CSDE Research Areas:
- Health of People and Populations
Sascha Dublin, MD, PhD, is a general internist and epidemiologist whose main research interest is studying the impact of prescription medications and other interventions using real-world health care data. Through this work, she aims to provide patients and health care providers with better information about the risks and benefits of different treatments so they can make more informed decisions.
Dr. Dublin’s work includes studies of medication use in relation to pneumonia risk and dementia risk in older adults. For example, her team found that heavy use of some commonly used medications including antihistamines increases dementia risk. She also has interest and expertise in improving the methods used to study drug safety in older people by better accounting for coexisting illnesses and functional and cognitive status.
Much of Dr. Dublin’s research focuses on the outcomes of medication use or other interventions in pregnancy. These studies take advantage of the rich clinical data becoming increasingly available through electronic health records (EHRs) to develop new knowledge that could improve care. For example, she is working with U.S. Food and Drug Administration (FDA) on study of birth defects in relation to opioid use in pregnancy and on a separate study to test whether collecting information from pregnant women through a mobile app can improve the data available to study medication safety in pregnancy.
Dr. Dublin recently completed an R01 grant from the National Institute of Child Health and Human Development to study maternal and infant outcomes after elective induction of labor. She also holds an R01 grant from the same institute to study the impact of treating mild to moderate hypertension during pregnancy.
Dr. Dublin has a strong interest in epidemiologic methods, particularly in approaches to better measure important variables. She has led methods-focused workgroups for the FDA’s Sentinel Initiative and has experience using Natural Language Processing to extract information from unstructured clinical text.