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

Authors

  • Olaf Dammann Tufts Public Health
  • Kenneth Chui
  • Anselm Blumer

DOI:

https://doi.org/10.5210/ojphi.v10i2.9357

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.

Author Biography

Olaf Dammann, Tufts Public Health

Professor and Vice Chair
Dept. of Public Health & Community Medicine
Tufts University School of Medicine

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Published

2018-09-21

How to Cite

Dammann, O., Chui, K., & Blumer, A. (2018). A Causally Naïve and Rigid Population Model of Disease Occurrence Given Two Non-Independent Risk Factors. Online Journal of Public Health Informatics, 10(2). https://doi.org/10.5210/ojphi.v10i2.9357

Issue

Section

Capstones and Working Papers