Methodologic issues in hospital epidemiology. IV. Risk ratios, confounding, effect modification, and the analysis of multiple variables.
Most information in hospital epidemiology comes from observational studies of hospitalized patients, not from planned experiments. Data from observational studies have frequently been used to investigate the effect of exposure to a single factor as a cause or determinant of a discrete outcome, such as infection or death. In such observational studies, the characteristics of study patients may vary widely even within a single hospital. In these investigations, extraneous variables such as differing severity of underlying illness, acting in concert with the exposure variable being studied, may distort the apparent effect of the exposure on the outcome through confounding or may modify the effect of the exposure on the outcome without distorting the relation between them. The data from six published studies are reanalyzed through the use of stratification by severity of underlying illness in order to demonstrate the impact of confounding and effect modification by a third, extraneous variable. Simple methods for dealing with confounding and effect modification in data from hospital epidemiology are presented.
Freeman
J
J
Hospital Epidemiology Unit, Channing Laboratory, Boston, Massachusetts 02115.
Goldmann
D A
DA
McGowan
J E
JE
D000704:Analysis of Variance; D003428:Cross Infection / Q000453:epidemiology*; D004812:Epidemiologic Methods; D006801:Humans; D012306:Risk