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Geographical and socioeconomic disparities across site-specific and pan-cancer risk factors

Principal Investigator
Name
Mikko Myrskylä
Degrees
Ph.D.
Institution
Max Planck Institute for Demographic Research
Position Title
Director
Email
About this CDAS Project
Study
PLCO (Learn more about this study)
Project ID
PLCO-1983
Initial CDAS Request Approval
Sep 22, 2025
Title
Geographical and socioeconomic disparities across site-specific and pan-cancer risk factors
Summary
Disparities in cancer incidence, stage at diagnoses, treatment, and survival have been studied and reported at geographical, societal and national levels. Socioeconomic status is central to understand these disparities, as lifestyle behaviors, healthcare-seeking patterns, geographical and environmental conditions, and occupational exposures are all strongly patterned by socioeconomic position. At the same time, the relationships between cancer and their risk factors are multifactorial: individual cancer is often attributable to multiple risk factors, while specific exposure often has pleiotropic effects across multiple cancers.
Our project aims to investigate the associations between diverse risk factors and multiple cancer types within the context of geographical and socioeconomic inequities using PLCO and NLST cohorts. We will further develop predictive models for cancer risk assessment, cancer burden evaluation and long-term projection. We aim to provide robust evidence to inform cancer preventions and public health interventions. Additionally, the predictive models and results will be validated in independent populations.
Aims

Aim 1: To use observational data from PLCO and NLST to perform a target trial emulation examining the relationship between exercise and cancer incidence and all-cause mortality
Aim 1: we will conduct a systematic analysis to investigate the associations between diverse risk factors and multiple cancer types considering different geographical and socioeconomic groups.
Aim 2: we will develop predictive models on cancer incidence and mortality using statistical and machine learning algorithms integrating multiple geographical and socioeconomic identifications.
Aim 3: we will evaluate the performance of predictive models based on independent populations.
Aim 4: we will predict cancer risk and burdens for difference geographical regions and socioeconomic groups.

Collaborators

Peng Li The Max Planck Institute for Demographic Research
Mikko Myrskylä The Max Planck Institute for Demographic Research