Understanding Cancer Information Burden and Quality Concerns: The Roles of Medical Statistics Literacy, Online Health Information Confidence, and Social Media.

Authors

Aditya Chakraborty, Sikta Das, Amy Myat Phyo

Published in

Journal of cancer education : the official journal of the American Association for Cancer Education. Oct 05, 2026. Epub Oct 05, 2026.

Abstract

Understanding factors shaping perceptions of cancer-related information is important for public health communication. This study examined associations of three information-related predictors, understanding medical statistics, confidence in online health information, and difficulty interpreting social media information, with two outcomes: cancer information was too hard to understand (CTU) and concern about cancer information quality (CCQ). Data came from the 2022 Health Information National Trends Survey. The cross-sectional study included 1972 participants. Survey-weighted univariable and multivariable logistic regression models assessed associations between the three predictors and CTU and CCQ, adjusting for sociodemographic characteristics, health insurance, and cancer history. LASSO logistic regression, random forest, XGBoost, and support vector models were implemented to assess prediction performances for the outcomes. SHAP and variable importance plots identified influential predictors. Difficulty understanding medical statistics was associated with higher odds of agreeing CTU (AOR = 1.89, 95% CI: 1.17-3.05) and agreeing CCQ (AOR = 1.69, 95% CI: 1.11-2.57). Lower confidence in finding helpful health information online was associated with higher odds of both outcomes, whereas complete confidence was associated with lower odds relative to very confident. Greater difficulty interpreting social media information was associated with CTU (AOR = 2.92, 95% CI: 1.66-5.14) and CCQ (AOR = 1.89, 95% CI: 1.07-3.33). SHAP and variable importance identified difficulty understanding medical statistics, confidence in online health information, and difficulty interpreting health information on social media as key predictors. These findings emphasize public health communication strategies prioritizing statistical literacy, clearer cancer information, and improved evaluation of online and social media health content.

PMID:
42830366
Bibliographic data and abstract were imported from PubMed on 05 Oct 2026.

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