Biopsychosocial Profile of Chronic Alcohol Users: Insights from a Cross-Sectional Study

TL;DR

This study identifies psychosocial and demographic factors linked to chronic alcohol use and relapse risk in patients undergoing alcohol withdrawal.

Contribution

The study provides a detailed biopsychosocial profile of chronic alcohol users using validated psychometric tools and socio-demographic data.

Findings

Higher alcohol-related consequences were linked to lower education and disadvantaged socio-professional status.

Self-efficacy in refusing alcohol was lower in individuals with co-occurring substance use and nicotine dependence.

DrInC scores were a strong predictor of readmission within three months.

Abstract

Introduction: Chronic alcohol use is a complex condition influenced by psychological, behavioral, and socio-demographic factors. This study aimed to develop a comprehensive psychosocial profile of individuals with alcohol use disorder (AUD) by examining associations between psychometric variables and relapse risk including repeated psychiatric hospitalizations. Methodology: A cross-sectional observational analytical study was conducted on a sample of 104 patients admitted for alcohol withdrawal management at the “Prof. Dr. Al. Obregia” Psychiatric Clinical Hospital in Bucharest between March 2023 and September 2024. Participants completed a set of validated psychometric tools: the Drinker Inventory of Consequences—Lifetime Version (DrInC), Readiness to Change Questionnaire—Treatment Version (RTCQ), Drinking Expectancy Questionnaire (DEQ), and Drinking Refusal Self-Efficacy Questionnaire (DRSEQ). Additional data were collected on the socio-demographic (education level, socio-professional category), genetic (family history of alcohol use), and behavioral factors (length of abstinence, tobacco use, co-occurring substance use disorders). Results: Higher alcohol-related consequence scores (DrInC) were significantly associated with lower education (p < 0.001, η2 = 0.483), disadvantaged socio-professional status (p < 0.001, η2 = 0.514), and family history of alcohol use (p < 0.001, η2 = 0.226). Self-efficacy (DRSEQ) was significantly lower among individuals with co-occurring substance use (p < 0.001) and nicotine dependence (p < 0.001). Logistic regression showed that the DrInC scores significantly predicted readmission within three months (OR = 1.09, p = 0.001). Conclusions: Psychometric tools are effective in identifying individuals at high risk. Personalized, evidence-based interventions tailored to both psychological and socio-professional profiles, combined with structured post-discharge support, are essential for improving long-term recovery and reducing the readmission rates.