BAYESIAN ESTIMATION OF ORGAN DOSES AND ...

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Research Journal in Engineering and Applied Sciences 2(4) 292-297 © Emerging Academy Resources (2013) (ISSN: 2276-8467) www.emergingresource.org

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BAYESIAN ESTIMATION OF ORGAN DOSES AND RISKS FROM PERSONNEL MONITORING DATA IN GHANA 1

T.C. Mokgosi, 2E. O. Darko, 2J.K. Amoako, 2P. O. Manteaw, and 3T. Ansah – Narh 1 Graduate School of Nuclear and Allied Sciences (SNAS), P.O. Box AE1, Atomic- Accra, Ghana. 2 Radiation Protection Institute (RPI), Ghana Atomic Energy Commission (GAEC) P.O. Box LG 80, Atomic- Accra, Ghana. 3 Ghana Space Science and Technology Institute (GSSTI), P.O. Box AE1, Atomic- Accra, Ghana. Corresponding Author: T.C. Mokgosi __________________________________________________________________________________________ ABSTRACT The studies of occupationally exposed workers to radiation have been based on recorded annual radiation doses. Monitoring programs in Ghana Atomic Energy Commission (GAEC) use thermoluminescent dosimeter which measures the whole body dose and not the actual dose to the organs and tissues. Bayesian Statistical model was used to estimate annual and cumulative absorbed doses to the organs and tissues including redbone marrow, female breast, thyroid, ovary, testes, lung and skin. The organ doses averaged over a cohort of occupationally exposed workers were estimated for the purpose of the ongoing cancer risk analysis. The skin indicates to be more susceptible organ to radiation with high mean dose of 1.15 ± 0.59 mGy/5yrs and followed by the thyroid and the female breast with the mean dose of 0.90 ± 0.46 mGy/5yrs and 0.72 ± 0.38 mGy/5yrs respectively. The red bone marrow, ovary and lungs received low dose with the mean dose estimates of 0.12 ± 0.11 mGy/5yrs, 0.20 ± 0.10 mGy/5yrs and 0.39 ± 0.19 mGy/5yrs respectively. SPSS was used to analyze the recorded data. Consequently the estimated mean annual cancer risks indicated low estimates as compared to the international reference levels. The study therefore indicates that the radiation doses are permissible to the limits verifying that medical facilities and the techniques used are adequate and acceptable. The study would therefore assist appropriate stakeholders to understand how to model the quantity of absorbed dose in a particular organ and compare with the whole body dose. ©Emerging Academy Resources KEYWORDS: Bayesian Estimation, Likelihood, Organ Doses, Risk, Monitoring Data __________________________________________________________________________________________ INTRODUCTION decrease to 5% when the dose is protracted over an The discovery of X-rays brought about a revolution extended period [NCRP, 1993]. in the medicine and nuclear power industry. Numerous studies have considered the mortality and Persons who are occupationally exposed to ionizing cancer incidence of various occupationally exposed radiation are prone to the risk of both chronic and groups in medicine, nuclear medicine, industry and acute effects [USDL, 2005]. It is therefore important research since the 1940s [Taulbee, Neton and Elliot, to estimate the dose to the various organs of the body 1980]. and the risk and the effects of ionizing radiation for occupationally exposed persons in Ghana. Cancer is an emerging public health problem in Africa. According to the International Agency for The study will provide some understanding of the research on Cancer (IARC) about 715 000 new cases quantity of radiation absorbed by the organs. It is on and 542 000 cancer deaths occurred in 2008 in this basis that the study will further improve the Africa. These numbers are projected to nearly double precision on direct estimates of risk after protracted (1.28 million new cancer cases and 970 000 deaths) low dose exposure and to strengthen the scientific by 2030 simply due to aging and growth of the basis of radiation protection. In this research organ population [Ferlay et al, 2010]. What is inadequate in doses will be calculated from the already existing these statistics is occupational exposure history of the monitoring data taken from the Dosimetry records of affected individuals. The general consensus of workers at GAEC in Ghana. opinion for the induction of cancer by ionizing radiation is 10% increase in cancer rate/Sv when the The study seeks to use Bayesian model to estimate dose is given over a short period of time with the mean organ equivalent dose of occupationally 292

Research Journal in Engineering and Applied Sciences (ISSN: 2276-8467) 2(4):292-297 Bayesian Estimation Of Organ Doses And Risks From Personnel Monitoring Data In Ghana

exposed persons in Ghana from personnel monitoring data.

In the study, the back of the body is assumed not to be exposed. The values apply to areas of the skin that faced the source of radiation (e.g. front of the face). Both of these ratios are expressed in SI units of gray and sieverts. The irradiation geometry is also important because the study assumes that the occupationally exposed workers perform their work mostly in an anterior-posterior position. Equation 1 provides the general formula that can be used to convert Hp(10) to organ dose for a given monitoring device;

MATERIALS AND METHODS Data Collection: Radiation dose records of the sample for the study were obtained from the Personal Dosimetry Laboratory of the Radiation Protection Institute (RPI), GAEC. The data was stored in a dedicated computer system that is consistently updated with measured values of HP (10) and Hp (0.07) after the field thermoluminescent dosimeters have been read. Thus, the hard copy records contain detailed quarterly monitoring results for each personyear for a facility. Stratified random sampling was used in collecting the data obtained from the RPI where the hospitals of each region are the strata. The data recorded 158 exposed workers with yearly thermoluminescent dosimeter (TLD) totals that are greater than zero. The data collected was from seven regions which are: Ashanti, Brong-Ahafo , Volta, Eastern, Central and Upper East. In each region, four public medical facilities were examined and for Greater Accra, thirteen private medical facilities were of interest in this study. The criteria of choosing the medical facilities were on the account that workers were exposed to different types of radiation sources such as x-rays machines, CT scanners, etc. and data collected on the terms of monitoring consistency for a period of five years, from the year 2007 to 2011. SPSS was used to analyze the sample data.

