Trends in testosterone prescription amongst medical specialties: a 5-year CMS data analysis.
Isabelle V CarterMichael J CallegariTarun K JellaAmr MahranThomas B CwalinaWade MunceyAram LoebNannan ThirumavalavanPublished in: International journal of impotence research (2022)
Testosterone Therapy (TTh) trends have changed as a result of clinical research and market forces over the past several years. Understanding the trends or preferences regarding testosterone prescriptions remains unknown. Our objective was to assess both regional and national trends in TTh prescriptions amongst medical specialties within the United States between 2013 and 2017. Publicly available data from the Center for Medicare and Medicaid Services (CMS) Part D Prescriber database with regards to TTh prescriptions across a 5-year span (January 1, 2013-December 31, 2017) were analyzed. TTh therapies were consolidated into four categories: Topical, Oral, Injection and Pellet. Statistical analysis utilizing R 4.0.2 was performed on the resulting data. Trends in prescription modality claim count and cost were plotted over the study period while statistical analysis evaluated associations between TTh modality and medical specialist. We found that Endocrinologists and Urologists prescribed topical testosterone more than all other specialties (60.4% and 53.5%, respectively), while Family and Internal medicine physicians were more likely to prescribe injections (59.82% and 50.69%, respectively). Oral and pellet testosterone were rarely prescribed across all specialties. In conclusion, the wide variation in modalities of testosterone prescriptions illustrates an opportunity for treatment guidelines to be streamlined across all specialists to improve patient outcomes.
Keyphrases
- replacement therapy
- data analysis
- healthcare
- primary care
- smoking cessation
- electronic health record
- health insurance
- mental health
- palliative care
- stem cells
- emergency department
- machine learning
- mesenchymal stem cells
- quality improvement
- clinical practice
- deep learning
- artificial intelligence
- cell therapy
- combination therapy