Next Article in Journal
Dysregulated Iron Homeostasis in Atopic Dermatitis: Linking Iron Deficiency to Clinical Severity and Quality of Life
Previous Article in Journal
Highly Processed Food and Functional Gastrointestinal Disorders in Children and Adolescents with Obesity: The Preventive Challenge—A Narrative Review
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 17
Issue 23
10.3390/nu17233745
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Segmenting Increasing- and High-Risk Alcohol Drinkers by Motives and Occasions: Implications for Targeted Interventions

by
Chloe Bennett
1,
Liam J. Barratt
2 and
Alastair O’Brien
1,*
1
Division of Medicine, University College London, London WC1E 6BT, UK
2
Drinkaware, London EC1Y 4SE, UK
*
Author to whom correspondence should be addressed.
Nutrients 2025, 17(23), 3745; https://doi.org/10.3390/nu17233745
Submission received: 30 October 2025 / Revised: 25 November 2025 / Accepted: 27 November 2025 / Published: 28 November 2025
(This article belongs to the Section Nutrition Methodology & Assessment)
Browse Figures
Review Reports Versions Notes

Abstract

Background/Objectives: Excess alcohol consumption remains a major challenge for public health in the UK. This study aimed to define and characterise clusters of increasing- and high-risk drinkers, based on drinking motives and occasions and contextualised by demographics and psychosocial factors, to inform tailored harm-reduction strategies. Methods: Secondary analysis of the nationally representative Drinkaware Monitor 2023 survey (n = 10,473) identified six clusters of increasing- and high-risk drinkers (n = 2486 weighted). Segmentation was based on drinking occasions and drinking motives using the Drinking Motives Questionnaire for Adults (DMQ-A). Clusters were then characterised using demographic factors, drinking patterns, and psychosocial indicators (dependency symptoms, loneliness, and risk perception), with weighted two-proportion z-tests and false discovery rate correction applied. Results: Six distinct subgroups emerged. Cluster 1 consisted of older drinkers (55+) motivated by product and taste, with fewer dependency symptoms and lower loneliness. Cluster 2 showed lower social motives but greater likelihood of drinking alone at home and with meals. Cluster 3, younger and more diverse, displayed very high social and identity motives (e.g., drinking to feel confident), alongside elevated loneliness and dependency indicators. Cluster 4 also presented strong social motives but had fewer product and taste motives and more adverse outcomes. Cluster 5, typically older and C2DE, reported higher coping motives (e.g., drinking to unwind) and more solitary drinking. Cluster 6 had consistently lower motives, a narrower range of drinking occasions, and a mixed dependency profile. Conclusions: Increasing- and high-risk drinkers are heterogeneous, shaped by different motives and contexts. Tailoring interventions to subgroup profiles, such as socially motivated younger adults and older adults whose drinking is driven by coping motives, may strengthen alcohol harm-reduction strategies.

1. Introduction

Alcohol misuse is a leading cause of premature mortality in the UK, and the second largest individual risk factor for disability-adjusted life years among people aged 15–49 [1]. In 2023, 10,473 deaths were attributed to alcohol-specific causes across the UK, the highest figure since records began [2]. Alcohol-related mortality, which includes partially alcohol-attributable conditions, also rose by 8.8% between 2019 and 2022 [3]. Part of this rise may reflect wider behavioural and service-level changes during the COVID-19 pandemic, including reduced access to healthcare and increases in alcohol consumption among some groups. For example, the 2020 Drinkaware Monitor reported that 11% of drinkers increased their consumption during the pandemic period [4]. The latest data indicates that alcohol-related conditions account for over 486,000 potential years of life lost in England, with rates continuing to rise since 2019 [5].
Alcohol contributes causally to over 200 medical conditions, including cancers and mental health disorders [6], with alcoholic liver disease responsible for nearly three-quarters of alcohol-specific deaths in the UK [7]. The effects of alcohol extend to nearly every internal system, including the brain, liver, pancreas, endocrine, immune and circulatory systems [8]. Classified as a Group 1 carcinogen, the World Health Organisation (WHO) has confirmed that no level of alcohol consumption is safe, though risk increases with dose, frequency and individual health status [9].
Patterns of consumption are typically measured using the Alcohol Use Disorders Identification Test (AUDIT), which provides a risk score reflecting both drinking behaviour and the likelihood of experiencing alcohol-related harm [10]. The latest Health Survey for England reported that 20% of 16–24-year-olds and 14% of 45–54-year-olds were classified as increasing-, higher-risk, or possibly dependent drinkers by the AUDIT [11]. Together, these groups represent almost a quarter of the UK population [12], emphasising the scale of potential harm. Despite this, public health interventions have often focused on dependent drinkers through treatment services or whole-population measures such as pricing and availability, with comparatively less emphasis on the large group of increasing- and high-risk drinkers. For example, minimum unit pricing (MUP) sets a legal lower limit on the price of alcohol, ensuring no drink can be sold below a specified unit threshold [13]. MUP was introduced in Scotland in 2018 (50p per unit) and in Wales in 2020, primarily targeting cheap, high-strength alcohol. Evaluations in Scotland show reductions in alcohol sales, particularly among heavier drinkers, alongside a decline in alcohol-specific hospital admissions and deaths [14,15]. Early evidence from Wales indicates similar effects [16]. These examples highlight the potential of whole-population pricing policies but also emphasise the need for complementary approaches tailored to the heterogeneity of increasing- and high-risk drinkers.
Targeted interventions for higher-risk drinkers, such as Identification and Brief Advice (IBA), are recognised as successful and cost-efficient [17]. IBA consists of a brief, structured conversation in which a health professional screens for hazardous drinking and provides tailored feedback and advice. While effective at the individual level, Public Health England (PHE) emphasises that IBA only achieves meaningful population impact when delivered consistently and at scale. In practice, implementation remains limited, with delivery often inconsistent across services, meaning current provision falls short of population needs [18]. At the same time, alcohol-related hospital admissions remain disproportionately concentrated in more deprived socioeconomic groups [11,17]. This pattern cannot be explained simply by higher consumption in these groups; rather, it reflects the alcohol harm paradox, whereby disadvantaged communities experience greater harm at equivalent or even lower levels of consumption compared with more affluent groups [19]. This highlights the need for strategies that go beyond dependence and address the broader spectrum of harmful alcohol use.
The prevention paradox [20] further illustrates that while high-risk or dependent drinkers face the greatest personal danger, most alcohol-related harm arises from the much larger population of moderate and heavy episodic drinkers [21,22]. For example, O’Dwyer et al. discovered that although dependent drinkers in Ireland had the highest individual risk of harm, 59% of all harms occurred among monthly or occasional heavy episodic drinkers [21,22]. The alcohol harm paradox adds a further dimension: harm is not evenly distributed but is disproportionately borne by those in disadvantaged socioeconomic groups. Together, these paradoxes emphasise that effective alcohol policy must go beyond targeting dependent drinkers alone.
Current UK alcohol-harm-reduction strategies include population-level measures (Chief Medical Officer guidelines, awareness campaigns, price and availability restrictions), primary care targeted interventions (screening, followed by brief advice), and specialist treatment services for dependence [23,24,25]. However, increasing- and high-risk drinkers are a heterogeneous group, and broad messaging may fail to resonate with them.
Segmentation can support the development of more tailored and effective strategies. Earlier analyses of Drinkaware Monitor data grouped drinkers by drinking motivations and occasions but relied on the Drinking Motives Questionnaire—Revised Short Form (DMQ-R SF) [26], a tool developed for an adolescent population, to achieve this. This reliance may have overlooked important drivers of adult drinking. The present study is the first to address this gap by combining drinking occasion data with the Drinking Motives Questionnaire for Adults (DMQ-A), an adult-adapted motives questionnaire [27], in a novel approach to identify six distinct clusters of increasing- and high-risk drinkers [28].
The aim of this study was to define and characterise these clusters, with drinking motives and occasions as the primary factors underpinning segmentation, and additional variables such as demographics, drinking patterns, and adverse outcomes included to provide context. Specifically, the objectives were to (1) describe how drinking motives and occasions vary across clusters compared with the benchmark population of increasing- and high-risk drinkers; (2) explore how demographic and behavioural factors contextualise these clusters; and (3) identify distinguishing features that may inform targeted harm-reduction strategies. It was hypothesised that clusters would show clear and systematic differences, indicating that interventions tailored to motives and drinking contexts could be more effective than generic approaches.

