Socioeconomic status as an effect modifier of alcohol consumption and harm: analysis of linked cohort data

Srinivasa Vittal Katikireddi, Elise Whitley, Jim Lewsey, Linsay Gray, Alastair H Leyland, Srinivasa Vittal Katikireddi, Elise Whitley, Jim Lewsey, Linsay Gray, Alastair H Leyland

Abstract

Background: Alcohol-related mortality and morbidity are high in socioeconomically disadvantaged populations compared with individuals from advantaged areas. It is unclear if this increased harm reflects differences in alcohol consumption between these socioeconomic groups, reverse causation (ie, downward social selection for high-risk drinkers), or a greater risk of harm in individuals of low socioeconomic status compared with those of higher status after similar consumption. We aimed to investigate whether the harmful effects of alcohol differ by socioeconomic status, accounting for alcohol consumption and other health-related factors.

Methods: The Scottish Health Surveys are record-linked cross-sectional surveys representative of the adult population of Scotland. We obtained baseline demographics and data for alcohol consumption (units per week and binge drinking) from Scottish Health Surveys done in 1995, 1998, 2003, 2008, 2009, 2010, 2011, and 2012. We matched these data to records for deaths, admissions, and prescriptions. The primary outcome was alcohol-attributable admission or death. The relation between alcohol-attributable harm and socioeconomic status was investigated for four measures (education level, social class, household income, and area-based deprivation) using Cox proportional hazards models. The potential for alcohol consumption and other risk factors (including smoking and body-mass index [BMI]) mediating social patterning was explored in separate regression models. Reverse causation was tested by comparing change in area deprivation over time.

Findings: 50 236 participants (21 777 men and 28 459 women) were included in the analytical sample, with 429 986 person-years of follow-up. Low socioeconomic status was associated consistently with strikingly raised alcohol-attributable harms, including after adjustment for weekly consumption, binge drinking, BMI, and smoking. Evidence was noted of effect modification; for example, relative to light drinkers living in advantaged areas, the risk of alcohol-attributable admission or death for excessive drinkers was increased (hazard ratio 6·12, 95% CI 4·45-8·41 in advantaged areas; and 10·22, 7·73-13·53 in deprived areas). We found little support for reverse causation.

Interpretation: Disadvantaged social groups have greater alcohol-attributable harms compared with individuals from advantaged areas for given levels of alcohol consumption, even after accounting for different drinking patterns, obesity, and smoking status at the individual level.

Funding: Medical Research Council, NHS Research Scotland, Scottish Government Chief Scientist Office.

Figures

Figure 1
Figure 1
Risks of alcohol-attributable harms by alcohol consumption and socioeconomic status Adjusted for age, sex, study wave, smoking, body-mass index, and binge drinking in the past week. SES=socioeconomic status.
Figure 2
Figure 2
Predicted probability of an alcohol-attributable event during follow-up, stratified by socioeconomic status All models were adjusted for age, sex, study wave, smoking, body-mass index, and binge drinking in the past week. The lines represent the predicted probability of experiencing an alcohol-attributable event (estimated from logistic regression models) and the shading denotes the 95% CI. (A) Deprivation was categorised according to the most deprived two quintiles vs the least deprived three quintiles. (B) Social class categorisation was based on manual vs non-manual occupations. (C) Education was categorised according to attainment of none, other school, or Scottish standard grade qualifications vs Scottish higher grade, higher national certificate, higher national diploma, or degree or above. (D) Household income was categorised by the lowest two quintiles vs highest three quintiles. Follow-up for household income measures was shorter because participants were not asked about this information in the first two study waves of data collection.

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Source: PubMed

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