AIMS: This is the third of a set of three papers evaluating drinking status and mortality risk. Analysis of three general population surveys of women evaluated all-cause mortality rates by drinking pattern. DESIGN AND PARTICIPANTS: Raw data from three studies of adult women were evaluated. Logistic regression models controlled for confounding characteristics. Meta-analysis combined study results. MEASUREMENTS: Drinking pattern was alternatively defined by quantity, frequency and volume of drinking. Final models included drinking pattern (including long-term abstainers and former drinkers) as well as age and other confounding variables. Models also evaluated interactions of age and, respectively, long-term abstinence and former drinking. FINDINGS: In models in which age was controlled, odds of death for long-term abstainers and former drinkers (defined by volume or quantity) were greater than those for light drinkers; odds of death for moderate and heavy drinkers (defined by quantity) were greater than those for light drinkers. When other psychosocial attributes were controlled, odds of death were similar for abstainers and light drinkers. When other psychosocial attributes were controlled, odds of death for heavy drinkers (defined by volume and quantity) were greater than those for light drinkers. When interactions of age and the two forms of abstinence were introduced, one study showed a significant effect of age and former drinking. CONCLUSIONS: Results were consistent with the hypothesis that characteristics of abstainers other than their non-use of alcohol may account for their higher mortality risk. With the exception of former drinkers compared to light drinkers, when interactions were introduced into models (for measures of quantity and frequency) findings were homogeneous across studies, lending generalizability to results.
AIMS: This is the third of a set of three papers evaluating drinking status and mortality risk. Analysis of three general population surveys of women evaluated all-cause mortality rates by drinking pattern. DESIGN AND PARTICIPANTS: Raw data from three studies of adult women were evaluated. Logistic regression models controlled for confounding characteristics. Meta-analysis combined study results. MEASUREMENTS: Drinking pattern was alternatively defined by quantity, frequency and volume of drinking. Final models included drinking pattern (including long-term abstainers and former drinkers) as well as age and other confounding variables. Models also evaluated interactions of age and, respectively, long-term abstinence and former drinking. FINDINGS: In models in which age was controlled, odds of death for long-term abstainers and former drinkers (defined by volume or quantity) were greater than those for light drinkers; odds of death for moderate and heavy drinkers (defined by quantity) were greater than those for light drinkers. When other psychosocial attributes were controlled, odds of death were similar for abstainers and light drinkers. When other psychosocial attributes were controlled, odds of death for heavy drinkers (defined by volume and quantity) were greater than those for light drinkers. When interactions of age and the two forms of abstinence were introduced, one study showed a significant effect of age and former drinking. CONCLUSIONS: Results were consistent with the hypothesis that characteristics of abstainers other than their non-use of alcohol may account for their higher mortality risk. With the exception of former drinkers compared to light drinkers, when interactions were introduced into models (for measures of quantity and frequency) findings were homogeneous across studies, lending generalizability to results.
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Keywords:
Alcohol Drinking--women; Americas; Behavior; Biology; Body Weight--women; Causes Of Death; Comparative Studies; Demographic Factors; Developed Countries; Economic Factors; Educational Status--women; Health; Minority Groups--women; Mortality; Mortality Determinants; North America; Northern America; Physiology; Population; Population Characteristics; Population Dynamics; Research Methodology; Risk Factors; Socioeconomic Factors; Socioeconomic Status; Studies; United States
Authors: Shelly-Ann M Love; Kari E North; Donglin Zeng; Natalia Petruski-Ivleva; Anna Kucharska-Newton; Priya Palta; Mariaelisa Graff; Laura Loehr; Sarah B Jones; Gerardo Heiss Journal: Am J Epidemiol Date: 2020-08-01 Impact factor: 4.897