2. Methods

2.1. Study Design

This study undertook a secondary analysis of cross-sectional data from the Drinkaware Monitor 2023, commissioned by Drinkaware and conducted by YouGov [29]. The survey is provided in full in Figure S1.
Participants were recruited via email invitations from YouGov’s established online research panel, which covers a broad range of demographic groups. This allowed YouGov to select a nationally representative sample across all four UK nations. Fieldwork took place in 2023, with responses collected through YouGov’s secure online survey platform, using unique survey links for each participant.
In total, 10,473 individuals completed the survey across England (6948), Wales (1302), Scotland (1565), and Northern Ireland (658). To allow robust analysis, the survey included larger samples from areas of highest deprivation (IMD deciles 1–2; n = 2349) and the lowest (IMD deciles 9–10; n = 2580). The dataset was weighted to reflect the regional, age, and social grade distributions within each UK country, with an additional weight applied to align with the overall UK population. All demographic information used for weighting derived from UK Census information by the Office for National Statistics and social grade definitions from the National Readership Survey. ABC1 represents higher and intermediate managerial, administrative and professional occupations, and C2DE represents skilled manual, semi-skilled and unskilled occupations, including those unemployed [29].

2.2. Participants

The analysis focused on respondents categorised as increasing- or high-risk drinkers according to the AUDIT; AUDIT scores of 0–7 signify low risk, 8–15 increasing risk, 16–19 higher risk, and ≥20 possible dependence [10]. In total, 2464 respondents were classified as increasing- or high-risk drinkers.

2.3. Measures

Segmentation was based on two domains of drinking behaviour: (i) drinking occasions and typical locations (A5_new), and (ii) drinking motivations (A11), assessed using the DMQ-A, which covers social, conformity, and coping motives [27]. Hierarchical clustering using Euclidean distance on scaled data identified six distinct clusters of increasing- and high-risk drinkers. Six clusters were produced after inspection of the clustering dendrogram and cutting at a height of 12. This number of clusters was retained as groups were distinguishable from one another, without the number of clusters becoming excessive. Given the intention of this exploratory study was to examine broad clusters of drinkers with a view to developing personas, a higher number of more distinct clusters was opted against for a smaller number of clusters that would be more practically useful. Of course, more nuanced clusters could be generated by cutting the dendrogram at a lower height—something which could be explored in future work.
Once defined, clusters were profiled against additional variables from the Drinkaware Monitor, including demographics, drinking patterns, symptoms of dependence, adverse outcomes, loneliness, and perceptions of risk, to provide contextual information.
Ordinal survey items were grouped into binary variables for analysis. For example, AUDIT items were coded as positive if participants reported symptoms monthly or more often. Drinking behaviours were recoded using pre-specified thresholds (e.g., weekly or more frequent drinking). Demographic variables were treated as binary indicators (1 = Yes, 0 = No). Full item wordings and coding schemes are provided in Table S3. For drinking prevalence, alcohol units were measured on a “typical” drinking day, with binge drinking characterised as 6 units in a single occurrence (women), or 8 (men) [30].

2.4. Statistical Analysis

All analyses applied survey weights provided by YouGov to align the sample with the UK population by age, sex, region, and social grade [29]. Despite 2464 respondents having met the inclusion criteria, weighting resulted in a slightly larger effective sample (n = 2486), which is used in the results. Cluster sizes therefore reflect weighted counts. Primary analyses focused on differences in drinking motives (captured in the dataset under variable A11) and drinking occasions (variable A5_new) between clusters and the benchmark of all increasing- and high-risk drinkers. Secondary analyses explored demographics (age, sex, social grade, IMD decile), drinking behaviours (frequency, typical units, binge drinking), and contextual indicators (symptoms of dependence, adverse outcomes, loneliness, social pressures, and perceived risk). Differences between cluster and benchmark proportions were analysed using two-proportion z-tests, with Benjamini–Hochberg false discovery rate [31] (FDR) correction applied for multiple comparisons; significance threshold was set at adjusted p < 0.05. All analyses were conducted in R (version 2025.05.1+513).
Survey data were cleaned prior to analysis: categorical responses were re-coded in line with the Drinkaware Monitor questionnaire. Results are reported as weighted percentages, percentage-point differences, and adjusted p-values.

2.5. Ethical Considerations

The Drinkaware Monitor 2023 dataset was anonymised prior to provision for secondary analysis and contained no personally identifiable information. The survey was conducted by YouGov in accordance with the Market Research Society Code of Conduct [32]. As analysis was undertaken on anonymised secondary data, no further ethical approval was required.

2.6. Acknowledgements

The authors acknowledge Drinkaware for providing access to the weighted Drinkaware Monitor 2023 dataset, including pre-segmented cluster outputs. Drinkaware receives voluntary, unrestricted donations from UK alcohol producers, pub operators, restaurants, supermarkets, and other retailers [33].

