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

Abstract

ObjectivesThis paper compares patterns of smoking and high-risk alcohol use across regions in England, and assesses the impact on these of adjusting for sociodemographic characteristics.DesignPopulation survey of 53 922 adults in England aged 16+ taking part in the Alcohol and Smoking Toolkit Studies.MeasuresParticipants answered questions regarding their socioeconomic status (SES), gender, age, ethnicity, Government Office Region, smoking status and completed the Alcohol Use Disorders Identification Test (AUDIT). High-risk drinkers were defined as those with a score of 8 or more (7 or more for women) on the AUDIT.ResultsIn unadjusted analyses, relative to the South West, those in the North of England were more likely to smoke, while those from the East of England, South East and London were less likely. After adjustment for sociodemographics, smoking prevalence was no higher in North East (RR 0.97, p>0.05), North West (RR 0.98, p>0.05) or Yorkshire and the Humber (RR 1.03, p>0.05) but was less common in the East and West Midlands (RR 0.86, p<0.001; RR 0.91, p<0.05), East of England (RR 0.86, p<0.001), South East (RR 0.92, p<0.05) and London (RR 0.85, p<0.001). High-risk drinking was more common in the North but was less common in the Midlands, London and East of England. Adjustment for sociodemographics had little effect. There was a higher prevalence in the North East (RR 1.67, p<0.001), North West (RR 1.42, p<0.001) and Yorkshire and the Humber (RR 1.35, p<0.001); lower prevalence in the East Midlands (RR 0.69, p<0.001), West Midlands (RR 0.77, p<0.001), East of England (RR 0.72, p<0.001) and London (RR 0.71, p<0.001); and a similar prevalence in the South East (RR 1.10, p>0.05)ConclusionsIn adjusted analyses, smoking and high-risk drinking appear less common in ‘central England’ than in the rest of the country. Regional differences in smoking, but not those in high-risk drinking, appear to be explained to some extent by sociodemographic disparities.

Measuring the health patterns of the ‘mixed/multiple’ ethnic group in Britain: data quality problems, reporting issues, and implications for policy

“…Black groups (Fuller 2015). Similar findings were reported for high risk drinking status using the Alcohol Toolkit Studies, the 'Mixed/multiple' group having a relative risk compared with the White group of 0.87; the Asian (0.08), Black (0.25), and Other (0.42) groups were all statistically significantly at lower risk (Beard et al, 2017).…”

“…While there has been increased reporting of smoking prevalence by ethnic group, the use of a single 'mixed' group is a major drawback. For example, using pooled data for England from the Smoking Toolkit Study (2014-16, n=53,922), Beard et al (2017) show that the mixed/multiple group had a greater adjusted risk of smoking (RR 0.96, 95% CI, 0.83-1.11) than the Asian (0.41, 0.38-0.45) or Black (0.40, 0.35-0.46) groups, compared to the White reference group. According to ONS' UK analysis of the 2014 Integrated Household Survey, current smoking rates in the mixed/multiple group were 29.4% for males and 19.2% for females, higher than for any other ethnic group (only Asians being broken down into their 2011 census categories) (ONS 2015).…”

Inequalities in hip fracture incidence are greatest in the North of England: regional analysis of the effects of social deprivation on hip fracture incidence across England

“…9, 10 A North-South divide in smoking prevalence was identified in household population survey data (2014-2016) with rates highest in the North East and lowest in the South East. 11 fractures over this period. 7 We reported that hip fracture incidence has decreased over time in women and increased in men; 7 an observation that has been supported by a recent analysis of UK CPRD data which showed that hip fracture incidence had similarly increased in men but plateaued in women.…”

Genetic Consequences of Social Stratification in Great Britain

“…Besides the known regional differences in income and SES, significant regional differences have been reported for mental 13 and physical 14 health problems. Regional differences in wealth and health are likely linked to each other, [15][16][17] and have been shown to be partly driven by migration. 14,18 If genome-wide complex trait variation is geographically clustered, this should also be taken into account in certain genetically-informative study designs.…”