Search

Background and Aims: The alcohol harm paradox (AHP) posits that disadvantaged groups suffer from higher rates of alcohol‐related harm compared with advantaged groups, despite reporting similar or lower levels of consumption on average. The causes of this relationship remain unclear. This study aimed to identify explanations proposed for the AHP. Secondary aims were to review the existing evidence for those explanations and investigate whether authors linked explanations to one another. Methods: This was a systematic review. We searched MEDLINE (1946–January 2021), EMBASE (1974–January 2021) and PsycINFO (1967–January 2021), supplemented with manual searching of grey literature. Included papers either explored the causes of the AHP or investigated the relationship between alcohol consumption, alcohol‐related harm and socio‐economic position. Papers were set in Organization for Economic Cooperation and Development high‐income countries. Explanations extracted for analysis could be evidenced in the empirical results or suggested by researchers in their narrative. Inductive thematic analysis was applied to group explanations. Results: Seventy‐nine papers met the inclusion criteria and initial coding revealed that these papers contained 41 distinct explanations for the AHP. Following inductive thematic analysis, these explanations were grouped into 16 themes within six broad domains: individual, life‐style, contextual, disadvantage, upstream and artefactual. Explanations related to risk behaviours, which fitted within the life‐style domain, were the most frequently proposed (n = 51) and analysed (n = 21). Conclusions: While there are many potential explanations for the alcohol harm paradox, most research focuses on risk behaviours while other explanations lack empirical testing. [ABSTRACT FROM AUTHOR]

Background: Environmental cues shape behaviour, but few studies compare the impact of targeting healthier vs. less-healthy cues. One online study suggested greater impact on selection from increasing the number of less-healthy (vs. healthier) snacks. The current study aimed to: (1) extend the previous study by using physically-present snacks for immediate consumption; (2) explore responsiveness by socio-economic position; (3) investigate possible mediators (response inhibition, food appeal) of any socio-economic differences in selection.Methods: In a between-subjects laboratory experiment UK adults (n = 417) were randomised according to their ID number (without blinding) to one of three ranges of options: Two healthier, two less-healthy ["Equal"] (n = 136); Six healthier, two less-healthy ["Increased Healthier"] (n = 143); Two healthier, six less-healthy ["Increased Less-Healthy"] (n = 138). Participants completed measures of response inhibition and food appeal, and selected a snack for immediate consumption from their allocated range. The primary outcome was selection of a healthier (over less-healthy) snack.Results: The odds of selecting a less-healthy snack were 2.9 times higher (95%CIs:1.7,5.1) in the Increased Less-Healthy condition compared to the Equal condition. The odds of selecting a healthier snack were 2.5 times higher (95%CIs:1.5,4.1) in the Increased Healthier (vs. Equal) condition. There was no significant difference in the size of these effects (- 0.2; 95%CIs:-1.1,0.7). Findings were inconclusive with regard to interactions by education, but the direction of effects was consistent with potentially larger impact of the Increased Healthier condition on selection for higher-educated participants, and potentially larger impact of the Increased Less-Healthy condition for less-educated participants.Conclusions: A greater impact from increasing the number of less-healthy (over healthier) foods was not replicated when selecting snacks for immediate consumption: both increased selections of the targeted foods with no evidence of a difference in effectiveness. The observed pattern of results suggested possible differential impact by education, albeit not statistically significant. If replicated in larger studies, this could suggest that removing less-healthy options has the potential to reduce health inequalities due to unhealthier diets. Conversely, adding healthier options could have the potential to increase these inequalities.Trial Registration: ISRCTN: ISRCTN34626166 ; 11/06/2018; Retrospectively registered. [ABSTRACT FROM AUTHOR]

Aim: To assess the impact of purchasing wine in 50 cl bottles compared with 75 cl bottles on the amount of wine consumed at home. Design Cross‐over randomized controlled trial with a 'usual behaviour' period of a maximum of 3 weeks between conditions. Setting: Households in the United Kingdom. Participants: One hundred and eighty‐six households that consumed between two and eight 75 cl bottles of wine each week. Intervention: Households were randomized to the order in which they purchased wine in two bottle sizes. During two 14‐day intervention periods, households purchased a pre‐set volume of wine—based on their baseline self‐reported weekly consumption—in either 75 cl bottles or 50 cl bottles. On days 7 and 14 of each study period, participating households sent photographs of each purchased wine bottle. Measurements The primary outcome was the volume of study wine in millilitres (ml) consumed during each study period estimated through returned photographs. The secondary outcome was the rate of consumption measured by the mean number of days taken to drink 1.5 litres from each bottle size. Findings One hundred and sixty‐six of 186 enrolled households satisfactorily completed the study. After accounting for pre‐specified covariates, 191.1 ml [95% confidence interval (CI) = 42.03–339.2] or 4.5% (95% CI = 1.0–7.9%) more wine was consumed per 14‐day period from 75‐cl bottles than from 50‐cl bottles. Consumption was 5.8% faster (95% CI = –10.9 to –0.4%) from 75 cl bottles than from 50 cl bottles. Conclusions: Consuming wine at home from 50 cl bottles, compared with 75 cl bottles, may reduce both amount consumed and rate of consumption. [ABSTRACT FROM AUTHOR]

Sugar-sweetened beverages (SSBs) are one of the largest added sugar sources to diets in the UK and USA. Health warning labels reduce hypothetical selection of SSBs in online studies but uncertainty surrounds their impact on selection of drinks for consumption. Calorie information labels are also promising but their impact on SSB selection is unclear. This laboratory study assessed the impact on SSB selection of 'on-pack' labels placed directly on physical products: i.a pictorial health warning label depicting an adverse health consequence of excess sugar consumption; and ii.calorie information labels. Potential moderation of any effects by socio-economic position (SEP) was also examined. Participants - 401 adults, resident in England, approximately half of whom were of lower SEP and half of higher SEP, were asked to select a drink from a range of two non-SSBs and four SSBs (subsequent to completing a separate study assessing the effects of food availability on snack selection). The drinks included 'on-pack' labels according to randomisation: Group 1: pictorial health warning label on SSBs; Group 2: calorie information label on all drinks; Group 3: no additional label. The primary outcome was the proportion of participants selecting an SSB. Compared to not having additional labels (39%), neither the pictorial health warning label (40%) nor calorie information labels (43%) affected the proportion of participants selecting an SSB. Lower SEP participants (45%) were more likely to select an SSB compared to those of higher SEP (35%), but SEP did not moderate the impact of labels on drink selection. In conclusion, pictorial health warning labels may be less effective in reducing SSB selection in lab-based compared with online settings, or depending on label design and placement. Findings suggest that effects might be absent when choosing from real products with actual 'on-pack' labels, positioned in a 'realistic' manner. Field studies are needed to further assess the impact of 'on-pack' SSB warning labels in real-world settings to rule out the possible contribution of study design factors. [ABSTRACT FROM AUTHOR]