3. Results

A weighted sample of n = 2486 increasing- and high-risk drinkers was analysed; cluster sizes ranged from n = 140 (Cluster 5) to n = 817 (Cluster 2). Demographic characteristics are presented in Table 1. The benchmark compared against is the total group of all increasing- and high-risk drinkers. Drinking motivations and occasions are shown in Table 2 and Table 3, respectively, with weekly drinking prevalence in Figure 1. Contextual indicators (symptoms of dependence, adverse outcomes, and loneliness) are shown in Figure 2. To avoid overcrowding the main table, 95% confidence intervals, and adjusted p-values are provided in Table S2.
Older respondents were overrepresented in Cluster 1 (aged 55+: 33.7% vs. 26.7%, adjusted p = 0.006; Table 1). Their motives emphasised product and taste (e.g., “because there are certain products you particularly enjoy”: 81.9% vs. 60.5%, adjusted p < 0.001) while social facilitation motives such as “because it makes you more outgoing” were lower (9.7% vs. 33.5%, adjusted p < 0.001; Table 2). They were more likely to drink in almost all occasion contexts apart from “home alone” or during “going out for a couple of drinks in the afternoon” (e.g., “several drinks at home with people in my household”: 65.8% vs. 52.4%, adjusted p < 0.001; Table 3). Weekly drinking prevalence of 4 or more times was also higher than the benchmark (42.8% vs. 34.0%, adjusted p = < 0.001; Figure 1). Cluster 1 were less likely to report feelings of “guilt or remorse after drinking” (12.8% vs. 18.6%, adjusted p = 0.003; Table S2) or “feel that you lack companionship” (37.4% vs. 52.3%, adjusted p < 0.001; Figure 2).
Cluster 2 showed no significant demographic differences versus the benchmark (Table 1). Their drinking motives were significantly lower across every variable, except “to help you unwind,”, “because you like the taste”, “because it is satisfying to have a high-quality drink,” “because it pairs well with food,” and “because there are certain products you particularly enjoy” which did not reach significance (Table 2). They were more likely to drink “at home alone” (54.4% vs. 47.3%, adjusted p = 0.001) and when “going out for a meal” (51.2% vs. 46.3%, adjusted p = 0.021; Table 3). Weekly drinking prevalence of 4 or more occasions was lower (60.6% vs. 67.3%, adjusted p = 0.003; Figure 1), and fewer reported injury related to drinking (2.5% vs. 4.5%, adjusted p = 0.031; Table S2).
Cluster 3 was overrepresented by younger participants (18–34: 46.8% vs. 35.6%, adjusted p < 0.001), and was more ethnically diverse (ethnic minority: 11.4% vs. 6.8%, adjusted p = 0.012). They displayed markedly higher social and identity motives such as “to put you at ease with people” (94.0% vs. 36.2%, adjusted p < 0.001; Table 2) and “because you feel more self-confident and sure of yourself” (92.0% vs. 35.5%, adjusted p < 0.001). Although weekly drinking prevalence of 4 or more occasions was lower (27.9% vs. 34.0%, adjusted p = 0.049; Figure 1), they showed higher likelihood of drinking in almost all occasion contexts, except “at home alone” where prevalence did not differ from the benchmark (Table 3). Additionally, feelings of loneliness were higher in Cluster 3, such as “feel that you lack companionship” (60.3% vs. 52.3%, adjusted p = 0.020; Figure 2).
Together, Clusters 1–3 highlight contrasting patterns: Cluster 1 defined by product and taste motives, Cluster 2 by lower social motives and more solitary drinking, and Cluster 3 by strong social and identity drivers, with wider drinking occasion coverage and higher loneliness.
Cluster 4 skewed younger (18–34: 53.3% vs. 35.6%, adjusted p < 0.001; Table 1), with more non-parents (66.8% vs. 51.1%, adjusted p < 0.001). They reported high social and identity motives (e.g., “because you feel more self-confident and sure of yourself”: 87.1% vs. 35.5%, adjusted p < 0.001; Table 2) but lower product and taste motives (e.g., “because it pairs well with food”: 7.7% vs. 35.4%, adjusted p < 0.001). They were less likely to drink “at home alone” (30.0% vs. 47.3%, adjusted p < 0.001) or “several drinks at home with people in my household” (35.7% vs. 52.4%, adjusted p < 0.001; Table 3). Adverse outcomes were more common (Table S2), with higher reports of drinking-related injuries (11.2% vs. 4.5%, adjusted p < 0.001) and feeling “isolated from others” (71.2% vs. 53.9%, adjusted p < 0.001), with lower perceived alcohol-related health risk (32.0% vs. 45.5%, adjusted p < 0.001). Responses within Cluster 4 were less consistent than in other clusters, with indicators spanning both high social-motivation items and higher levels of emotionally driven outcomes (Table S2).
Cluster 5 was overrepresented by older participants (aged 55+: 42.7% vs. 26.7%, adjusted p < 0.001) of those of C2DE social grade (56.3% vs. 42.0%, adjusted p < 0.001; Table 1). Their motives were lower across most variables, including social and dining-related motives (e.g., “to celebrate a special occasion with friends”: 20.3% vs. 65.0%, adjusted p < 0.001; “because it pairs well with food”: 20.2% vs. 35.4%, adjusted p < 0.001), but higher for coping (e.g., “to help you unwind”: 76.4% vs. 51.4%, adjusted p < 0.001; Table 2). Weekly drinking prevalence of 4 or more times was higher (46.8% vs. 34.0%, adjusted p = 0.006; Figure 1) and they were more likely to drink “at home alone” (69.9% vs. 47.3%, adjusted p < 0.001) but reported lower prevalence across all other occasions (Table 3). They showed higher perceived alcohol-related health risk (59.6% vs. 45.5%, adjusted p = 0.010) and reported less peer pressure (“being asked to explain or justify why you aren’t drinking alcohol”: 14.4% vs. 23.5%, adjusted p = 0.038; Table S2).
Similarly, Cluster 6 also presented older (aged 55+: 37.4% vs. 26.7%, adjusted p = 0.004) and more likely to be social grade C2DE (53.6% vs. 42.0%, adjusted p = 0.005; Table 1). Their motivations were lower across all variables including taste, social, dining-related and coping domains (e.g., “because you like the taste” (34.6% vs. 76.0%, adjusted p < 0.001; “to celebrate a special occasion with friends” (29.7% vs. 65.0%, adjusted p < 0.001; Table 2). Drinking was also less common across every measured occasion, including at home (“several drinks at home with people in my household”: 36.5% vs. 52.4%, adjusted p < 0.001) and at events (22.0% vs. 37.7%, adjusted p < 0.001; Table 3). Adverse consequences included greater reports of an alcohol-related injury (8.5% vs. 4.5%, adjusted p = 0.021), though they were less likely to report “a feeling of guilt or remorse after drinking” (12.5% vs. 18.6%, adjusted p = 0.043; Table S2).
Clusters 4–6 reinforce that while demographic differences are evident, the strongest variation arises in motives and drinking occasions, which underpin differences in symptoms of dependence and related outcomes.

4. Discussion

This study segmented increasing- and high-risk drinkers in the UK into six clusters defined primarily by drinking motives and drinking occasions, with demographic, behavioural, and psychosocial indicators examined to provide context. The findings confirmed that substantial heterogeneity exists within this population, with clusters differing in the reasons they drink and the contexts in which drinking occurs. These motivational and situational differences underpin variation in symptoms of dependency, loneliness, and risk perception, emphasising that harmful drinking is shaped by diverse drivers. Universal strategies remain important, but tailored interventions that account for motive- and context-specific pathways may be more effective.

