ArticleEnvironment international2023
Beyond the cancer slope factor: Broad application of Bayesian and probabilistic approaches for cancer dose-response assessment.
Article in Environment international, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Article
- Soil-water-crop pathways of heavy metal contamination and human health risks in intensive smallholder farms of the Nahavand Plain, Iran.Scientific reports · 2026Article
- The Impact of High Urban Temperatures on Pesticide Residues Accumulation in Vegetables Grown in the Greater Accra Metropolitan Area of Ghana.Journal of xenobiotics · 2025Article
- Analysis of Heavy Metal Characteristics and Health Risk Assessment of Dried Fish Marketed in Guangzhou, China.Biological trace element research · 2025Article
- Comprehensive assessment of heavy metal (HMs) contamination and associated health risks in agricultural soils and groundwater proximal to industrial sites.Scientific reports · 2025Article
- Invited Perspective: Uneven Progress Addressing Population Variability in Human Health Risk Assessment.Environmental health perspectives · 2024Article
- Advancing probabilistic risk assessment by integrating human biomonitoring, new approach methods, and Bayesian modeling: A case study with the mycotoxin deoxynivalenol.Environment international · 2023Article
- A mode of action-based probabilistic framework of dose-response assessment for nonmutagenic liver carcinogens: a case study of PCB-126.Toxicological sciences : an official journal of the Society of Toxicology · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
Traditional cancer slope factors derived from linear low-dose extrapolation give little consideration to uncertainties in dose-response model choice, interspecies extrapolation, and human variability. As noted previously by the National Academies, probabilistic methods can address these limitations, but have only been demonstrated in a few case studies. Here, we applied probabilistic approaches for Bayesian Model Averaging (BMA), interspecies extrapolation, and human variability distributions to 255 animal cancer bioassay datasets previously used by governmental agencies. We then derived predictions for both population cancer incidence and individual cancer risk. For model uncertainty, we found that lower confidence limits from BMA and from U.S. Environmental Protection Agency (EPA)'s Benchmark Dose Software (BMDS) correlated highly, with 86% differing by <10-fold. Incorporating other uncertainties and human variability, the lower confidence limits of the probabilistic risk-specific dose (RSD) at 10
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