Commentary on Meier <i>et al</i>.: Gender disparities in the effects of alcohol pricing policies on consumption and harm reduction
Bibliographic record
Abstract
While increasing alcohol tax and introducing a minimum unit price will probably have only a moderate impact on women's drinking, the harm that women experience from others’ drinking could be substantially reduced. If this epidemiological model were to be applied to measure the effects of alcohol pricing policies on alcohol's harm to others, different gender-specific effects may be found. Studies have found that women are more likely to experience many harms from others’ drinking than men, including from family and domestic violence, sexual and verbal abuse, financial harm and from care-giving; for instance, when they are required to care for drinkers and drinkers’ dependents [3-6]. Meier and colleagues’ modelling results show that the introduction of MUP and a 10% tax increase in the United Kingdom will lead to greater reduction of consumption among men than women. Subsequently, we posit that this could reduce the alcohol harm to others that women experience from men's drinking, given that the majority of harmful drinkers are men. To the best of our knowledge, no studies have explored the effects of alcohol pricing policies on interpersonal harms from others’ drinking in different gender and other population subgroups, and this is a significant research gap in the field. Meier et al. [1] found that the estimated impact of alcohol pricing policy on women's consumption was moderate and would probably only affect consumption of high-risk drinking women who live in the most deprived areas of the United Kingdom. The modelling showed that women in socio-economically advantaged groups are likely to maintain their alcohol consumption and spend more money on alcohol after the tax or price increase than women in other socio-economic groups. However, men are likely to continue to spend the same amount on alcohol and reduce total consumption if the price goes up, particularly if they are socio-economically disadvantaged. Similar gender-specific effects of alcohol pricing policies might be expected in other high-income countries with similar drinking cultures to the United Kingdom, such as in Australia and Canada [2, 7]. Given that the gaps in heavy drinking prevalence between the sexes are even greater in many low- and middle-income countries (LMICs; for example, in China, India, Thailand and countries in Africa) than in Australia, the United Kingdom and United States [8], even more marked effects on harm to women from others’ drinking might be expected. Therefore, applying taxation or a floor price policy in LMICs could substantially reduce men's consumption and their alcohol-related hospitalizations, and while achieving a very small or almost no impact on women's drinking, the harm women experience from others’ drinking could also be substantially reduced. The gap in alcohol consumption between men and women has narrowed in the last two decades, with binge-drinking and alcohol use disorder increasing more rapidly among middle-aged women [9-11]. UK national statistical data show that the proportions of men and women drinking riskily (men and women who exceeded 8 and 6 units of alcohol on their heaviest drinking day, respectively) were very similar in 2017, although men still drank more [12]. Modelling the gender-specific effects of alcohol pricing policies among different age groups with different levels of drinking might provide evidence that consumption and harm among middle-aged women can be reduced, as it has been for middle-aged men by interventions that increase price: for instance, by separately modelling interventions, changes in consumption and subsequent hospitalization rates among middle-aged women. Including women in all age groups, despite adjusting for age, may mask the direct effects of pricing policies on middle-aged women, if interventions have less impact on younger and older women's drinking and experiences of harm. The modelling work by Meier et al. provides evidence that alcohol taxation and minimum pricing policies can not only reduce gender gaps in alcohol consumption and alcohol-related hospitalizations in the United Kingdom, but that these policies can also reduce social inequalities, as both men and women in the most deprived areas were estimated to reduce their alcohol consumption more than drinkers from less deprived areas. While this study has provided strong evidence using the modelling and predictive data, future studies could use real-time data to evaluate the gender specific impact of MUP among different socio-economic groups on alcohol consumption and alcohol-related harms, for instance, in two UK regions retrospectively, following the introduction of MUP in Scotland (1 May 2018) and Wales (2 March 2020) and test the validity of the current modelling study. None.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".