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. 2018 Sep 21;10(2):e216.
doi: 10.5210/ojphi.v10i2.9357. eCollection 2018.

A Causally Naïve and Rigid Population Model of Disease Occurrence Given Two Non-Independent Risk Factors

Olaf Dammann  1   2 , Kenneth Chui  1 , Anselm Blumer  2
Affiliations

A Causally Naïve and Rigid Population Model of Disease Occurrence Given Two Non-Independent Risk Factors

Olaf Dammann et al. Online J Public Health Inform. .

Abstract

We describe a computational population model with two risk factors and one outcome variable in which the prevalence (%) of all three variables, the association between each risk factor and the disease, as well as the association between the two risk factors is the input. We briefly describe three examples: retinopathy of prematurity, diabetes in Panama, and smoking and obesity as risk factors for diabetes. We describe and discuss the simulation results in these three scenarios including how the published information is used as input and how changes in risk factor prevalence changes outcome prevalence.

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Figures

Figure 1
Figure 1
The associations among two non-independent risk factors and one outcome are quantified by three odds ratios.
Figure 2
Figure 2
Fourfold table depicting the four entities defined by the presence (+) or absence (-) of a binary risk factor and an outcome.
Figure 3
Figure 3
Simulation results of Step 1 in example #1, retinopathy of prematurity.
Figure 4
Figure 4
Simulation results of Step 1 in example #2, diabetes in Panama.
See this image and copyright information in PMC

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