Gender-specific predictors of risky alcohol use among general hospital inpatients

Gen Hosp Psychiatry. 2013 Jan-Feb;35(1):9-15. doi: 10.1016/j.genhosppsych.2012.08.002. Epub 2012 Nov 6.

Abstract

Objective: To investigate sociodemographic-, living situation- and substance-related variables as predictors of current risky alcohol use for both female and male general hospital inpatients.

Method: The sample of 6050 female and 8282 male general hospital inpatients was recruited in 2002-2004. Using the Alcohol Use Disorder Identification Test-Consumption, they were assigned to four drinking groups: abstinent, moderate use, slightly increased use and notably increased use. Gender-specific predictors of group affiliation were determined using multivariate multinomial logistic regressions.

Results: In both genders, younger age, rural living environment, the occurrence of lifetime alcohol use disorders (AUDs) and current tobacco smoking were positively associated with risky alcohol use. Higher education was positively associated with slightly and notably increased use for women. Living alone, being divorced/ widowed and being unemployed (relative risk ratios=1.4-1.7) were positively associated with notably increased use for men. In both genders, older age, less education and the occurrence of lifetime AUDs were positively associated with abstinence.

Conclusions: Higher educated women are likely to report risky alcohol use. Marriage may have a protective effect on level of alcohol use for men only. In addition to the implementation of routine alcohol screening, the examined data may provide cost-effective information that could be used to tailor interventions.

Publication types

  • Clinical Trial
  • Multicenter Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Age Factors
  • Alcohol Drinking / epidemiology*
  • Alcohol-Related Disorders / epidemiology*
  • Educational Status
  • Female
  • Germany / epidemiology
  • Hospitals, General
  • Humans
  • Inpatients / statistics & numerical data*
  • Logistic Models
  • Male
  • Marital Status / statistics & numerical data
  • Middle Aged
  • Prospective Studies
  • Residence Characteristics / statistics & numerical data
  • Risk-Taking*
  • Rural Population / statistics & numerical data
  • Sex Factors
  • Smoking / epidemiology
  • Young Adult