Kevin Martínez-Folgar, MD, PhD

Scientist  ·  Cancer Surveillance Branch  ·  International Agency for Research on Cancer (IARC), WHO  ·  Lyon, France

Dr. Kevin Martínez-Folgar is a physician-epidemiologist originally from Guatemala whose work examines how contextual factors shape cancer risk and outcomes across populations, integrating cancer surveillance, molecular epidemiology, and biomarker approaches to understand the ways in which distinct conditions become biologically embedded in disease.

He began his research career studying urban environments and chronic liver disease across Latin American cities, tracing how place, poverty, and social vulnerability translate into organ-level pathology. That trajectory led him to cancer and to a core conviction: differences in who gets cancer, at what stage, and whether they survive are not random. They are structured. Studying that structure is fundamental to understanding cancer etiology and improving outcomes for everyone.

Much of his work has focused on gastrointestinal and hepatological malignancies, including H. pylori, stomach cancer, and liver cancer, as well as on how exposomic and metabolic frameworks can illuminate disparities across populations.

He is currently a Scientist at the International Agency for Research on Cancer (IARC) in Lyon, within the Cancer Surveillance Branch, where his work focuses on global cancer epidemiology with a particular emphasis on pediatric cancer surveillance and molecular epidemiology at a global scale.

Dr. Martínez-Folgar received his medical degree from the University of San Carlos of Guatemala and earned a PhD in Epidemiology from the Dornsife School of Public Health at Drexel University, where he was a Graduate Research Fellow at the Urban Health Collaborative. He completed postdoctoral training at the University of Michigan’s Center for Global Health Equity and at the Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics (DCEG), U.S. National Cancer Institute, NIH. Early in his career he conducted research at INCAP’s CIIPEC in Guatemala City and received training in population-level physical activity measurement at the National Institute of Public Health in Mexico.

His research portfolio spans mortality and life expectancy inequalities, COVID-19 excess mortality, hepatic disease disparities, healthcare access, and urban determinants of health across Latin American cities. He published the first quantitative description of excess mortality in Guatemala during the COVID-19 pandemic and contributed to the first multi-country comparison of excess mortality across Latin American nations.

He applies multilevel modeling, causal inference, geospatial analysis, and molecular and AI-assisted data workflows. A devoted data visualization enthusiast, he uses R (ggplot2) and Python (matplotlib) to make sense of data before it has words and to shape the evidence that informs public policy and clinical practice around cancer.

Skills & Tools

  • Statistical computing: R (primary — ggplot2, tidyverse, sf), Python (pandas, matplotlib, etc.)
  • Geospatial analysis: GIS, spatial econometrics, mapping
  • Methods: Multilevel modeling, causal inference, mediation analysis, mortality estimation, longitudinal analysis
  • Data workflows: AI-assisted data capture and analysis pipelines, reproducible research
  • Languages: Spanish (native), English (fluent), French (basic)

Contact & Profiles

A little more

from Guatemala import kevin_mf as ThinkOnData

roles = ["Dad", "Brother", "Son", "Husband",
         "Physician", "Epidemiologist",
         "Coder", "Researcher"]

for role in roles:
    print(f"I'm a {role}")