4.1. Interpretation and Implications

A key contribution of this study is its focus on increasing- and high-risk drinkers, a group often neglected in both research and practice. Public health interventions in the UK typically target either dependent drinkers, through specialist treatment services, or the whole population, through measures such as pricing, availability, and awareness campaigns [14,15,17]. As a result, the large group of drinkers who fall into the increasing- and high-risk categories often receive little tailored attention, despite representing almost a quarter of the population and experiencing substantial alcohol-related harm [11]. The prevention paradox [20] illustrates why this group is so important: although dependent drinkers face the greatest individual danger, most alcohol-related harm arises from the much larger population of increasing- and high-risk drinkers. Recognising the diversity across this group may therefore help inform more responsive and effective policy approaches.
The results highlight the extent of this heterogeneity. Clusters 1 and 3 both consumed alcohol regularly and were significantly more likely than the benchmark to drink across a broad range of occasions. However, the drivers and risks underpinning this behaviour differed greatly. Cluster 1, an older and predominantly white subgroup, was motivated by product and taste, drank more frequently on a weekly basis and often at home with household members, reporting fewer psychosocial vulnerabilities. By contrast, Cluster 3, a younger and more diverse group, was strongly socially motivated, drank more often in out-of-home settings such as events and nights out, and showed higher loneliness, peer pressure, and symptoms of dependence. Whereas Cluster 1’s consumption reflected routine and embedded home-based drinking, Cluster 3’s profile was characterised by socially driven and emotionally vulnerable patterns. This comparison illustrates how groups with similarly broad drinking occasions can nevertheless present very different psychosocial profiles, requiring equally different interventions. Policies that influence product value and marketing (e.g., MUP, labelling, advertising restrictions) may be more relevant to Cluster 1 [34,35,36,37], while younger socially motivated groups like Cluster 3 may benefit from community-based programmes, alcohol-free leisure alternatives, and digital tools that provide non-alcoholic means of social connection [38]. Although both Clusters 3 and 4 were socially motivated, Cluster 3 was more strongly linked with loneliness and peer pressure, whereas Cluster 4 was marked by lower risk perception and higher injury, suggesting different intervention priorities. These patterns also show that motivational and contextual factors can overlap across segments. For example, while Cluster 1 and Cluster 3 differ clearly in age and social context, both display broad drinking occasions arising from distinct underlying drivers. This reinforces that the clusters are heuristic rather than mutually exclusive categories.
Differences were also apparent between Clusters 2 and 5. Both groups preferred to drink alone at home, but their motives and contexts diverged. Cluster 2 showed no demographic differences from the benchmark and reported lower social and enhancement motives, suggesting a risk of normalised but less harmful solitary consumption. Weekly drinking prevalence was not elevated, and adverse outcomes were relatively limited, implying that light-touch strategies such as awareness campaigns about home drinking [39] or unit measures may be appropriate, such as the ‘Know Your Units’ campaign in Northern Ireland [40]. By contrast, Cluster 5 was older and more likely to be of C2DE social grade, combined solitary home drinking with higher coping motives, and showed significantly higher weekly drinking prevalence. This profile points to alcohol use for stress or emotional regulation, indicating a need for more intensive responses such as mindfulness-based support, integrated mental health and alcohol services, or workplace wellbeing programmes [41,42,43]. Thus, even where drinking occasions overlap (e.g., drinking at home), demographic, motivational, and behavioural differences imply the need for tailored intervention strategies.
Clusters 4 and 6 shared relatively low endorsement of product- and taste-related motives and fewer routine drinking occasions (e.g., “several drinks at home with people in my household”), yet both reported higher adverse consequences, underlining different pathways to harm. Cluster 4, a younger subgroup who were less likely to be parents, showed high social and identity motives but low taste and product motives alongside reduced risk perception and lower willingness to cut down. Their drinking occasions were less home-based and more often in out-of-home contexts, but adverse outcomes such as alcohol-related injury and loneliness were more common. This group may benefit from psychosocial interventions such as motivational interviewing, but with messaging that directly addresses the social normalisation of heavy drinking and provides alternative forms of social connection [44]. By contrast, Cluster 6 was older and more likely to be of C2DE social grade, characterised by consistently lower motives across all domains and fewer drinking occasions across contexts. Although adverse outcomes were mixed, with higher reports of alcohol-related injuries but less feelings of “guilt or remorse”, their consistently low endorsement of drinking motives suggests this group may be less responsive to interventions targeting social or coping drivers. Alternative approaches, such as clearer risk communication, proactive clinical screening, and digitally delivered brief interventions, may therefore be more effective [38]. Even within older adults, profiles diverged: Cluster 1’s taste-driven drinking, Cluster 5’s coping-motivated drinking, and Cluster 6’s low-motive profile each imply different intervention needs, underlining that there are distinct differences among older drinkers.
Together, these comparisons highlight that increasing- and high-risk drinkers cannot be treated as a homogeneous group. Even among individuals who drink in the same settings, underlying motives and psychosocial profiles differ, meaning that tailored strategies are essential if interventions are to achieve meaningful impact.
Although both biological sex and gender identity were collected, gender-stratified analyses were not conducted due to the exploratory scope of this segmentation. Given well-established gender differences in drinking motives, future work could examine whether the latent classes or their underlying motivations differ by gender. The survey also included validated wellbeing measures (loneliness, depressive affect, anxiety), but these were excluded to keep the segmentation focused on core motivational and behavioural indicators. Similarly, variables such as financial stress and attitudes toward alcohol policy were omitted to avoid broadening the model beyond its intended scope. Incorporating these contextual and psychosocial factors in future analyses could offer a more integrated understanding of the pressures shaping drinking behaviour.

4.2. International Approaches and Relevance

The multifaceted nature of these clusters highlights the value of tailoring interventions to subgroup characteristics. International evidence offers examples of how motive- and context-sensitive approaches can complement universal policies. Personality-targeted programmes such as PreVenture demonstrate that addressing psychosocial vulnerabilities can reduce binge drinking in adolescents [45,46], suggesting potential for adaptation to adult subgroups defined by coping or social motives. The Icelandic Prevention Model underscores the effectiveness of strengthening community and social infrastructure, though its reliance on high levels of coordination limits transferability [47,48]. Digital tailoring, including apps, may be useful for younger, socially motivated drinkers [49], but are less suitable for older or more deprived groups.
While the present study does not assess intervention effectiveness, the cluster characteristics offer a basis for generating hypotheses about which subgroups may benefit from particular forms of support. For instance, groups with stronger coping-related motives may require approaches focused on emotional regulation and wellbeing, whereas socially driven groups might respond better to peer-oriented messaging or alcohol-free social alternatives. These insights may be relevant for primary care, community programmes, and public health campaigns that aim to move beyond broad, one-size-fits-all messaging.
Implementing segmented approaches in practice presents challenges. Tailored strategies may require greater up-front resources and careful attention to identifying target groups accurately. However, if such approaches improve acceptability and engagement among specific subgroups, the additional investment may be justified. The present segmentation therefore provides a starting point for future work exploring how motive- and occasion-based tailoring could complement existing UK alcohol policies.

