The regional geography of alcohol consumption in England: Comparing drinking frequency and binge drinking

Abstract

Alcohol consumption frequency and volume are known to be related to health problems among drinkers. Most of the existing literature that analyses regional variation in drinking behaviour uses measures of consumption that relate only to volume, such as 'binge drinking'. This study compares the regional association of alcohol consumption using measures of drinking frequency (daily drinking) and volume (binge drinking) using a nationally representative sample of residents using the Health Survey for England, 2011-2013. Results suggest the presence of two differentiated drinking patterns with relevant policy implications. We find that people in northern regions are more likely to binge drink, whereas people in southern regions are more likely to drink on most days. Regression analysis shows that regional variation in binge drinking remains strong when taking into account individual and neighbourhood level controls. The findings provide support for regional targeting of interventions that aim to reduce the frequency as well as volume of drinking.

Healthier central England or North–South divide? Analysis of national survey data on smoking and high-risk drinking

“…For example, previous studies suggest that regional variations differ according to the measure of alcohol consumption used (eg, volume vs frequency). 31 Finally, although these findings relate to England specifically, which has the largest difference in economic output between regions of any country in Europe, it is most likely that comparable variation would be found in other countries. 34 In conclusion, smoking and high-risk drinking appear to be less common in 'central England' than in the rest of the country.…”

“…The pattern is somewhat different for frequency of drinking, with those in Southern regions more likely to report that they drink on most days. 31 Data from the British Health and Lifestyle Survey noted that individual characteristics have an independent effect on neighbourhood variations in smoking but that significant between-ward differences in smoking behaviour remain which cannot be explained either by population composition or ward-level deprivation. 32 However, there are several issues with these previous studies.…”

Exploring the characteristics of newly defined at-risk drinkers following the change to the UK low risk drinking guidelines: a retrospective analysis using Health Survey for England data

“…A number of demographic characteristics were identified from the literature as potentially impacting upon alcohol consumption levels and the corresponding measures from the HSE were selected for the analysis to establish whether at-risk male drinkers were distinct from low risk male drinkers under both the previous and the new drinking guidelines and to establish whether the change in guidelines affected the demographic characteristics associated with being an at-risk drinker. Demographic characteristics that have been associated with differing levels of alcohol consumption and were included in the analysis are: age [43], grouped from 16 to 34, 35–54, 55–74 and 75+; social class [44, 45], grouped using the National Statistics Socio-Economic Classification (NS-SEC) categorising the employment status of the participant (managerial and professional, intermediate, routine and manual, not classified); marital status [46, 47] grouped as single, married/cohabiting, separated/divorced/widowed; geographical region [6, 48–50], grouped by former Government Office Region; ethnicity [51, 52], regrouped into white and non-white groups due to small sample sizes in the non-white groups; smoking status [53], grouped by never smoker, ex-occasional smoker, ex-regular smoker, current smoker; and physical health [54, 55] measured as limiting long-lasting illness, non-limiting long-lasting illness, no long-lasting illness. The full HSE questionnaire (2015) can be accessed online [42].…”

A Bayesian Spatio-Network Model for Multiple Adolescent Adverse Health Behaviours

“…The modelling approaches used to quantify the effects of these drivers on adverse health behaviours are typically based around generalised linear models (GLMs) and extensions thereof. For example, Castillo et al (2017) used a GLM to study whether regional targeting of interventions that aim to reduce the frequency as well as volume of drinking may be effective, while Fujimoto and Valente (2015) sought to provide insight into how adolescent health behaviour is predicated. In contrast, multiple membership multiple classification (MMMC, Browne et al, 2001) approaches extend GLMs by explicitly modelling network effects via random effects representing peer influence in the model.…”

“…Additionally, they typically consider only a single health behaviour, and thus are not able to quantify the similarities in these behaviours and their drivers. For example, Castillo et al (2017) ignores the spatial dependence that may be present in the data, while Lorant and Tranmer (2019) only consider one health behaviour at a time. This study thus proposes a novel spatio-network modelling approach for inter-related adolescent health behaviours, which for the first time enables the relative importance of individual factors, friendship effects and spatial effects on multiple health behaviours to be quantified.…”