Julia DiTosto, MS
Julia DiTosto, MS
Julia DiTosto, MS is a PhD student in epidemiology at the Perelman School of Medicine at the University of Pennsylvania. Her research focuses on applying causal inference methods to reproductive and cardiovascular epidemiology, with a particular interest in the long-term health impacts of gynecologic conditions.
DiTosto earned her MS in Community Health and Prevention Research with a focus in Epidemiology and her BS in Human Biology at Stanford University.
In this Q&A, Julia DiTosto shares how her research bridges causal inference methods with gynecologic and cardiovascular health, the complexities of working with real-world data, and the value she finds in collaboration, curiosity, and community within DBEI.
Can you tell us about your current research and what inspired you to pursue this area?
I’m broadly interested in applying causal inference methods to gynecologic and cardiovascular health. My PhD dissertation work explores how common gynecologic conditions such as uterine fibroids and PCOS may influence cardiovascular disease risk. I investigate whether different treatment approaches for these conditions—both medical and surgical— have an impact on long-term heart health.
Other research interests include studying medication safety during pregnancy, particularly when randomized clinical trials may not be feasible, and leveraging real-world data sources, including administrative claims and electronic health records, to generate insights that can improve clinical care.
How does your research intersect with real-world challenges?
Since my research leverages real-world data sources, there are many challenges! The main issue is that datasets like electronic health records and insurance claims weren’t originally designed for research – they’re created for billing and administrative purposes. This means they have built-in biases and measurement errors that we need to watch out for. I spend a lot of time figuring out how to use causal inference methods like target trial emulation and probabilistic bias analysis to work around these problems.
Another big challenge is that medical treatments are often not straightforward in clinical practice. When someone gets treatment for a condition, it’s rarely a simple A-to-B process. They might try several different medications over months before finding what works, or their treatment plan might change based on how they’re responding. This is totally normal and reflects good patient care, but it makes the data messier to work with. So I also focus on applying methods that can handle these time-varying treatments – basically, ways to analyze data that capture how treatments actually unfold in the real world rather than assuming they follow a linear timeline.
What do you find most rewarding about working in the Department of Biostatistics, Epidemiology and Informatics?
The best part about being in DBEI is definitely the people. My Epidemiology PhD cohort is small yet mighty, working on everything from pharmacology to pain research to examining how structural racism affects health outcomes to diving into how genetic data can be used in epidemiologic studies. It’s really inspiring to be surrounded by such diverse and impactful work.
I’m also involved with the newly launched Center for Health Innovations in Reproductive and Perinatal Population Research (CHIRP), which has been a great experience. CHIRP is led by Drs. Schisterman, Mumford, Caniglia, and Hinkle. It brings together faculty and both pre- and post-doctoral fellows who are all passionate about pushing forward research in reproductive and perinatal epidemiology. Being part of that community has been really rewarding.
Can you share a significant recent project, publication, or professional recognition that you are particularly proud of contributing to or achieving?
The first chapter of my dissertation was recently published in Paediatric and Perinatal Epidemiology! It will be included in their special issue on causal inference methods in reproductive and perinatal health. This work actually started out as a final project in Dr. Enrique Schisterman’s Doctoral Seminar and then evolved into my methods aim for my dissertation. It is my first simulation project! The paper is entitled The Invisible Burden: Examining the Impact of Exposure Misclassification in Epidemiologic Analyses of Uterine Fibroids.
What advice would you give to students or early-career professionals in your field?
I would tell students interested in exploring a PhD or early in the process to be curious! The best way to learn is to ask questions – not just about topics that you are familiar with but also areas you’re interested in exploring. Don’t be afraid to engage in discussions that push you outside your comfort zone.
Questions like “Wait, how does that work?” or “what if we approached this differently?” can lead to really valuable insights. Whether it’s chatting with classmates, attending seminars outside your immediate area (DBEI biweekly seminars, LDI seminars, clinical grand rounds, symposiums at conferences), or just speaking up in meetings when something sparks your interest – that curiosity and willingness to engage is what makes the PhD experience so rewarding.
What is an interest or pastime that you enjoy outside of academia?
I love to try out new recipes, especially from my two favorite cookbooks: Nothing Fancy by Alison Roman and More is More by Molly Baz. I also enjoy hiking, yoga, and spending time with my friends and family in beautiful places 😀