4.3. Strengths and Limitations

A key strength of this study is the focus on increasing- and high-risk drinkers, a group typically overlooked in favour of dependent drinkers or the general population. By using motives and occasions as the primary basis for segmentation, this study extends previous work that relied on adolescent-oriented tools [26] or location-only approaches [50] and adds to prior segmentation research on UK drinkers [28]. To the best of our understanding, this study is the first in the UK to apply an adult-validated motives tool (DMQ-A) combined with drinking occasion data to cluster increasing- and high-risk drinkers, providing novel insight into an overlooked group. Examining symptoms of dependency, loneliness, and related outcomes as contextual factors adds nuance without confounding the underlying segmentation.
Several limitations should be acknowledged. The cross-sectional design prevents causal inference, meaning it is not possible to establish whether factors such as loneliness are causes or consequences of harmful drinking, nor to assess how individuals may transition between clusters over time. Alcohol consumption was self-reported, raising the possibility of recall and social desirability biases. Such surveys typically capture only 40–60% of actual consumption when compared with sales data, indicating a high likelihood of under-reporting in the present study [51,52,53]. Online panel recruitment may also under-represent groups with limited digital access. This study uses secondary data, and therefore the selection and measurement of variables were constrained by the existing survey design. Several potentially influential variables such as detailed mental health indicators, income, and cultural background, were not included in this initial exploratory cluster analysis. Similarly, gender-stratified motive analyses and wellbeing-based analyses were not conducted, and this may limit the interpretability of certain clusters. There was notable heterogeneity within some clusters, particularly Cluster 4, which suggests that further sub-structure may exist.

4.4. Future Directions

Future research should investigate whether individuals transition between motive-driven clusters over time. Such transitions may be tied to life-course shifts in roles and responsibilities: prior work demonstrates that alcohol use changes with age and role transitions (e.g., employment, marriage, parenthood) [54], and large longitudinal cohorts show drinking trajectories that peak in early adulthood before declining [55,56]. Evaluating interventions tailored to specific motives and occasions (e.g., stress management for coping-driven drinkers, social alternatives for identity-driven groups) against generic approaches will be essential. Linking segmentation to broader policy levers, such as pricing and availability restrictions, would also clarify how tailored and population-level strategies interact. Such evidence will help translate segmentation into practical tools for alcohol harm reduction.

5. Conclusions

This study identified six clusters of increasing- and high-risk drinkers, defined by drinking motives and occasions and contextualised by demographics and psychosocial outcomes. These ranged from taste-motivated drinkers (Cluster 1), to solitary home drinkers (Cluster 2), socially driven groups (Clusters 3 and 4), and older subgroups with coping or low-motive patterns (Clusters 5 and 6). Comparisons between clusters further demonstrate that even drinkers who consume alcohol in similar contexts (e.g., at home) may differ substantially in their underlying motivations and vulnerabilities, underscoring the heterogeneity of this population.
The segmentation presented here should be interpreted as exploratory and hypothesis-generating rather than as evidence for intervention effectiveness. Nonetheless, the findings highlight potential subgroups that may merit further investigation and point to ways in which universal alcohol policies could be complemented by more tailored approaches aligned with motive- and context-specific pathways.
Importantly, this study emphasises the value of focusing on increasing- and high-risk drinkers, a large but often overlooked population who contribute disproportionately to alcohol-related harm in line with the prevention paradox. Understanding their diversity represents an important step towards developing more nuanced strategies for reducing alcohol-related harm in the UK.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu17233745/s1. Figure S1: Complete Drinkaware Monitor Questionnaire 2023; Table S2: Complete two-proportion z-test results for cluster-benchmark comparisons; Table S3: Question codes and survey wording, alongside coding used for statistical analysis.

Author Contributions

Conceptualisation and Initial Analysis, L.J.B.; Secondary Analysis and Investigation, C.B.; Software, R (version 2025.05.1+513), Resources and Data Curation, L.J.B.; Writing—Original Draft Preparation, C.B.; Writing—Review and Editing, L.J.B., A.O.; Supervision, A.O. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding. Alastair O’Brien receives a proportion of his salary from the UCLH NIHR BRC.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

The authors acknowledge Drinkaware for providing access to the weighted Drinkaware Monitor 2023 dataset, including pre-segmented cluster outputs.

Conflicts of Interest

Drinkaware receives voluntary, unrestricted donations from UK alcohol producers, pub operators, restaurants, supermarkets, and other retailers [33]. No other conflicts to declare.

