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Record W4387331515 · doi:10.1111/add.16358

Improving the epidemiology of low‐risk drinking guidelines is not enough

2023· article· en· W4387331515 on OpenAlexaboutno aff
Michael Livingston

Bibliographic record

VenueAddiction · 2023
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
FundersAustralian Research CouncilCurtin University of Technology
KeywordsRigourPopulationTransparency (behavior)Risk assessmentWork (physics)EpidemiologyRisk analysis (engineering)MedicineEnvironmental healthPolitical scienceEngineeringComputer scienceLawComputer security

Abstract

fetched live from OpenAlex

Work to improve the precision of the epidemiology underlying national low-risk drinking guidelines is important, but until the field engages more deeply in understanding how risk is interpreted, communicated and understood, guidelines will continue to have uncertain impacts. Shield et al. [1] draw upon the recent redevelopment of the Canadian Low Risk Drinking Guidelines to formulate some key principles that, they argue, should underpin future guidelines work internationally. This is an admirable attempt to further earlier work by Holmes et al. [2] arguing for increasing rigour and transparency in the guidelines setting process and offers much food for thought. Fundamentally, the setting of guidelines is concerned with risk, with (i) accurately estimating via sophisticated epidemiology and modelling the risks of various outcomes (often mortality) associated with drinking, (ii) determining some level of population risk considered acceptable and (iii) communicating these risks to the population. Much of the energy in the various guidelines committees in recent decades has been focused upon (i), which has led to substantial improvements in our understanding of the population impacts of alcohol e.g. [3, 4], although there remains ongoing debate and uncertainty in key areas [5]. Strikingly little research has been conducted on either (ii) or (iii). It is remarkable that guidelines committees have, from at least the 2009 Australian guidelines [6], relied upon a 1969 analysis of risk acceptability by Starr [7], which has since been critiqued and expanded upon in a large body of work examining risk perception and acceptability [8, 9]. Research has demonstrated clearly that risk perceptions and acceptability vary markedly among different risks, depending upon factors including familiarity, immediacy, personal experience and perceived benefits (among many others) [10]. Further, there are clear and predictable variations in risk acceptability between subpopulations, based on gender, age, living situation and more [11-13]. Surprisingly little work has followed to situate alcohol epidemiology within these broader literatures on risk. Thus, our reliance upon relatively simplistic risk thresholds (1/100 in the recent Australian and UK guidelines) seems arbitrary. This supports the argument put forward by Shield et al. that providing a continuum of risk is a more appropriate approach to guideline development, letting individuals make their own, informed decisions about risk acceptability by providing a range of risk thresholds or a continuous risk function. This is, however, obviously contingent upon (iii), the communication and understanding of risk by the general public. The Canadian guidelines provide a good example of the challenges here, with the relatively sophisticated risk continuum simplified throughout hundreds of media articles into a single guideline of two drinks per week [14, 15]. Our understanding of how best to communicate the risks that underpin drinking guidelines remains poor, despite potential lessons from a substantial broader research field [16, 17]. Fundamentally, many of the questions raised by Shield et al. are empirical questions that require targeted research—what measures of ‘health loss’ are best understood by the general public? What levels of risk are acceptable, and how should we interpret variation in risk perception and acceptability when developing guidelines? Are simple, single-threshold guidelines more acceptable and useful to the target population than guidelines that include continuums of risk? How should we best communicate guidelines such that consumers are making genuinely informed choices? Alcohol epidemiology has made major and important advances in recent decades, and our understanding of the health and social impacts of alcohol continues to improve as methods develop. Guidelines rely upon ever more precise and complex estimates of risk, based upon sophisticated models and well-argued epidemiological assumptions. These advances have not necessarily been matched by improvements in our understanding of risk perception and communication, and the alcohol field should prioritize research regarding these topics and collaboration with experts in risk and risk communication to ensure that guidelines deliver on their potential for population health. This work was entirely written and conceptualised by Michael Livingston. Open access publishing facilitated by Curtin University, as part of the Wiley - Curtin University agreement via the Council of Australian University Librarians. M.L. served on the Australian Low-Risk Drinking Guidelines expert advisory panel for the revised guidelines released in 2019. He has no other interests to declare. Data sharing not applicable - no new data generated, or the article describes entirely theoretical research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.217

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.077
GPT teacher head0.346
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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