Lingbo Liu and others published their research on “A Multi-Constraint Monte Carlo Simulation Method for Downscaling U.S. Cancer Data from County to ZCTA.” This study introduces an innovative method that estimates suppressed county-level cancer counts and extends the data to ZIP Code Tabulation Areas (ZCTA) by leveraging population structures as probabilistic constraints. Ensuring consistency across data levels and accounting for demographic variations in cancer risk, this approach provides precise and reliable results. Using 2016–2020 cancer incidence data from the Utah Cancer Registry, the method demonstrated high accuracy and consistency across urban and rural areas, significantly outperforming machine learning models like Random Forest and Extreme Gradient Boosting. This work enables more detailed and reliable cancer data analysis, offering a new way for deeper insights into public health trends.