Commentary on Chan & Hall (2020): Advances in specifying cannabis consumption
Bibliographic record
Abstract
Cannabis consumption patterns are not evenly distributed and more frequent users, who are the minority of the population, disproportionately consume cannabis. Accurate measurement of the quantity and frequency of cannabis will require new approaches to surveillance, and could lead to better assessment of the health outcomes of cannabis exposure. Chan & Hall estimate the proportion of total cannabis consumption in Australia by people who use cannabis daily compared to those who use less frequently 1. Analogous to alcohol, where the majority is consumed by those who drink the most frequently 2, Chan & Hall found that people who use cannabis daily consume approximately 80–85% of the cannabis used in Australia, providing a stark reminder that consumption patterns are not evenly distributed. People who use more frequently represent a minority of the total population of consumers of cannabis, but disproportionately consume the substance. In the midst of rapidly changing cannabis legal and regulatory landscapes in multiple US states, Canada and Uruguay (among others), these methods and findings can be used to inform public health policy. Research on patterns of alcohol consumption have encouraged the focus of interventions targeting heavier alcohol consumption 3, 4. While much less is known about how cannabis consumption patterns relate to health and social outcomes, identifying use patterns beyond basic ‘any’ versus ‘none’ subgroups could inform development of tailored interventions. To date, studies that examine an overall association of cannabis use with psychiatric outcomes show mixed results 5. However, research that takes into account frequency of cannabis consumption during adolescence shows a dose–response relationship with negative health outcomes 6, 7, suggesting a scientific justification to focus efforts on the most frequent users. Might subgroups that use the most cannabis be the priority for harm reduction and health promotion? This concept is proposed by Chan & Hall, with specific recommendations to impose taxes on cannabis products in proportion to potency, strengthen social norms that discourage heavy consumption, restrict marketing practices that target heavy use patterns and screen and intervene in medical settings with the people who use cannabis the most heavily 1. Nevertheless, such a focus on heavier consumption patterns should not come at the exclusion of broader prevention strategies to reduce cannabis and other substance use initiation, such as expanding universal preventive interventions that have demonstrated lasting protective effects 8, 9. Indeed, pursuing these strategies in parallel is likely to maximize public health benefits. Having information about quantity and frequency allows for examination of the overall proportion of cannabis consumed by daily users, but a key additional need is to understand cannabis product potency (analogous to comparing beer, wine and spirits in determining alcohol quantities). Approaches to determining alcohol consumption based on a ‘standard drink’ are well established 10 and interviews have been designed to convert self-report of beverage consumption into these standard drink measures 11, although definitions of such standards may vary across jurisdictions 12. Much less is known about how to operationalize such standardization for cannabis. What is a ‘standard cannabis unit’? This issue is becoming increasingly complex with the proliferation in many jurisdictions of cannabis-based products such as edibles and highly concentrated oils and waxes used in vaping or dabbing devices. Freeman proposes to standardize cannabis use by adopting a 5-mg Δ-9-tetrahydrocannabinol (THC) approach 13. In this approach, oral, combustible, vaporized (and other) formulations would all be based on how much THC is ingested overall. Of course, a key limitation is that users may not know the potency of the products they consume and so conversion to a standard may be based on inaccurate information. A more simplistic approach is taken by Chan & Hall in which dosages from different smoking devices (i.e. ‘bongs’ and ‘joints’) were converted using a standard formula 1. Similarly, national surveys in the United States have relied upon measures of frequency and self-reported ‘joints’ as an approximation for the quantity consumed 14. It is clear that more research is needed to inform cannabis standardization measures 13. With legalization of cannabis in multiple jurisdictions, information to allow more precise dosage consideration is plausible as part of regulatory systems that monitor product potency, although these would still be quite complex undertakings. In the meantime, improvements to measurement of consumption frequency is also needed. Surveys such as the US National Survey on Drug Use and Health query the number of days of use, but not occasions per day 15. However, accurately quantifying total exposure will be based on using occasions per day, number of days of use and quantity consumed per use. Alternatives to current practice may be warranted to capture the details needed for a full assessment of the health outcomes associated with cannabis use. Finally, although this study was limited to Australia, opportunities exist to determine whether similar patterns are identified in other countries that collect surveillance information about quantity/frequency of cannabis consumption. Key questions include: do consumption patterns vary across jurisdictions; how do different legal and regulatory schemes influence consumption patterns; what consumption patterns are associated with various health and social outcomes? Only with cross-national comparisons using standardized approaches to quantifying cannabis consumption will these important questions be answered. W.M.C. reports long-term holdings in General Electric Company, 3 M Companies and Pfizer, Incorporated, unrelated to the present work. The opinions expressed in this article are those of the authors and do not necessarily represent the opinions of the National Institute on Drug Abuse, the National Institutes of Health, or the Centers for Disease Control and Prevention or the US Department of Health and Human Services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".