A Multi-constraint Monte Carlo Simulation Approach to Downscaling Cancer Data.

Details

• Alternative Title:
  Health & Place, 2025, v. 91: A Multi-constraint Monte Carlo Simulation Approach to Downscaling Cancer Data.
• Journal Article:
  Health & Place
• Personal Author:
  Liu, Lingbo ; Cowan, Lauren ; Wang, Fahui ; Onega, Tracy
• Description:
  This study employs an innovative multi-constraint Monte Carlo simulation method to estimate suppressed county-level cancer counts for population subgroups and extend the downscaling from county to ZIP Code Tabulation Areas (ZCTA) in the U.S. Given the known cancer counts at a higher geographic level and larger demographic groups at the same geographic level as constraints, this method uses the population structure as probability in the Monte Carlo simulation process to estimate suppressed data entries. It not only ensures consistency across various data levels but also accounts for demographic structure that drives varying cancer risks. The 2016-2020 cancer incidence data from the Utah Cancer Registry is used to validate our approach. The method yields results with high precision and consistency across the full urban-rural continuum, and significantly outperforms several machine-learning models such as Random Forest and Extreme Gradient Boosting.
• Content Notes:
  Author manuscript
• Subjects:
  • Humans
  • Monte Carlo Method
  • Neoplasms
  • Registries
  • Incidence
  • Machine Learning
• Keywords:
  • Cancer data suppression
  • spatial data interpolation
  • geo-imputation
  • population downscaling
  • multi-constraint Monte Carlo simulation method
• Source:
  Health & Place, 2025, v. 91
• DOI:
  https://doi.org/10.1016/j.healthplace.2024.103411
• ISSN:
  1353-8292 ; 1873-2054
• Pubmed ID:
  39764879
• Pubmed Central ID:
  PMC11788035
• Document Type:
  Text
• Funding:
  HHSN261201800016C/CA/NCI NIH HHSUnited States/ ; HHSN261201800016I/CA/NCI NIH HHSUnited States/ ; R01 CA267990/CA/NCI NIH HHSUnited States/
• Genre:
  Journal Article
• Place as Subject:
  Utah
• Pages in Document:
  103411 (35 pdf pages)
• Volume:
  91
• Download URL:
  https://stacks.cdc.gov/view/cdc/261797/cdc_261797_DS1.pdf
• File Type:
  [PDF - 2.39 MB]
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha-512:2a7908026fcef1f90ba253828db289b57ed59520ccd370530fdf6d09ffe540f1278b3cf8ad2a08c21f9a189a0080f6b58c2d4a2c32a28f53026e2dd0b7f036c2