Alcohol health-warning labels: promises and challenges
Bibliographic record
Abstract
Should we have graphic and prominent health warnings on alcohol bottles? Yes, for two reasons. First, as cigarette health warnings have increased in size and the graphic nature of their imagery, we have seen increased negative perceptions of smoking,1,2 reduced cigarette appeal,3 increased quit attempts 4 and significant reductions in smoking.5 Second, text warning statements on the harms of alcohol are believable and personally relevant to alcohol consumers.6–8 Additionally, similar to warnings on cigarette packages, graphic alcohol warnings used in recent studies increased fear arousal, intention to reduce alcohol consumption, warning recognition and perceptions of alcohol risks as well as decreased product appeal.9–11 Warnings on cigarette packs have been extensively studied. Their use in practice has evolved from text format messages that occupy increasingly higher surface areas on the pack,12 through graphic warnings that include a pictorial depiction of a diseased organ or an individual suffering from smoking-related diseases,13 to adding contact information on help lines.14 Despite the strong impact of graphic warnings, they still lacked one element: the ability to dissolve branding elements on cigarette packs. Plain packaging standardizes the colours, font, background and shape of cigarette packs, thereby maximizing the prospects of increased attention to warnings and reducing its ability to serve as a marketing tool. Plain packaging results in decreased brand awareness,15 improved warning recognition 13 and increased quitting.16,17 Australia was an early adopter of plain packaging, and the evidence surrounding the positive public health outcomes, coupled with its robustness to tobacco industry legal challenges, served as a catalyst for other governments—such as the UK and France—to adopt similar policies.18 We have learned lessons from the tobacco packaging literature: using specific, direct and large graphic warnings, especially on plain packs, reduces positive consumer and product perceptions.19 We have applied these lessons to two alcohol-label health-warning studies and found results similar to the tobacco control literature.9,10 Continuing to research tobacco-like warnings on alcohol products can shed light on how to help manage, for what some jurisdictions, is a serious public health problem. There is a convincing case for further research on alcohol-label health warnings. Yet because the social acceptability of alcohol use remains high, the public does not warmly welcome the idea of alcohol health warnings. After publishing our studies,9,10,19 we have had substantial national and international coverage on radio, TV and online press. Public opinion on the matter was divided: On the one hand, we were labelled as ‘nannyists’ and ‘temperance movement advocates’ and on the other hand, our work was thought of as ‘reviving the debate’ and ‘increasing awareness.’ Perhaps it is not surprising that public perception of our work was somewhat negative, as 80% of Canadians consume alcohol regularly, 38% of those 15 or older engaged in risky drinking and 27% engaged in long-term heavy drinking.20 With the ease of using technologies such as Twitter and Facebook, proposing protective health policy measures that can be seen as ‘overreach’ can elicit viral reactions; thus politicians may be hesitant to implement healthy policies. Of course, the alcohol industry is likely to oppose labelling. Product packaging including bottle shapes, seals, colours and descriptor fonts are all marketing differentiation tools that companies use to signal consumers. Millions of dollars are invested in advertising campaigns, and any health-warning label will reduce the appeal of products. Like the tobacco industry,21 the alcohol industry has demonstrated its willingness to oppose the use of health warnings and plain packaging policies. In the face of opposition towards alcohol health warnings, how can public health interests be fulfilled? First, we have to remember that cigarette smoking used to be just as socially acceptable as alcohol use. Increasing the awareness of the public towards harms led to the endorsement of measures that restrict tobacco use.22 Governments followed the public's momentum for regulating tobacco products, including the use of health warnings. Second, the public gravely underestimates the negative consequences of heavy and risky drinking despite the fact that they result in a yearly economic cost of C$14.6 billion in Canada alone.23 Public health advocates need to de-normalize heavy drinking, because it causes liver cirrhosis, cancer and a multitude of other diseases. Further, drinking and driving continues to cost lives, injuries and lost productivity.24 Third, researchers must continue to study alcohol health warnings and encourage others to do the same. If the evidence base for alcohol health warnings rises to the level of that which exists for tobacco warnings, then the case will be made. A journey of a thousand miles starts with a single step—small steps starting with visible, direct and specific text warnings can be effective. As with cigarette pack warnings, multiple warnings should be circulated to prevent habituation to the messages.7,25 Finally, we must accept that given the history of the backlash from the tobacco industry—hiring expert witnesses to testify falsely, paying historians to re-write history, encouraging journalists to write sympathetic articles and funding researchers to undermine the causality between smoking and diseases26–31—the same could and will happen with the alcohol industry. However, researchers and public health campaigners should not be deterred. Our responsibility is to continue to maintain scientific objectivity and test the value of warnings. The primary view should be to improve public health. Public awareness is key; public opinion supporting the public interest can ultimately overcome government inertia. Given the right conditions, governments can be convinced to put public health interests before the financial interests of the alcohol industry. A proactive dialogue involving the public, industry and governments, similar to the front-of-package nutrition labelling proposal initiated by Health Canada could be an effective forum for finding the right balance between government, public and industry interests.32 None declared.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.113 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.018 | 0.034 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.028 | 0.036 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".