Body Potassium Content and Radiation Dose from ⁴⁰K for the Urals Population (Russia)

<div><p>Long-term whole-body monitoring of radionuclides in residents of the Urals Region has been performed at the Urals Research Center for Radiation Medicine (URCRM, Chelyabinsk). Quantification of ⁴⁰K was achieved by measuring the ⁴⁰K photopeak with four phoswich detectors in whole body counter SICH-9.1M. The current study presents the results of ⁴⁰K measurements in 3,651 women and 1,961 <i>t</i>-test; <i>U</i>-test men aged 11–90; measurements were performed in 2006–2014. The residents belonged to two ethnic groups, Turkic (Tatar, Bashkir) and Slavs (mainly Russian). The levels of ⁴⁰K-body contents depend upon gender, age, and body mass. Significant ethnic-differences were not found in ⁴⁰K-body contents and ⁴⁰K concentrations in terms of Bq per kg of body weight (in groups homogenous by age and gender). Both ⁴⁰K-body contents and concentrations were significantly higher in men than in women in all age-groups; the difference was about 25%. The measured ⁴⁰K-body content in men of 20–50 years was about 4200 Bq (134 g of K) and about 3000 Bq (95 g of K) in women. By the age of 80 these values decreased to 3200 Bq (102 g of K) in men and 2500 Bq (80 g of K) in women. Annual dose rates were maximal in the age group of 20–30 years– 0.16 mGy/y for men and 0.13 mGy/y for women. Further, the dose-rates decreased with age and in the groups of 60–80 years were 0.13 mGy/y for men and 0.10 mGy/y for women. Within groups homogeneous by age and gender, individual dose rates are described by a normal statistical distribution. The coefficient of variation ranges from 9 to 14%, and on the average is 12.5%. Doses from naturally occurring ⁴⁰K accumulated over 70 years were found to be 9.9 mGy for men and 8.3 mGy for women; over 90 years - 12.5 and 10.4 mGy.</p></div

Uncertainty of stochastic parametric approach to bone marrow dosimetry of 89,90Sr

The objective of this study is to evaluate the uncertainties of the dosimetric modeling of active marrow (AM) exposure from bone-seeking 89,90Sr. The stochastic parametric skeletal dosimetry (SPSD) model was specifically developed to study the long-term effects resulting from chronic 89,90Sr exposure in populations of the radioactively contaminated territories of the Southern Urals region of the Russian Federation. The method permits the evaluation of the dose factors (DF(AM ← TBV) and DF(AM ← CBV)), which convert the radionuclide activity concentration in trabecular (TBV) and cortical (CBV) bone volumes into dose rate in the AM, and their uncertainties. The sources of uncertainty can be subdivided into inherent uncertainties related to the individual variability of the simulated objects and introduced uncertainties related to model simplifications. Inherent uncertainty components are the individual variability of bone chemical composition, bone density, bone micro- and macro-architecture as well as AM distribution within the skeleton. The introduced uncertainties may result from the stylization of bone segment geometry, assumption of uniform cortical thickness, restriction of bone geometry and the selection of the applied voxel resolution.The inherent uncertainty depends on a number of factors of influence. Foremost, it is the result of variability of AM distribution within the skeleton. Another important factor is the variability of bone micro- and macro-architecture. The inherent uncertainty of skeletal-average dose factors was found to be about 40–50%. The introduced uncertainty associated with the SPSD model approach does not exceed 16% and mainly depends on the error of bone-shape stylization. The overall inherent and introduced uncertainties of DF(AM ← TBV) and DF(AM ← CBV) are below 55% and 63%, respectively. The results obtained will be incorporated into the stochastic version of the Techa River Dosimetry System (TRDS-2016MC) that provides multiple realizations of the annual doses for each cohort member to obtain both a central estimate of the individual dose and information on the dose uncertainty

Anthropometric characteristic of persons under study.

<p>Anthropometric characteristic of persons under study.</p

Characteristics of annual dose rates for Urals residents depending on gender and age.

<p>Characteristics of annual dose rates for Urals residents depending on gender and age.</p

Characteristics of phantoms, DF-values and dose coefficients for ⁴⁰K derived from [12].

<p>Characteristics of phantoms, DF-values and dose coefficients for ⁴⁰K derived from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0154266#pone.0154266.ref012" target="_blank">12</a>].</p

Parameters of multiple regressions for two independent variables (age, BMI) which predict specific activity of ⁴⁰K.

<p>Parameters of multiple regressions for two independent variables (age, BMI) which predict specific activity of ⁴⁰K.</p

Dependence of dose coefficient on body weight.

<p>Dependence of dose coefficient on body weight.</p

Comparison of the results of ⁴⁰K measurements for adult persons with published data.

<p>Comparison of the results of ⁴⁰K measurements for adult persons with published data.</p

Correction of confidence intervals in excess relative risk models using Monte Carlo dosimetry systems with shared errors

<div><p>In epidemiological studies, exposures of interest are often measured with uncertainties, which may be independent or correlated. Independent errors can often be characterized relatively easily while correlated measurement errors have shared and hierarchical components that complicate the description of their structure. For some important studies, Monte Carlo dosimetry systems that provide multiple realizations of exposure estimates have been used to represent such complex error structures. While the effects of independent measurement errors on parameter estimation and methods to correct these effects have been studied comprehensively in the epidemiological literature, the literature on the effects of correlated errors, and associated correction methods is much more sparse. In this paper, we implement a novel method that calculates corrected confidence intervals based on the approximate asymptotic distribution of parameter estimates in linear excess relative risk (ERR) models. These models are widely used in survival analysis, particularly in radiation epidemiology. Specifically, for the dose effect estimate of interest (increase in relative risk per unit dose), a mixture distribution consisting of a normal and a lognormal component is applied. This choice of asymptotic approximation guarantees that corrected confidence intervals will always be bounded, a result which does not hold under a normal approximation. A simulation study was conducted to evaluate the proposed method in survival analysis using a realistic ERR model. We used both simulated Monte Carlo dosimetry systems (MCDS) and actual dose histories from the Mayak Worker Dosimetry System 2013, a MCDS for plutonium exposures in the Mayak Worker Cohort. Results show our proposed methods provide much improved coverage probabilities for the dose effect parameter, and noticeable improvements for other model parameters.</p></div