants use in insomnia is one of the most common practices. Antidepressants such as trazodone, amitriptyline, and mirtazapine were ranked 1, 3, and 4 respectively in the most prescribed hypnotics. Current FDAapproved prescription medications for insomnia include: five benzodiazepines, three non-benzodiazepine, and one selective-melatonin receptor agonist. Despite the availability of these drugs, studies consistently show a rise in offlabel prescriptions like antidepressants, antipsychotics, and anticonvulsants in the treatment of insomnia. Physicians as well as patients prefer antidepressants over hypnotics because of cost and formulary considerations. However, there is growing concern that off-label use of antidepressants may be more risky due to lack of safety and efficacy data.

Objective: The study objective is to compare insomnia prescribing patterns between off-label antidepressants versus FDAapproved hypnotics, with particular emphasis on market-driven patient and physician socioeconomic characteristics.

Methods and Analyses: The study design will be a retrospective study using National Ambulatory Medical Care Survey data from National Center for Health Statistics of the Centers for Disease Control. We will first perform population-based descriptive analyses to estimate national weighted frequency of each drug. Second, bivariate analyses will be used to compare the selected variables associated with insomnia prescribing patterns between off label antidepressants and FDAapproved hypnotics, using weight-adjusted Chi-Square and logistic regression. Significance of the study: Literature has showed that off-label use is widespread but often not supported by strong evidence. To address this concern, our study will investigate the prevalence of off-label antidepressants drug use in insomnia from national representative database. It will also identify patient and physician characteristics associated with off-label antidepressant prescribing. Ultimately, these results will provide valuable empirical evidence for future regulation and education of medical practice.