References

  1. Institute for Health Metrics and Evaluation. Global Health Data Exchange. Institute for Health Metrics and Evaluation, 2021. Available online: https://vizhub.healthdata.org/gbd-results/ (accessed on 28 October 2025).
  2. Office for National Statistics. Alcohol-Specific Deaths in the UK. Office for National Statistics, 2025. Available online: https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/causesofdeath/bulletins/alcoholrelateddeathsintheunitedkingdom/registeredin2023 (accessed on 28 October 2025).
  3. Office for Health Improvement and Disparities. Alcohol Profiles for England: Short Statistical Commentary, February 2024. gov.uk. 2024. Available online: https://www.gov.uk/government/statistics/alcohol-profiles-for-england-february-2024-update/alcohol-profiles-for-england-short-statistical-commentary-february-2024 (accessed on 28 October 2025).
  4. Drinkaware Monitor 2020: Drinking and the Coronavirus Pandemic. Drinkaware.co.uk. 2020. Available online: https://www.drinkaware.co.uk/research/drinkaware-monitors/drinkaware-monitor-2020-drinking-and-the-coronavirus-pandemic (accessed on 28 October 2025).
  5. Office for Health Improvement and Disparities. Alcohol Profile: Short Statistical Commentary, February 2025. gov.uk. 2025. Available online: https://www.gov.uk/government/statistics/alcohol-profile-february-2025-update/alcohol-profile-short-statistical-commentary-february-2025 (accessed on 28 October 2025).
  6. World Health Organisation. Alcohol; World Health Organisation: Geneva, Switzerland, 2024. [Google Scholar]
  7. Office for National Statistics. Alcohol-Specific Deaths in the UK. www.ons.gov.uk. 2024. Available online: https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/causesofdeath/bulletins/alcoholrelateddeathsintheunitedkingdom/registeredin2022 (accessed on 28 October 2025).
  8. National Institute on Alcohol Abuse and Alcoholism. Alcohol’s Effects on the Body. National Institute on Alcohol Abuse and Alcoholism, 2025. Available online: https://www.niaaa.nih.gov/alcohols-effects-health/alcohols-effects-body (accessed on 28 October 2025).
  9. World Health Organisation. No Level of Alcohol Consumption Is Safe for Our Health. World Health Organization, 2023. Available online: https://www.who.int/europe/news/item/04-01-2023-no-level-of-alcohol-consumption-is-safe-for-our-health (accessed on 28 October 2025).
  10. World Health Organisation. AUDIT: The Alcohol Use Disorders Identification Test: Guidelines for Use in Primary Health Care; WHO: Geneva, Switzerland, 2001. [Google Scholar]
  11. NE Digital. Adult Drinking. NHS England, 2024. Available online: https://digital.nhs.uk/data-and-information/publications/statistical/health-survey-for-england/2022-part-1/adult-drinking (accessed on 28 October 2025).
  12. Office for National Statistics. Estimates of the population for the UK, England and Wales, Scotland and Northern Ireland. Office for National Statistics, 2024. Available online: https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/populationestimatesforukenglandandwalesscotlandandnorthernireland (accessed on 28 October 2025).
  13. Population Health Directorate. Alcohol and Drugs: Minimum Unit Pricing. www.gov.scot. 2025. Available online: https://www.gov.scot/policies/alcohol-and-drugs/minimum-unit-pricing (accessed on 28 October 2025).
  14. Public Health Scotland. Evaluating the Impact of Minimum Unit Pricing for Alcohol in Scotland: A Synthesis of the Evidence; Public Health Scotland: Edinburgh, UK, 2023. [Google Scholar]
  15. O’Donnell, A.; Anderson, P.; Jané-Llopis, E.; Manthey, J.; Kaner, E.; Rehm, J. Immediate impact of minimum unit pricing on alcohol purchases in Scotland: Controlled interrupted time series analysis for 2015–18. BMJ 2019, 366, l5274. [Google Scholar] [CrossRef]
  16. Welsh Government. Consultation on Setting the Minimum Price of Alcohol Beyond 2026; Welsh Government: Welsh, UK, 2025.
  17. Public Health England. The Public Health Burden of Alcohol: Evidence Review. gov.uk. 2016. Available online: https://www.gov.uk/government/publications/the-public-health-burden-of-alcohol-evidence-review (accessed on 28 October 2025).
  18. O’Donnell, A.; Kaner, E. Are Brief Alcohol Interventions Adequately Embedded in UK Primary Care? A Qualitative Study Utilising Normalisation Process Theory. Int. J. Env. Res. Public. Health 2017, 14, 350. [Google Scholar] [CrossRef]
  19. Rossow, I.; Bye, E.K. The alcohol harm paradox: Is it valid for self-reported alcohol harms and does hazardous drinking pattern matter? BMC Public. Health 2024, 24, 3053. [Google Scholar] [CrossRef]
  20. Rose, G. Strategy of prevention: Lessons from cardiovascular disease. BMJ 1981, 282, 1847–1851. [Google Scholar] [CrossRef] [PubMed]
  21. Kreitman, N. Alcohol Consumption and the Preventive Paradox. Addiction 1986, 81, 353–363. [Google Scholar] [CrossRef]
  22. O’Dwyer, C.; Mongan, D.; Millar, S.R.; Rackard, M.; Galvin, B.; Long, J.; Barry, J. Drinking patterns and the distribution of alcohol-related harms in Ireland: Evidence for the prevention paradox. BMC Public Health 2019, 19, 1323. [Google Scholar] [CrossRef]
  23. NICE. Alcohol-Use Disorders: Prevention. 2010. Available online: https://www.nice.org.uk/ (accessed on 28 October 2025).
  24. Department of Health and Social Care. Alcohol Consumption: Advice on Low Risk Drinking. gov.uk. 2016. Available online: https://www.gov.uk/government/publications/alcohol-consumption-advice-on-low-risk-drinking (accessed on 28 October 2025).
  25. Office for Health Improvement and Disparities. Alcohol: Applying All Our Health. gov.uk. 2022. Available online: https://www.gov.uk/government/publications/alcohol-applying-all-our-health/alcohol-applying-all-our-health (accessed on 28 October 2025).
  26. Kuntsche, E.; Kuntsche, S. Development and Validation of the Drinking Motive Questionnaire Revised Short Form (DMQ–R SF). J. Clin. Child. Adolesc. Psychol. 2009, 38, 899–908. [Google Scholar] [CrossRef]
  27. D’Aquino, S.; Callinan, S.; Smit, K.; Mojica-Perez, Y.; Kuntsche, E. Why do adults drink alcohol? Development and validation of a Drinking Motives Questionnaire for adults. Psychol. Addict. Behav. 2022, 37, 402. [Google Scholar] [CrossRef] [PubMed]
  28. Drinkaware. Drinkaware Monitor 2023. www.drinkaware.co.uk. 2023. Available online: https://www.drinkaware.co.uk/research/drinkaware-monitors/drinkaware-monitor-2023/ (accessed on 28 October 2025).
  29. YouGov. Drinkaware Monitor 2023 Technical Report. 2023. Available online: https://media.drinkaware.co.uk/media/ttpn0iab/drinkaware-monitor-2023-technical-report.pdf (accessed on 28 October 2025).
  30. Office for National Statistics. Adult Drinking Habits in Great Britain. ons.gov.uk. 2018. Available online: https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/drugusealcoholandsmoking/bulletins/opinionsandlifestylesurveyadultdrinkinghabitsingreatbritain/2017 (accessed on 28 October 2025).
