Predictors of alcohol misuse and abuse in older women

J Nurs Scholarsh. 2005;37(4):329-35. doi: 10.1111/j.1547-5069.2005.00057.x.


Purpose: To determine the predictive ability of self-report questions, physical measures, and biomarkers to detect alcohol misuse and abuse among older women.

Design and methods: Healthy women volunteers age 60 and older who fit selection criteria were enrolled. The 135 participants were divided into nondrinkers (ND; n = 63) and drinkers (D; n = 72) based on self-reports of quantity and frequency of standard drinks consumed per month. The mean ages for the groups were 69.2 (ND) and 69.6 (D).

Findings: The best predictor was a score >0 on the T-ACE, a four-item instrument to detect alcohol abuse. Other significant predictors were: (a) behaviors: smoking, mixing over-the-counter (OTC) drugs with alcohol, heavy coffee drinking, using alcohol to sleep, and less sleep latency; and (b) biomarkers: higher mean corpuscular volume (MCV), hemoglobin (Hgb), hematocrit (Hct), and high-density lipoprotein cholesterol (HDL). The heaviest drinker subgroup had more physical stigmata, including broken blood vessels in nose and larger liver spans.

Conclusions: The "best predictor model" showed that older women who were at risk for alcohol misuse or abuse had T-ACE scores of 1 or higher, used two or more OTC drugs regularly, drank large amounts of coffee, used alcohol to fall asleep, and had less sleep latency. Because positive T-ACE scores have high sensitivity and specificity for alcohol abuse, scores of 1 or greater should be addressed in clinical settings, e.g., referrals for more definitive diagnoses and relevant treatment.

Publication types

  • Multicenter Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Aged, 80 and over
  • Alcoholism / prevention & control*
  • Blood Chemical Analysis
  • Case-Control Studies
  • Female
  • Geriatric Assessment
  • Humans
  • Logistic Models
  • Mass Screening / methods*
  • Middle Aged
  • Multivariate Analysis
  • Predictive Value of Tests
  • Psychological Tests
  • Risk-Taking