Drinking Patterns and Myocardial Infarction: A Linear Dose–Response ModelAlcoholism: Clinical and Experimental Research Published Online: 20 Nov 2008
The relation of alcohol intake to cardiovascular health is complex, involving both protective and harmful effects, depending on the amount and pattern of consumption. Interpretation of data available on the nature of these relations is limited by lack of well-specified, mathematical models relating drinking patterns to alcohol-related consequences. Here we present such a model and apply it to data on myocardial infarction (MI).
The dose–response model derived assumes: (1) each instance of alcohol use has an effect that either increases or decreases the likelihood of an alcohol-related consequence, and (2) greater quantities of alcohol consumed on any drinking day add linearly to these increases or decreases in risk. Risk was reduced algebraically to a function of drinking frequency and dosage (volume minus frequency, a measure of the extent to which drinkers have more than 1 drink on days when they drink). In addition to estimating the joint impact of frequency and dosage, the model provides a method for calculating the point at which risk related to alcohol consumption is equal to background risk from other causes. A bootstrapped logistic regression based on the dose–response model was conducted using data from a case-control study to obtain the predicted probability of MI associated with current drinking patterns, controlling for covariates.
MI risk decreased with increasing frequency of drinking, but increased as drinking dosage increased. Rates of increasing MI risk associated with drinking dosage were twice as high among women as they were among men. Relative to controls, lower MI risk was associated with consuming <>
Use of a well-specified mathematical dose–response model provided precise estimates for the first time of how drinking frequency and dosage each contribute linearly to the overall impact of a given drinking pattern on MI risk in men and women.
Request Reprint E-Mail: russell@prev.org
_________________________________________________________________
