Population Health  ·  Cancer Epidemiology  ·  Health Equity

"Cancer doesn't
discriminate.."

But we know it does.

Differences in who gets cancer, at what stage, and whether they survive are not random. I argue that studying those differences is one of the most direct paths into understanding cancer.

Kevin Martínez-Folgar, MD, PhD

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

As physicians we act as lifeguards in a river, always pulling the drowning to shore. But one day we need to ask who (or what) is throwing so many people into the river? I was treating patients daily, but one day I wanted to understand the why's. So I became an epidemiologist. I am a physician-scientist born in Guatemala and trained in Guatemala and the United States, and my work tries to answer Geoffrey Rose's fundamental question: why does this patient have this disease, in this place, at this particular time? My research integrates cancer surveillance, molecular epidemiology, and biomarker approaches to understand inequities in cancer outcomes and the ways in which social conditions become biologically embedded in disease. Much of my work has focused on disparities and hepato-gastro malignancies, but my broader goal is to understand how place, environment, and social context interact with biology to shape cancer etiology, risk, and treatment.

My work combines quantitative epidemiology with computational methods, using tools such as R, Python, Spark, geographic information systems, and AI-assisted workflows to translate complex data into insights that can inform public health policy and clinical practice.

What I study

My work spans cancer epidemiology, molecular approaches, and data science, unified by one question: why are cancer burdens distributed unequally across populations?

Cancer Epidemiology

Cancer Disparities & Global Surveillance

Using population differences as a scientific instrument to understand why cancer occurs, in whom, and under what conditions. Population-level monitoring of incidence, mortality, and survival, from Latin American cities to global registries, as a tool for tracing causal pathways and informing prevention.

Molecular Epidemiology

Molecular & Exposomic Approaches

Integrating biomarker data, proteomics, and exposomic frameworks to understand how biological pathways mediate the effects of social and environmental exposures on cancer risk and progression. Linking population-level patterns to molecular mechanisms.

Methods

Data Science & AI in Epidemiology

Reproducible analytic workflows in R and Python. Geospatial analysis, multilevel modeling, causal inference, and AI-assisted data pipelines, translating complex population data into evidence for policy and clinical practice.

  • Cancer Epidemiology
  • Global Cancer Surveillance
  • Cancer Disparities
  • Molecular Epidemiology
  • Biomarker Approaches
  • Infection-Related Malignancies
  • Pediatric Cancer Surveillance
  • Health Equity
  • Geospatial Analysis
  • Causal Inference
  • AI-Assisted Analysis
  • Data Visualization

Publications

  1. 01

    Excess mortality during the COVID-19 pandemic in Guatemala

    Martinez-Folgar K, Alburez-Gutierrez D, Paniagua-Avila A, Ramirez-Zea M, Bilal U

    American Journal of Public Health, 2021

    DOI: 10.2105/AJPH.2021.306452 ↗
  2. 02

    Inequalities in life expectancy in six large Latin American cities — SALURBAL study

    Bilal U, …, Martinez-Folgar K, et al.

    The Lancet Planetary Health, 2019

    DOI: 10.1016/S2542-5196(19)30235-9 ↗
  3. 03

    Mortality amenable to healthcare in Latin American cities

    Mullachery P, …, Martinez-Folgar K, et al.

    International Journal of Epidemiology, 2022

    DOI: 10.1093/ije/dyab137 ↗
  4. 04

    Variability and social patterning of cancer mortality in 343 Latin American cities

    Alfaro T, Martinez-Folgar K, Bilal U, et al.

    The Lancet Global Health, 2025

    DOI: 10.1016/S2214-109X(24)00446-7 ↗

Full list on publications page and Google Scholar ↗