  31. Haynes, W. Benjamini–Hochberg Method; Encyclopedia of Systems Biology; Springer: New York, NY, USA, 2013; p. 78. [Google Scholar]
  32. YouGov. Global Code of Conduct & Ethics. yougov.com. 2022. Available online: https://corporate.yougov.com/documents/277/Global_Code_of_Conduct__Ethics_June_2022_Update_19.07.22.pdf (accessed on 28 October 2025).
  33. Drinkaware. Governance, Funding and Support. www.drinkaware.co.uk. 2025. Available online: https://www.drinkaware.co.uk/about-us/governance-funding-and-support (accessed on 28 October 2025).
  34. Critchlow, N.; Moodie, C.; Gallopel-Morvan, K. Restricting the content of alcohol advertising and including text health warnings: A between—Group online experiment with a non—Probability adult sample in the United Kingdom. Alcohol Clin. Exp. Res. 2024, 48, 1155–1167. [Google Scholar] [CrossRef] [PubMed]
  35. Anderson, P.; de Bruijn, A.; Angus, K.; Gordon, R.; Hastings, G. Impact of Alcohol Advertising and Media Exposure on Adolescent Alcohol Use: A Systematic Review of Longitudinal Studies. Alcohol. Alcohol. 2009, 44, 229–243. [Google Scholar] [CrossRef]
  36. Mohan, G. An evaluation of the impact of a national Minimum Unit Price on alcohol policy on alcohol behaviours. J. Public. Health 2025, 47, e94–e105. [Google Scholar] [CrossRef]
  37. Clay, J.M.; Farkouh, E.K.; Stockwell, T.; Thomas, G.; Johnston, K.; Naimi, T.S. The impact of alcohol minimum pricing policies on vulnerable populations and health equity: A rapid review. Int. J. Drug Policy 2025, 145, 105014. [Google Scholar] [CrossRef]
  38. Kaner, E.F.S.; Beyer, F.R.; Garnett, C.; Crane, D.; Brown, J.; Muirhead, C.; Redmore, J.; O’Donnell, A.; Newham, J.J.; de Vocht, F.; et al. Personalised digital interventions for reducing hazardous and harmful alcohol consumption in community-dwelling populations. Cochrane Database Syst. Rev. 2017, 9, CD011479. [Google Scholar] [CrossRef] [PubMed]
  39. MacLean, S.; Room, R.; Cook, M.; Mugavin, J.; Callinan, S. Affordances of home drinking in accounts from light and heavy drinkers. Soc. Sci. Med. 2022, 296, 114712. [Google Scholar] [CrossRef] [PubMed]
  40. HSC Public Health Agency. ‘Know Your Units’ Living Well campaign launched|HSC Public Health Agency. Available online: https://www.publichealth.hscni.net/news/know-your-units-living-well-campaign-launched (accessed on 28 October 2025).
  41. Garland, E.L.; Howard, M.O. Mindfulness-based treatment of addiction: Current state of the field and envisioning the next wave of research. Addict. Sci. Clin. Pract. 2018, 13, 14. [Google Scholar] [CrossRef] [PubMed]
  42. Dzidowska, M.; Lee, K.S.K.; Wylie, C.; Bailie, J.; Percival, N.; Conigrave, J.H.; Hayman, N.; Conigrave, K.M. A systematic review of approaches to improve practice, detection and treatment of unhealthy alcohol use in primary health care: A role for continuous quality improvement. BMC Fam. Pract. 2020, 21, 33. [Google Scholar] [CrossRef]
  43. Howlett, N.; García-Iglesias, J.; Bontoft, C.; Breslin, G.; Bartington, S.; Freethy, I.; Huerga-Malillos, M.; Jones, J.; Lloyd, N.; Marshall, T.; et al. A systematic review and behaviour change technique analysis of remotely delivered alcohol and/or substance misuse interventions for adults. Drug Alcohol. Depend. 2022, 239, 109597. [Google Scholar] [CrossRef]
  44. Meier, P.S.; Warde, A.; Holmes, J. All drinking is not equal: How a social practice theory lens could enhance public health research on alcohol and other health behaviours. Addiction 2017, 113, 206–213. [Google Scholar] [CrossRef]
  45. Newton, N.C.; Debenham, J.; Slade, T.; Smout, A.; Grummitt, L.; Sunderland, M.; Barrett, E.L.; Champion, K.E.; Chapman, C.; Kelly, E.; et al. Effect of Selective Personality-Targeted Alcohol Use Prevention on 7-Year Alcohol-Related Outcomes Among High-risk Adolescents. JAMA Netw. Open 2022, 5, e2242544. [Google Scholar] [CrossRef]
  46. Newton, N.C.; Conrod, P.J.; Slade, T.; Carragher, N.; Champion, K.E.; Barrett, E.L.; Kelly, E.V.; Nair, N.K.; Stapinski, L.; Teesson, M. The long-term effectiveness of a selective, personality-targeted prevention program in reducing alcohol use and related harms: A cluster randomized controlled trial. J. Child. Psychol. Psychiatry 2016, 57, 1056–1065. [Google Scholar] [CrossRef]
  47. Meyers, C.C.A.; Mann, M.J.; Thorisdottir, I.E.; Garcia, P.R.; Sigfusson, J.; Sigfusdottir, I.D.; Kristjansson, A.L. Preliminary impact of the adoption of the Icelandic Prevention Model in Tarragona City, 2015–2019: A repeated cross-sectional study. Front. Public Health 2023, 11, 1117857. [Google Scholar] [CrossRef]
  48. Kristjansson, A.L.; Mann, M.J.; Sigfusson, J.; Thorisdottir, I.E.; Allegrante, J.P.; Sigfusdottir, I.D. Development and Guiding Principles of the Icelandic Model for Preventing Adolescent Substance Use. Health Promot. Pract. 2019, 21, 62–69. [Google Scholar] [CrossRef] [PubMed]
  49. Garnett, C.; Oldham, M.; Loebenberg, G.; Dinu, L.; Beard, E.; Angus, C.; Burton, R.; Field, M.; Greaves, F.; Hickman, M.; et al. Evaluating the effectiveness of the Drink Less smartphone app for reducing alcohol consumption compared with usual digital care: A comprehensive synopsis from a 6-month follow-up RCT. Public Health Res. 2025, 13, 1–26. [Google Scholar] [CrossRef]
  50. Ally, A.K.; Lovatt, M.; Meier, P.S.; Brennan, A.; Holmes, J. Developing a social practice-based typology of British drinking culture in 2009-2011: Implications for alcohol policy analysis. Addiction 2016, 111, 1568–1579. [Google Scholar] [CrossRef]
  51. Boniface, S.; Shelton, N. How is alcohol consumption affected if we account for under-reporting? A hypothetical scenario. Eur. J. Public. Health 2013, 23, 1076–1081. [Google Scholar] [CrossRef]
  52. Boniface, S.; Kneale, J.; Shelton, N. Drinking pattern is more strongly associated with under-reporting of alcohol consumption than socio-demographic factors: Evidence from a mixed-methods study. BMC Public Health 2014, 14, 1297. [Google Scholar] [CrossRef]
  53. Bellis, M.A.; Hughes, K.; Jones, L.; Morleo, M.; Nicholls, J.; McCoy, E.; Webster, J.; Sumnall, H. Holidays, celebrations, and commiserations: Measuring drinking during feasting and fasting to improve national and individual estimates of alcohol consumption. BMC Med. 2015, 13, 113. [Google Scholar] [CrossRef] [PubMed]
  54. Neve, R.J.M.; Lemmens, P.H.; Drop, M.J. Changes in Alcohol Use and Drinking Problems in Relation to Role Transitions in Different Stages of the Life Course. Subst. Abus. 2000, 21, 163–178. [Google Scholar] [CrossRef]
  55. Britton, A.; Ben-Shlomo, Y.; Benzeval, M.; Kuh, D.; Bell, S. Life course trajectories of alcohol consumption in the United Kingdom using longitudinal data from nine cohort studies. BMC Med. 2015, 13, 47. [Google Scholar] [CrossRef] [PubMed]
  56. Shaw, B.A.; Krause, N.; Liang, J.; McGeever, K. Age differences in long-term patterns of change in alcohol consumption among aging adults. J. Aging Health 2011, 23, 207–227. [Google Scholar] [CrossRef] [PubMed]
