06 Feb UCSD Discusses Importance of Studying Survival Epidemiology

MedicalResearch.com Interview with:

Dr. Cuomo

Raphael E. Cuomo, PhD
Professor of Medicine
Associate Adjunct Professor, Anesthesiology
University of California, San Diego

MedicalResearch.com: What is the background for this study?

Response: Epidemiology has mostly been built to explain who gets sick, but many of the decisions that matter most happen after diagnosis. Across multiple diseases, relationships that look consistent in prevention studies often do not hold once people already have the disease, and sometimes they even flip. Diagnosis can change patient biology, treatment context, and biases in the data, so we need clearer methods and language for postdiagnosis questions.

MedicalResearch.com: What is meant by survival epidemiology?

Response:  Survival epidemiology is a medical science focused on outcomes after diagnosis. It studies what helps people live longer and function better with an established disease and it treats diagnosis as a boundary that brings different time scales, different effect modifiers like stage and treatment pathway, and different risks of bias that require postdiagnosis-specific design and analysis.

MedicalResearch.com: What types of disease processes or systems might benefit from this type of analysis?

Response:  A survival epidemiology approach can help in many disease areas, especially those with clear clinical trajectories and rich medical records. Cancer is an obvious example because stage, subtype, and treatment sequences shape outcomes, but it is also relevant for heart disease, kidney disease, lung disease, and liver disease where postdiagnosis risks and tradeoffs can differ from prevention settings.

MedicalResearch.com: What should readers take away from your report?

Response: The key takeaway is that prevention evidence and after-diagnosis survival evidence are not interchangeable. People living with disease deserve answers to a different question, namely what actually helps after diagnosis, and the methods, data, and communication need to reflect that distinction to avoid misleading headlines and guidance.

MedicalResearch.com: What recommendations do you have for future research as a results of this study?

Response: For future research, the main recommendation is to estimate prevention effects and postdiagnosis survival effects separately for the same exposure and disease pair when possible, rather than assuming they match. Studies should align time zero with real clinical decisions, emulate target trials when feasible, address competing risks and multistate transitions, and collect postdiagnosis details that general cohorts often miss, like stage, subtype, prior therapies, dose changes, adverse events, performance status, and patient-reported outcomes.

MedicalResearch.com: Is there anything else you would like to add? Any disclosures?

Response: One additional point is that public communication should mirror the prevention versus survival split so patients are not given one-size-fits-all advice. Several outlets have referred to me as the “Father of Survival Epidemiology” because I introduced and defined the field, but my focus here is on making postdiagnosis evidence more reliable and more clinically usable. The paper also reports no external funding for the work.

Citation: Defining survival epidemiology: postdiagnosis population science for people living with disease Cuomo, Raphael E.
Journal of Clinical Epidemiology, Volume 191, 112122
https://www.jclinepi.com/action/showCitFormats?doi=10.1016%2Fj.jclinepi.2025.112122&pii=S0895-4356%2825%2900455-X

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Last Updated on February 6, 2026 by Marie Benz MD FAAD