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Basic models for disease occurrence in epidemiology

W D Flanders1, D G Kleinbaum

  • 1Emory University School of Public Health, Division of Epidemiology, Atlanta, GA 30329, USA.

International Journal of Epidemiology
|February 1, 1995
PubMed
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Epidemiologists often use binomial, Poisson, and exponential distributions for disease occurrence. These models frequently yield similar results, but their application requires careful consideration of independence and risk homogeneity assumptions.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Estimating disease occurrence is fundamental in epidemiology.
  • Commonly used distributions for disease variability include binomial, Poisson, and exponential.
  • A comparative analysis of these distributions in epidemiological contexts is lacking.

Purpose of the Study:

  • To compare and contrast the epidemiological applications of binomial, Poisson, and exponential distributions.
  • To discuss the assumptions, limitations, and results derived from each distribution.

Main Methods:

  • A commentary discussing the key assumptions and limitations of binomial, Poisson, and exponential distributions.
  • Comparison of analytical results obtained using each of the three distributions.

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Main Results:

  • The binomial, Poisson, and exponential distributions often produce similar results in epidemiological analyses.
  • Similar outcomes can generally be expected regardless of which of these three distributions is employed.

Conclusions:

  • Application of these distributions is inappropriate if independence or homogeneity of risks assumptions are violated.
  • These basic distributions can inform the use of other models, such as the Gaussian distribution, for disease-exposure studies.