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

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.