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With a focus on learning, we employ a range of strategies to support innovation, collaboration across centers, and university-wide discussion and decision-making


Tenth Annual Grant Winners 2009-2010

Andres Malave, Ph.D.

Leanne Lai, Ph.D.

Title: Sociodemographic Patterns of Insomnia Drug Prescriptions in the United States


Grant Winners 2009-2010

Need/Background: Insomnia is currently the most prevalent sleep disorder. Approximately 20% of adults examined in a general medical setting experience severe and persistent insomnia. While multiple alternative and FDA-approved pharmacological treatments are available for the treatment of insomnia, the majority of treatments evidence off-label prescription use: the antidepressant trazodone is reportedly the most frequently prescribed drug for insomnia, and accounts for approximately 20% of the insomnia market total prescription share. However, concern over the safety and inappropriateness of such non FDA-approved prescriptions warrants further investigation into physician prescribing patterns.

Objective: Our goal is to identify prescribing patterns for insomnia treatment in US outpatient settings, with a particular emphasis on physician and patient sociodemographics.

Methods and Analysis: This project proposes a secondary data analysis using a nationally representative database. Data will be extracted from the newly released National Ambulatory Medical Care Survey (NAMCS) from the US Department of Health and Human Services.

Inclusion criteria for insomnia drugs will be established according to the most recent American Insomnia Association reports regarding insomnia drug treatment. Population-based descriptive analyses will be used to estimate the national weighted frequencies of selected insomnia drug prescriptions. Eisenberg’s sociologic theory will be adopted to identify potential predictor variables (e.g., age, race, gender, physician specialty, office setting, office ownership, insurance coverage, metropolitan residence). We will further construct a weighted logistic regression model to estimate the odds and marginal probabilities of covariates that may have statistically significant contributions toward predicting insomnia prescribing patterns.

Significance of study and expected results: This study expands upon existing literature by applying a specified measure of insomnia to a national, population-based sample. Our focus on insomnia prescriptions and their associated physician characteristics and patient demographics will provide much-needed insight into the nature of pharmacological insomnia treatment and regulatory policies regarding the pharmacological treatment of insomnia.