International Journal of Epidemiology, Vol 26, 220-223, Copyright © 1997 by International Epidemiological Association
C Zocchetti, D Consonni and PA Bertazzi
BACKGROUND: Cross-sectional data are frequently encountered in epidemiology
and published results are predominantly presented in terms of prevalence
odds ratios (POR). A recent debate suggested a switch from POR, which is
easily obtained via logistic regression analysis available in many
statistical packages, to prevalence rate ratios (PRR). We thought it useful
to explore the mathematical relationship between PRR and POR and to
evaluate the degree of divergence of the two measures as a function of the
prevalence of disease and exposure. METHODS: With the use of some algebra
and the common definitions of prevalence of the disease (Pr(D)), prevalence
of the exposure (Pr(E)), PRR, and POR in a 2 x 2 table, we have identified
a useful formula that represents the mathematical relationship between
these four quantities. Plots of POR versus PRR for selected values of Pr(D)
and Pr(E) are reported. RESULTS: Mathematically speaking the general
relationship takes the form of a second order curve which can change
curvature and/or rotate around the point POR = PRR = 1 according to the
values of Pr(D) and Pr(E), with POR being always further from the null
value than is PRR. The discrepancies are much more influenced by variations
in Pr(D) than in Pr(E). CONCLUSIONS: We think that the choice between POR
or PRR in a cross-sectional study ought to be based on epidemiological
grounds and not on the availability of software tools. The paper offers a
formula and some-examples for a better understanding of the relationship
between PRR and POR as a function of the prevalence of the disease and the
prevalence of the exposure.
ARTICLES
Relationship between prevalence rate ratios and odds ratios in cross- sectional studies
Clinica del Lavoro, Luigi Devoto, Instituti Clinici di Perfezionamento, Milan, Italy.
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