Ozempic Impact Analysis on Obesity and Hypertension

Importance

The Ozempic Impact Analysis on Obesity and Hypertension underscores the pressing need within the healthcare sector to evaluate the effectiveness of treatments for chronic conditions. This need arises from the growing prevalence of these conditions and the imperative to improve patient outcomes through personalized care. This evaluation informs healthcare providers about the potential benefits and limitations of Ozempic in managing obesity and hypertension, areas of growing concern globally. By attempting to evaluate Ozempic's impact, this project sheds light on the complexities of treatment responses among diverse patient demographics, guiding more nuanced clinical approaches. The value it brings lies in its contribution to evidence-based medicine, paving the way for improved therapeutic strategies and patient care in the face of uncertain treatment outcomes.

Overview

This project examines Ozempic's impact on obese and hypertensive patients (40-75 years), leveraging exploratory data analysis, data merging, and double-lasso techniques to address endogeneity and estimate treatment effects accurately. By analyzing medical claims and prescription data, we aim to reveal how Ozempic's effectiveness varies across patient demographics, providing critical insights into its use in clinical practices.

Methodology Overview

• Exploratory Data Analysis: Initial pattern observation and data preparation for integrity.
• Causal Analysis: Endogeneity resolution to ensure precise treatment effect estimation.
• Modeling & Evaluation: Implementing double-lasso methods to explore Ozempic's impacts and assessing model validity against real-world outcomes.

Contents

• OzempicImpact-Notebook.ipynb: Jupyter Notebook containing all code, visualizations, and written analysis.
• OzempicImpact-HTML.html: An HTML export of the Jupyter Notebook.
• OzempicImpact-ObesityHypertension-Report.pdf: The comprehensive report including insights and implications of findings.

Data Sources

• Medical Claims Data: Dataset encapsulating medical claims for services rendered to the specified patient group.
• Prescription Data: Concentrated subset of the Medical dataset detailing prescriptions dispensed.
• Access is exclusive to our project team, adhering to confidentiality and ethical data handling standards.

Credits

• Machine Learning professor Jörn Boehnke at the MSBA program of UC Davis, whose teachings have been instrumental in the execution of this project.
• Thanks to my classmates Colin Chen and Jessica Yang for their contributions.

License

The project is released under the "MIT License".