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IARC 60th Anniversary - 19-21 May 2026

Session : 20/05/26 - Posters

Associations of maternal education with suggested childhood cancer risk factors: findings from the Childhood Cancer and Leukemia International Consort

KANG A. 10, MILIGI L. 13, SCHEURER M. 5, SPECTOR L. 7, SCHÜZ J. 1, DOLATKHAH R. 1, ERDMANN F. 2, BOUAOUN L. 1, MUELLER B. 3, PETRIDOU E. 4, SCHRAW J. 5, KANE E. 6, MARCOTTE E. 7, FORCE L. 3, DOCKERTY J. 8, MOISSONNIER M. 1, OLSSON A. 1, ROMAN E. 6, CLAVEL J. 9, METAYER C. 10, MAGNANI C. 11, MORA A. 10, RASHED W. 12, CHOW E. 3, BONAVENTURE A. 9

1 IARC, Lyon, France; 2 University Medical Center of the Johannes Gutenberg University Mainz , Mainz, Germany; 3 Fred Hutchinson Cancer Center, Seattle, Washington, United States; 4 Medical School, National and Kapodistrian University of Athens, Athens, Greece; 5 Baylor College of Medicine, Houston, Texas, United States; 6 University of York, York, England, United Kingdom; 7 University of Minnesota, Twin Cities , Minneapolis, Minnesota, United States; 8 University of Otago, Dunedin, New Zealand; 9 Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAe, Center for Research in Epidemiology and StatisticS (CRESS), Paris, France; 10 University of California, Berkeley , Berkeley, California, United States; 11 University of Eastern Piedmont, Novara, Italy; 12 Faculty of Pharmacy-Ahram Canadian University, 6th October, Egypt, Egypt; 13 Institute for the Study and Prevention of Cancer (ISPRO), Florence, Tuscany, Italy

Background
Causes of childhood cancer remain poorly understood. Although socioeconomic status (SES)is not typically considered a direct etiologic factor for childhood cancers, it may be linked to risk through pathways involving lifestyle, environmental, and occupational exposures, and can act as a confounder in epidemiological studies.
Objectives
Using data from the case-control studies of the Childhood Cancer and Leukemia International Consortium (CLIC), we explored how maternal education as a key SES indicator, varies across studies/countries and contributes to understanding of potential environmental and lifestyle risk factors.
Methods
Control group data from cancer-free children matched by diagnosis date of cases from 16 studies were included, using both interview-based and health registry sources. Maternal education, the primary SES measure used in previous analyses with pooled CLIC data, was categorized as low, medium, or high according to the International Standard Classification of Education. Multinomial logistic regression assessed associations between maternal education and perinatal/lifestyle factors, calculating crude and adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for high vs. low education.
Results
Maternal education levels varied across studies and over time, with the highest proportions of highly educated mothers in the U.S. and lowest in Costa Rica, Italy, and Egypt. Higher maternal education was generally positively associated with higher birthweight, breastfeeding, daycare attendance, and maternal prenatal alcohol consumption. Higher maternal education was generally inversely associated with lower birthweight, younger maternal age, paternal occupational pesticide exposure, maternal prenatal smoking, and having more siblings. The direction of associations for older maternal age and for caesarean delivery differed substantially across regions. Exclusion of mothers <21 years at birth of the index child had little effect on the results.
Conclusion/Implication
This multi-country analysis supports the use of maternal education for adjustment as a proxy for SES, showing largely consistent associations with various behaviors and exposures. While the direction of associations was generally consistent, the strengths varied sometimes considerably by geographical region. These findings support the inclusion of maternal education as a covariate in analyses of childhood cancer risk when pooling CLIC studies.

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Exploring the association between maternal education (high versus low) and childhood cancer suggested risk factors: A heatmap of crude ORs from univariate multinomial regression model