Commentary on Peña <i>et al</i>.: The broader public health relevance of understanding and addressing the alcohol harm paradox
Bibliographic record
Abstract
Socio-economic inequalities in alcohol-attributable mortality make an important contribution to socio-economic health inequalities overall. A comprehensive approach to reducing socio-economic inequalities in alcohol-related health requires combining the implementation of evidence-based, cost-effective alcohol control policies with broader policy measures that act upon the structural, economic and social root causes of socioeconomic inequalities. The ‘alcohol harm paradox’ is the public health phenomenon that individuals with low socio-economic status (SES) experience greater alcohol-attributable harm despite equal or lower levels of alcohol consumption [1]. The study by Peña et al. [2] is the most recent and potentially most comprehensive effort yet to investigate the role of joint effects between SES and various behavioral risk factors, most importantly alcohol use, as a potential explanation of the alcohol harm paradox. The interaction effects between a low SES and alcohol use that were demonstrated by the authors are not merely useful to explain the alcohol harm paradox; they are probable contributors to severe public health crises of our times, such as the stagnation and decline of life expectancy at birth in the general population of the United States. Seminal research by Case & Deaton [3] has demonstrated that the increases in mortality that are underlying these recent trends are largely driven by an increase in so-called ‘deaths of despair’; that is, deaths from causes that are closely linked to alcohol and drug use (alcohol and drug poisoning, alcoholic liver cirrhosis and suicide). Individuals with low SES are most affected by these increases in mortality. Similarly, inequalities in alcohol-attributable mortality are rising in Europe and constitute an important driver of socio-economic inequality in mortality in many parts of Europe [4]. This underlines the public health importance of understanding and acting upon socio-economic inequalities in alcohol-attributable health above and beyond understanding the alcohol harm paradox. The rise in socio-economic inequalities that can be expected as a consequence of the current COVID-19 pandemic adds urgency to understanding the alcohol harm paradox and the ways in which the high alcohol-attributable burden among those with low SES can be addressed [5]. What options exist to tackle inequalities in alcohol-attributable harm from a public health perspective? Unfortunately, the most cost-effective alcohol control policies, such as taxation, regulation of availability and implementation of screening and brief intervention (SBI) [6], are not well equipped per se to target low SES populations if we do not pay close attention in their implementation [7]. For example, increasing the coverage with SBI may, in fact, exacerbate socio-economic inequalities in health outcomes due to lower health-care access for individuals with low SES [8]. It is therefore important to combine such initiatives with efforts to increase and facilitate health-care access for low SES populations and to ensure that SBI is offered across a wide range of health-care services, including occupational health-care and community health centers. Minimum unit pricing is the policy with the strongest evidence so far on addressing socio-economic inequality in alcohol consumption and alcohol-attributable harm [9, 10]. By setting a floor price on the cheapest alcohol, which is more likely to be purchased by heavy drinkers and drinkers with low SES, minimum unit pricing has been shown to be a promising tool in lowering inequalities in alcohol-attributable harm. Currently, however, only ten countries [11] in the WHO European Region have implemented some form of minimum unit pricing [12]. Even if effective alcohol policies are being implemented, their impact upon health inequality in alcohol-attributable harm is limited, given that the prevalence and average level of drinking are often already lower among those with low SES. Thus, alcohol policies must be accompanied by upstream policy measures that address the root causes of the socio-economic inequalities themselves. Such upstream policies include initiatives for social welfare, universal health-care coverage, quality and equality in education and reducing stigma and social exclusion [13]. Importantly, a ‘health in all policies’ approach should be applied in all policy planning, assessing potential health consequences for the most disadvantaged groups explicitly, rather than focusing upon productivity alone [14]. In conclusion, relying exclusively upon fast-acting downstream interventions that are directed at emerging health consequences will fail to address the underlying causes that give rise to the alcohol-related inequalities in the first place [13]. A comprehensive approach to reducing inequalities in alcohol-related health has to act on several levels, addressing the social determinants of health, relevant behavioral risk factors and health consequences down the line [13]. None. Charlotte Probst: Conceptualization. Carolin Killian: Conceptualization.
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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".