Micro(nano)plastics and cancer link hypothesis: applying the Bayesian assessment of research causality framework for transparent evidence integration in public health
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Background: Micro(nano)plastics (MNPs) are pervasive environmental contaminants found in oceans, soils, air, food, and human tissues. Their ubiquity coincides with rising cancer incidence, yet evidence remains fragmented across toxicological, mechanistic, and epidemiological domains, limiting causal inference. Traditional approaches such as the Bradford Hill criteria and qualitative Weight-of-Evidence (WoE) provide useful guidance but lack probabilistic rigor for modern risk assessment.
Materials and Methods: this paper introduces the Bayesian Assessment of Research Causality (BARC) framework as a methodological advance in risk analysis. BARC integrates diverse evidence streams into a transparent, quantitative probability of causation. Combined with WoE, it preserves interpretive breadth while adding the precision and reproducibility of Bayesian inference. The framework can be implemented through scalable tiers: simplified Bayes factor combination (Appendix A), hierarchical modeling with shared pathways (Appendix B), and full measurement-error-corrected models. Artificial intelligence further strengthens BARC by enabling automated evidence extraction, dynamic model updating, and polymer-specific sub-modeling.
Results: applied to the emerging MNPs-cancer link, BARC illustrates how structured probabilistic reasoning can guide preventive and regulatory action before epidemiologic certainty is reached. Conclusions: more broadly, BARC offers a flexible, transparent framework for environmental health risk assessment and evidence-based public health policy.
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