# Cancer incidence data at the ZIP Code Tabulation Area level in the United States interpolated by Monte Carlo simulation with multiple constraints

## TL;DR

This study creates detailed cancer incidence data for small U.S. geographic areas using a simulation method, improving public health research and interventions.

## Contribution

A novel multi-constraint Monte Carlo simulation framework to reconstruct and disaggregate suppressed cancer data to ZIP Code Tabulation Areas.

## Key findings

- The method reconstructs suppressed county-level cancer data using demographic and macro-level incidence constraints.
- The dataset enables high-resolution spatial analyses and precision public health interventions at multiple geographic scales.
- The approach ensures consistency and reliability of cancer incidence data across different spatial levels.

## Abstract

High-quality cancer data are fundamental for public health research and policy, but cancer data for small geographic units and population subgroups in the United States are rarely available due to small-sample suppression rules, spatial coarsening, and data incompleteness. These limitations hinder high-resolution spatial analyses and precision public health interventions. This study provides a high-resolution cancer incidence dataset for the U.S., generated through a multi-constraint Monte Carlo simulation framework that reconstructs suppressed county-level cancer data and systematically disaggregates them to ZIP Code Tabulation Areas (ZCTAs), guided by demographic constraints. This method integrates population subgroup structures and macro-level incidence rates as constraints, ensuring consistency and reliability across spatial scales. The resulting dataset spans multiple geographic units, from state and county levels to ZCTAs, enabling comprehensive analyses of cancer burden, in-depth spatial analyses, and precision public health interventions across multiple scales.