title: Longitudinal patterns and predictors of alcohol consumption in the United States.
abstract: OBJECTIVE
  We examined demographic predictors of longitudinal patterns in alcohol consumption.
  
  
  METHODS
  We used mixed-effects models to describe individual alcohol consumption and change in consumption with age, as well as the associations between consumption and birth year, national alcohol consumption, and demographic factors, among 14 105 adults from the National Health and Nutrition Examination Survey I Epidemiologic Follow-Up Study.
  
  
  RESULTS
  Alcohol consumption declined with increasing age, and individual consumption mirrored national consumption. Higher consumption was associated with male gender, being White, being married, having a higher educational level, having a higher income, being employed, and being a smoker. Faster age-related decline in consumption was associated with earlier cohorts, being male, being married, having a lower educational level, and being a smoker.
  
  
  CONCLUSIONS
  Compared with alcohol consumption among earlier cohorts, that among recent cohorts declined more slowly with increasing age, suggesting that negative health effects of alcohol could increase in the future.
authors.affiliation: David Geffen School of Medicine at UCLA, Division of Geriatrics, 10945 Le Conte Ave, Suite 2339, Los Angeles, CA 90095-1687, USA. aamoore@mednet.ucla.edu
authors.forename: Alison A
authors.initials: AA
authors.lastname: Moore
authors.forename: Robert
authors.initials: R
authors.lastname: Gould
authors.forename: David B
authors.initials: DB
authors.lastname: Reuben
authors.forename: Gail A
authors.initials: GA
authors.lastname: Greendale
authors.forename: M Kallin
authors.initials: MK
authors.lastname: Carter
authors.forename: Kefei
authors.initials: K
authors.lastname: Zhou
authors.forename: Arun
authors.initials: A
authors.lastname: Karlamangla
mesh_terms: D000328:Adult; D017677:Age Distribution; D000368:Aged; D000428:Alcohol Drinking / Q000453:epidemiology* / Q000523:psychology; D015988:Cohort Effect; D004522:Educational Status; D004651:Employment / Q000706:statistics & numerical data; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D007182:Income / Q000706:statistics & numerical data; D016014:Linear Models; D008137:Longitudinal Studies; D008297:Male; D017533:Marital Status / Q000706:statistics & numerical data; D008875:Middle Aged; D015999:Multivariate Analysis; D009749:Nutrition Surveys; D011159:Population Surveillance; D011237:Predictive Value of Tests; D012307:Risk Factors; D017678:Sex Distribution; D012907:Smoking / Q000453:epidemiology; D014481:United States / Q000453:epidemiology; D044465:White People / Q000523:psychology / Q000706:statistics & numerical data
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