Document Type

Master's Culminating Experience

Publication Date

2019

Abstract

Depression is the sixth most costly health condition in the United States, and depression that does not respond to its first trial of antidepressant treatment adds an annual cost of $9,529 per person per year. Thus, choosing an effective starting antidepressant can decrease the overall cost of depression to society. A secondary analysis of data from the Collaborative Psychiatric Epidemiology Survey (CPES) was performed to create models that can predict the efficacy of second-generation antidepressants in treating sadness. Two sets of Principal Component Analyses (PCAs) and logistic regressions were performed on variables associated with patient demographics, clinical symptoms, past medical history, and current mental health treatments: the first set explored associations between symptom clusters and drug efficacy, and the second set created models to predict the efficacy of each of the seven antidepressants. This study found that when treating sadness, paroxetine and venlafaxine should be avoided in depression with low moods, fluoxetine should be avoided in depression with high anxiety, and sertraline should be avoided in depression with high levels of fatigue. In addition, the models created to predict drug efficacy had a mean accuracy of 84% and internal validity of 62%. Since fewer than 50% of patients currently respond to their first antidepressant, the use of this model could provide a modest improvement to choosing efficacious starting antidepressants, subsequently decreasing the total disease burden depression poses onto society.

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License.

Additional Files

Lin_Poster-Final.pdf (447 kB)
mph-lin-poster


Included in

Public Health Commons

Share

COinS