Analysis of Indoor Radon Data Using Bayesian, Random Binning, and Maximum Entropy Methods.

Three statistical methods: Bayesian, randomized data binning and Maximum Entropy Method (MEM) are described and applied in the analysis of US radon data taken from the US registry. Two confounding factors-elevation of inhabited dwellings, and UVB (ultra-violet B) radiation exposure-were considered to be most correlated with the frequency of lung cancer occurrence. MEM was found to be particularly useful in extracting meaningful results from epidemiology data containing such confounding factors. In model testing, MEM proved to be more effective than the least-squares method (even via Bayesian analysis) or multi-parameter analysis, routinely applied in epidemiology. Our analysis of the available residential radon epidemiology data consistently demonstrates that the relative number of lung cancers decreases with increasing radon concentrations up to about 200 Bq/m3, also decreasing with increasing altitude at which inhabitants live. Correlation between UVB intensity and lung cancer has also been demonstrated.

The cancer mortality in high natural radiation areas in poland.

The cancer mortality ratios (CMRs) in Poland in high and low level radiation areas were analyzed based on information from national cancer registry. Presented ecological study concerned six regions, extending from the largest administration areas (a group of voivodeships), to the smallest regions (single counties). The data show that the relative risk of cancer deaths is lower in the higher radiation level areas. The decrease by 1.17%/mSv/year (p = 0.02) of all cancer deaths and by 0.82%/mSv/year (p = 0.2) of lung cancers only are observed.Tribute to Prof. Zbigniew Jaworowski (1927-2011).

Pooled Bayesian analysis of twenty-eight studies on radon induced lung cancers.

The influence of ionizing radiation of (222)Rn and its progeny on lung cancer risks that were published in 28 papers was re-analyzed using seven alternative dose-response models. The risks of incidence and mortality were studied in two ranges of low annual radiation dose: 0-70 mSv per year (391 Bq m(-3)) and 0-150 mSv per year (838 Bq m(-3)). Assumption-free Bayesian statistical methods were used. The analytical results demonstrate that the published incidence and mortality data do not show that radiation dose is associated with increased risk in this range of doses. This conclusion is based on the observation that the model assuming no dependence of the lung cancer induction on the radiation doses is at least ∼90 times more likely to be true than the other models tested, including the linear no-threshold (LNT) model.

A stochastic markov model of cellular response to radiation.

A stochastic model based on the Markov Chain Monte Carlo process is used to describe responses to ionizing radiation in a group of cells. The results show that where multiple relationships linearly depending on the dose are introduced, the overall reaction shows a threshold, and, generally, a non-linear response. Such phenomena have been observed and reported in a number of papers. The present model permits the inclusion of adaptive responses and bystander effects that can lead to hormetic effects. In addition, the model allows for incorporating various time-dependent phenomena. Essentially, all known biological effects can be reproduced using the proposed model.