Nutrients 17 03745 g001
Figure 1. Weekly drinking prevalence (A1; positive = ≥4 times per week) by cluster against benchmark (all increasing- and high-risk drinkers) as weighted proportions with 95% confidence intervals. * adjusted p < 0.05, ** p < 0.01, *** p < 0.001.
Figure 1. Weekly drinking prevalence (A1; positive = ≥4 times per week) by cluster against benchmark (all increasing- and high-risk drinkers) as weighted proportions with 95% confidence intervals. * adjusted p < 0.05, ** p < 0.01, *** p < 0.001.
Nutrients 17 03745 g001
Nutrients 17 03745 g002aNutrients 17 03745 g002b
Figure 2. Symptoms of dependence (T1; positive = monthly or more), adverse drinking consequences (T4, T6; positive = yes in the past year), and loneliness (MH_5_1; positive = occasionally or more often) significantly higher or lower than the benchmark (all increasing- and high-risk drinkers—indicated by middle bar). * adjusted p < 0.05, ** p < 0.01, *** p < 0.001. Full statistics are provided in Table S2.
Figure 2. Symptoms of dependence (T1; positive = monthly or more), adverse drinking consequences (T4, T6; positive = yes in the past year), and loneliness (MH_5_1; positive = occasionally or more often) significantly higher or lower than the benchmark (all increasing- and high-risk drinkers—indicated by middle bar). * adjusted p < 0.05, ** p < 0.01, *** p < 0.001. Full statistics are provided in Table S2.
Nutrients 17 03745 g002aNutrients 17 03745 g002b
Table 1. Demographics by cluster as weighted proportions (%) against benchmark (all increasing- and high-risk drinkers). * adjusted p < 0.05, ** p < 0.01, *** p < 0.001.
Table 1. Demographics by cluster as weighted proportions (%) against benchmark (all increasing- and high-risk drinkers). * adjusted p < 0.05, ** p < 0.01, *** p < 0.001.
VariableBenchmarkCluster 1Cluster 2Cluster 3Cluster 4Cluster 5Cluster 6
(n = 2486)(n = 622)(n = 817)(n = 344)(n = 315) (n = 140)(n = 248)
GenderMale61.3658.265.5462.4952.1965.8263.09
Female38.6441.834.4637.5147.8134.1836.91
Age18–3435.5527.67 **37.1346.82 ***53.29 ***11.79 ***25.45 ***
35–5437.7138.6636.3439.3634.5945.5637.14
55+26.7333.66 **26.5413.82 ***12.12 ***42.65 ***37.41 **
EthnicityEthnic minority6.773.86 *7.2511.41 *8.533.325.72
White90.3994.52 **89.6684.60 **87.5894.2591.87
Parental statusParent (U18)23.7124.6723.3128.5519.7125.7719.83
Parent (O18)25.5731.95 **26.2212.25 ***15.6 **37.11 *32.09
Non-parent51.0846.349.4558.4866.83 ***36.19 **46.64
Social gradeABC157.9763.3460.558.2855.9243.75 **46.4 **
C2DE42.0336.6639.541.7244.0856.25 **53.6 **
IMD decile1 to 330.5326.2832.2231.728.9628.9636.58
4 to 738.9840.5837.3540.4740.9840.9834.01
8 to 1030.4933.1430.4327.8330.0630.0629.41
Table 2. Drinking motivations (A11; positive = “half of the time” or more) by cluster as weighted proportions (%) against benchmark (all increasing- and high-risk drinkers). * adjusted p < 0.05, ** p < 0.01, *** p < 0.001.
Table 2. Drinking motivations (A11; positive = “half of the time” or more) by cluster as weighted proportions (%) against benchmark (all increasing- and high-risk drinkers). * adjusted p < 0.05, ** p < 0.01, *** p < 0.001.
ResponseBenchmarkCluster 1Cluster 2Cluster 3Cluster 4Cluster 5Cluster 6
Because drinking is part of the fun with family or friends56.476.68 ***39.31 ***89.91 ***67.47 ***10.74 ***27.15 ***
Because drinking adds a certain warmth to social occasions 56.8578.59 ***40.13 ***90.81 ***68.06 ***16.16 ***19.13 ***
To celebrate a special occasion with friends 64.9783.76 ***53.27 ***90.81 ***77.69 ***20.27 ***29.69 ***
To calm down when you are tense30.0124.7 *24.81 **59.32 ***30.5148.59 ***8.76 ***
To help you unwind51.4252.3148.7283.39 ***37.73 ***76.44 ***17.15 ***
To make you more outgoing33.519.66 ***23.87 ***86.06 ***80.63 ***9.98 ***5.91 ***
To overcome shyness30.614.79 ***19.94 ***86.03 ***79.02 ***8.94 ***4.58 ***
Because you feel more self-confident and sure of yourself35.469.2 ***24.48 ***92.02 ***87.09 ***16.3 ***4.54 ***
To put you at ease with people36.2211.62 ***27.1 ***93.96 ***79.73 ***13.43 ***5.74 ***
Because it is satisfying to have a high-quality drink52.1764.84 ***51.3184.93 ***31.7 ***32.16 ***15.22 ***
Because it pairs well with food35.3950.56 ***37.4951.59 ***7.69 ***20.24 ***11.77 ***
Because there are certain products you particularly enjoy60.4781.91 ***56.8085.8 ***35.22 ***52.3120.34 ***
Because you like the taste76.0192.78 ***76.1191.31 ***58.08 ***77.1034.6 ***
Because it makes you happy60.2879.36 ***43.44 ***92.1 ***67.06 *69.79 *9.86 ***
Because it gives you a pleasant feeling66.6684.64 ***53.75 ***93.77 ***69.8979.63 **15.2 ***
Table 3. Drinking occasions (A5_new; positive = “once a week or more” or “1–3 times a month”) by cluster as weighted proportions (%) against benchmark (all increasing- and high-risk drinkers). * adjusted p < 0.05, ** p < 0.01, *** p < 0.001.
Table 3. Drinking occasions (A5_new; positive = “once a week or more” or “1–3 times a month”) by cluster as weighted proportions (%) against benchmark (all increasing- and high-risk drinkers). * adjusted p < 0.05, ** p < 0.01, *** p < 0.001.
ResponseBenchmarkCluster 1Cluster 2Cluster 3Cluster 4Cluster 5Cluster 6
Drinking at home alone47.2942.7554.35 ***51.9530.02 ***69.87 ***38.17 **
A small number of drinks at home with people in my household56.5370.14 ***59.3367.88 ***37.17 ***28.87 ***37.62 ***
Several drinks at home with people in my household52.3665.77 ***53.4061.53 **35.68 ***29.79 ***36.47 ***
Getting together at your or someone else’s house37.2642.86 *37.8447.03 ***34.9211.58 ***25.27 ***
Going out for a meal46.2557.59 ***51.2 *52.1130.5 ***9.41 ***34.23 ***
Evening or night out with friends51.2857.23 *52.2159.58 **51.7717.01 ***40.54 **
Going out for a couple of drinks in the afternoon30.7927.8234.7143.38 ***25.835.86 ***28.28
Drinking at events37.6643.2 *40.2549.28 ***33.167.22 ***22.02 ***
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Bennett, C.; Barratt, L.J.; O’Brien, A. Segmenting Increasing- and High-Risk Alcohol Drinkers by Motives and Occasions: Implications for Targeted Interventions. Nutrients 2025, 17, 3745. https://doi.org/10.3390/nu17233745

AMA Style

Bennett C, Barratt LJ, O’Brien A. Segmenting Increasing- and High-Risk Alcohol Drinkers by Motives and Occasions: Implications for Targeted Interventions. Nutrients. 2025; 17(23):3745. https://doi.org/10.3390/nu17233745

Chicago/Turabian Style

Bennett, Chloe, Liam J. Barratt, and Alastair O’Brien. 2025. "Segmenting Increasing- and High-Risk Alcohol Drinkers by Motives and Occasions: Implications for Targeted Interventions" Nutrients 17, no. 23: 3745. https://doi.org/10.3390/nu17233745

APA Style

Bennett, C., Barratt, L. J., & O’Brien, A. (2025). Segmenting Increasing- and High-Risk Alcohol Drinkers by Motives and Occasions: Implications for Targeted Interventions. Nutrients, 17(23), 3745. https://doi.org/10.3390/nu17233745

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop