BASIC MODELS FOR DISEASE OCCURRENCE IN EPIDEMIOLOGY

Background. One of the epidemiologist's most basic tasks is estimation of disease occurrence. To perform this task, the epidemiologist frequ ently models variability in disease occurrence using one of three dist ributions-the binomial, the Poisson or the exponential distribution. A lthough epidemiologists often use them and their properties appear in standard texts, we know of no text or review that compares and contras ts epidemiological application of these distributions. Methods. In thi s commentary, we discuss these three basic distributions. We note key assumptions as well as limitations, and compare results from analyses based on each distribution. Results and Conclusions. We illustrate tha t the three distributions, although superficially different, often lea d to similar results. We argue that epidemiologists should often obtai n similar results regardless of which distribution they use. We also p oint out that application of all three distributions can be inappropri ate if assumptions of independence or homogeneity of risks fail to hol d. Finally, we briefly review how these basic distributions can be use d to justify use of other distributions, such as the Gaussian distribu tion, for studying disease-exposure associations.