Lanting Yang, Ph.D., M.P.H.

Dr. Lanting Yang is an Assistant Professor in the Department of Pharmaceutical Outcomes and Policy at the University of Florida College of Pharmacy. She is a pharmacoepidemiologist whose research uses real-world data to advance medication safety and effectiveness, particularly among older adults and individuals with cardiovascular, metabolic, and other chronic conditions.

Dr. Yang earned her PhD in Pharmaceutical Outcomes and Policy Research and her MPH in Epidemiology from the University of Pittsburgh, as well as a BS in Clinical Pharmacy from Harbin Medical University. Before joining UF, she completed postdoctoral training at the University of California, San Diego.

Her research has been supported by the Skaggs Family Foundation and the American Diabetes Association. Additional information about her research interests and current areas of work is available in the Research Profile section. She welcomes opportunities to mentor students and collaborate with investigators across pharmacy, medicine, public health, data science, and environmental health.

Dr. Yang’s research integrates pharmacoepidemiology, environmental health, and data science to understand how medications, environmental exposures, and patient characteristics jointly influence health outcomes in older adults and people living with chronic conditions.

i. Medication safety and comparative effectiveness: Uses Medicare claims, commercial insurance claims, and electronic health records to study medication use, drug–drug interactions, and treatment outcomes among populations at elevated risk of polypharmacy and medication-related harm, including older adults and people with diabetes, cardiovascular disease, immunosuppressive conditions, and multiple chronic conditions.

ii. Drug–environment interactions: Examines how extreme heat, air pollution, and other environmental exposures may alter medication-related risks and health outcomes, with a focus on people with diabetes, cardiovascular disease, multiple chronic conditions, and other medically or socially vulnerable populations.

iii. Methods and collaboration: Applies advanced pharmacoepidemiologic and data science approaches, including case-crossover and longitudinal study designs, time-varying survival models, trajectory and hierarchical clustering methods, and machine learning.