Clinical therapeutics · 2026
Regional (in)Consistency and Comparability of Cannabis Laboratory Testing: A Statistical Exploration of Total Yeast and Mold.
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Antoszewski Graham, Carrasco Heather, Glaros Jini, Slade Mackenzie
Аннотация
PURPOSE: Independent laboratory testing of cultivated cannabis is essential for ensuring product quality and protecting consumer health. While individual US states have collected extensive laboratory testing data from their medical and adult-use cannabis markets, a comprehensive, cross-state analysis remains lacking. To address this gap, we apply a design science research methodology to develop a structured framework for analyzing and comparing cannabis laboratory testing practices across decentralized regulatory environments.
METHODS: With data obtained through Freedom of Information Act requests from Maryland, Massachusetts, Michigan, New Jersey, Nevada, and Rhode Island, we construct a statistical artifact that models the total yeast and mold contamination profile within cannabis flower products utilizing an innate characteristic of the state-reported lab data: overdispersion.
FINDINGS: Lognormal models showed superior ability to capture the full breadth of total yeast and mold counts, from the proportion of uncontaminated sampling to the probability of high contamination events in state data, compared with the generalized gamma distribution. State data with higher regulatory action limits had generally better goodness of fit, both statistically and visually, to the proposed models than their lower-limit state counterparts. This analysis reveals inconsistencies in testing outcomes and protocols, offering a clearer understanding of how regulatory differences impact public health protections and market transparency.
IMPLICATIONS: By leveraging design science principles, this research produces a practical tool that supports evidence-based decision-making. The resulting framework is not only valuable to laboratories and industry stakeholders but also highly relevant to policymakers seeking to harmonize testing standards, enhance consumer safety, and promote regulatory alignment across jurisdictions. As more data becomes available, the framework can be expanded to support broader policy development and cross-state collaboration.
METHODS: With data obtained through Freedom of Information Act requests from Maryland, Massachusetts, Michigan, New Jersey, Nevada, and Rhode Island, we construct a statistical artifact that models the total yeast and mold contamination profile within cannabis flower products utilizing an innate characteristic of the state-reported lab data: overdispersion.
FINDINGS: Lognormal models showed superior ability to capture the full breadth of total yeast and mold counts, from the proportion of uncontaminated sampling to the probability of high contamination events in state data, compared with the generalized gamma distribution. State data with higher regulatory action limits had generally better goodness of fit, both statistically and visually, to the proposed models than their lower-limit state counterparts. This analysis reveals inconsistencies in testing outcomes and protocols, offering a clearer understanding of how regulatory differences impact public health protections and market transparency.
IMPLICATIONS: By leveraging design science principles, this research produces a practical tool that supports evidence-based decision-making. The resulting framework is not only valuable to laboratories and industry stakeholders but also highly relevant to policymakers seeking to harmonize testing standards, enhance consumer safety, and promote regulatory alignment across jurisdictions. As more data becomes available, the framework can be expanded to support broader policy development and cross-state collaboration.