Modeling the effects of biologically incorporated radionuclides and chronic low-dose ionizing radiation exposure on both cancer and chronic non-cancer diseases.
Efforts to model the health effects of low-dose ionizing radiation (IR) have often focused on cancer. Meanwhile, significant evidence links IR and age-associated non-cancer diseases. Modeling of such complex processes, which are not currently well understood, is a challenging problem. In this paper we briefly overview recent successful attempts to model cancer on a population level and propose how those models may be adapted to include the impact of IR and to describe complex non-cancer diseases. We propose three classes of models which we believe are well suited for the analysis of the health effects in human populations exposed to low-dose IR. These models use biostatistical/epidemiological techniques and mathematical formulas describing the biological mechanisms of the impact of IR on human health. They can combine data from multiple sources and from distinct levels of biological/population organization. The proposed models are intrinsically multivariate and non-linear and capture the dynamic aspects of health change.
Manton
K G
KG
Center for Demographic Studies, Duke University, Durham, NC 27708, USA. kgmanton@duke.edu
Kulminski
A
A
Akushevich
I
I
Kravchenko
J
J
D001499:Bayes Theorem; D055592:Biophysical Phenomena; D001703:Biophysics; D002908:Chronic Disease; D004307:Dose-Response Relationship, Radiation; D005260:Female; D005828:Genetics, Population; D006801:Humans; D008297:Male; D008954:Models, Biological; D008957:Models, Genetic; D009381:Neoplasms, Radiation-Induced / Q000453:epidemiology* / Q000209:etiology; D017711:Nonlinear Dynamics; D011153:Population; D011839:Radiation, Ionizing; D011868:Radioisotopes / Q000009:adverse effects*; D013269:Stochastic Processes
D011868:Radioisotopes