Socioeconomic position indicators and risk of alcohol-related medical conditions: A national cohort study from Sweden

Abstract

Background: Alcohol consumption contributes to excess morbidity and mortality in part through the development of alcohol-related medical conditions (AMCs, including alcoholic cardiomyopathy, hepatitis, cirrhosis, etc.). The current study aimed to clarify the extent to which risk for these outcomes differs as a function of socioeconomic position (SEP), as discrepancies could lead to exacerbated health disparities.

Methods and findings: We used longitudinal Swedish national registries to estimate the individual and joint associations between 2 SEP indicators, educational attainment and income level, and risk of AMC based on International Classification of Diseases codes, while controlling for other sociodemographic covariates and psychiatric illness. We conducted Cox proportional hazards models in sex-stratified analyses (N = 1,162,679 females and N = 1,196,659 males), beginning observation at age 40 with follow-up through December 2018, death, or emigration. By the end of follow-up, 4,253 (0.37%) females and 11,183 (0.93%) males had received an AMC registration, corresponding to overall AMC incidence rates among females and males of 2.01 and 5.20, respectively. In sex-stratified models adjusted for birth year, marital status, region of origin, internalizing and externalizing disorder registrations, and alcohol use disorder (AUD) registration, lower educational attainment was associated with higher risk of AMC in both females (hazard ratios [HRs] = 1.40 to 2.46 for low- and mid-level educational attainment across 0 to 15 years of observation) and males (HRs = 1.13 to 1.48). Likewise, risk of AMC was increased for those with lower income levels (females: HRs = 1.10 to 5.86; males: HRs = 1.07 to 6.41). In secondary analyses, we further adjusted for aggregate familial risk of AUD by including family genetic risk scores for AUD (FGRSAUD), estimated using medical, pharmacy, and criminal registries in extended families, as covariates. While FGRSAUD were associated with risk of AMC in adjusted models (HR = 1.17 for females and HR = 1.21 for males), estimates for education and income level remained largely unchanged. Furthermore, FGRSAUD interacted with income level, but not education level, such that those at higher familial liability to AUD were more susceptible to the adverse effect of low income. Limitations of these analyses include the possibility of false negatives for psychiatric illness registrations, changes in income after age 40 that were not accounted for due to modeling restrictions, restriction to residents of a high-income country, and the inability to account for individual-level alcohol consumption using registry data.

Conclusions: Using comprehensive national registry data, these analyses demonstrate that individuals with lower levels of education and/or income are at higher risk of developing AMC. These associations persist even when accounting for a range of sociodemographic, psychiatric, and familial risk factors. Differences in risk could contribute to further health disparities, potentially warranting increased screening and prevention efforts in clinical and public health settings.

Fig 1. HRs between education level and/or income with risk of AMCs, across time, from Models 1A/1B–Model 4.

Models 1A and 1B include education level or income, respectively, as the primary predictors of interest, and are adjusted for birth year, marital status, and region of origin. Model 2 includes both education and income level with the aforementioned covariates. Model 3 further adjusts for internalizing and externalizing registrations. Model 4 further adjusts for AUD registrations. Results for females are presented in the top panel and for males in the bottom panel. The horizontal dashed gray line at an HR of 1 represents the null hypothesis. AMC, alcohol-related medical condition; AUD, alcohol use disorder; HR, hazard ratio.