D DT  H P  d   T  Ka

Hp(d)/Ka represents the personal dose equivalent per unit of air kerma (Sv/Gy). Equation 1 is part of the experiment to compare the results with the Bayesian statistical method to determine the variation [Simon et al, 2006]. Estimation of Organ Doses using Bayesian Statistical Method: A Bayesian statistical approach was used to estimate the unforeseen quantities (real dose) given the values of the seen quantities (recorded dose) [Mitchell, Ostrouchov, Frome and Kerr, 1997]. An important aspect of Bayesian statistical method is the connection between the unforeseen dose and the seen dose in the form of conditional probability distribution. The prior probability distribution in the method is based on the previously recorded data acquired from the personnel dosimetry database at the RPI of GAEC. The records approximately represent the state of the knowledge about the unforeseen dose (real dose) and the seen dose (the recorded) prior to observation or measurements. The definite values of the recorded measurements are fitted in the model as conditional information and laws of probability are exploited to find the conditional distribution of the unforeseen values given the seen values. In the quarterly exposure period, the quantities of interest are as follows: c the unforeseen dose to the TLD badge x the seen dose to the TLD badge, The quantity c is considered a random variable because the knowledge of its real value is not known. The requirements for a probability distribution are then used for every possible value of c, where P (c )  1 (meaning the sum of all probabilities

Table 1: Tissue and Organ Dose coefficients in gray per sievert at two Energies and Average Value of 35 keV as used in the study Organ or tissue [Gy/Sv]

10 10 10 10 10 10 0.07

Red bone marrow Female breast Thyroid Ovary Testes Lung Skin

(1)

where, DT/ Ka represents the organ absorbed dose per unit of air Kinetic Energy Released per unit Mass (kerma) (Gy/kg).

Estimation of Organ Doses using Personnel Monitoring Data from TLD Measurements: The coefficients necessary to convert monitored dose based on TLD measurements to organ dose are contained in ICRP Publication 74(ICRP 74, 200) represented by Table 1. The coefficients are listed in the document by tissue of interest, exposure geometry, and radiation energy. These can be used to convert from ambient dose equivalent (H*(10)) to free-air KERMA (Ka) for various photon energies and also the coefficients necessary to convert HP(10) to Ka. The study assumes that, the predominant energy from diagnostic X-ray machines to be between ~30 keV and ~40 keV for X-ray beam of 75 to 120 peak voltage (kVp); thus the midpoint 35 keV is used [Taulbee, 1980].

d(mm)

HP d    Ka 



0.10 0.87 0.87 0.24 0.99 0.37 1.1

c

must be 1). The expression comprehends that the random variable c, represents all possible events in the entire sample space and with an assurance that one event will occur. The study investigates P(c) referring to the distribution of probabilities that concerns one individual in one exposure period. The seen dose is also treated as a random variable and

[ICRP 74, 2001]

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Research Journal in Engineering and Applied Sciences (ISSN: 2276-8467) 2(4):292-297 Bayesian Estimation Of Organ Doses And Risks From Personnel Monitoring Data In Ghana

erstwhile to its scrutiny there are uncertainties for a known c. Hence, the connection between x and c takes the form of a conditional probability distribution P( x | c) . The notation represents the probability of event x occurring after assuming that event c has already occurred. Conditional probability distribution P(c | x) is called the posterior distribution and is given in equation 2 as the Bayes’ Theorem. P(c | x)  k (c) P( x) P( x | c) (2)

data. In short, the Bayes Theorem can be expressed as equation 4. (4) Risk Estimation: The radiation risk models developed by the BEIR used in its 2006 recommendations are described in chapter 2. In general radiation risk estimation is derived from incidence for specific tumour sites when adequate dose response data are available from Japanese life span study (LSS), analyses of multiple studies. The ICRP notes that for the purposes of radiological protection, it is scientifically acceptable to assume that the incidence of cancer will rise in direct proportion to an increase in the equivalent dose in the relevant organs and tissues, below about 100 mSv. The nominal risk coefficients are adopted in risk estimation of the individual organs [NRC, 2006].

k (c) is the normalizing constant which

where ensures

that



c

P (c | x )  1 .

To

conclude,

P( x | c) is the solution to the findings of the study and is called the likelihood of c for each observed x and is given by f (c | x) [Mitchell et al, 2007]. Construction of the Likelihood Function: There are two ingredients to a Bayesian analysis. First, a model for the data given some unknown parameters, specified as a probability (density) function. The parameter of interest c (organ dose) follows a normal distribution. The parameter of interest is sometimes usefully thought of as the “true state of nature”. The prior distribution of

Nominal Risk Coefficients: Nominal risk coefficients are derived by averaging sex and age at exposure lifetime risk estimate representative population [ICRP, 2007]. The ICRP publication 103 simplified the risk calculations by combining male and female average risks. The nominal risks of working age population are computed for the following organs of interest in the study: redbone marrow, female breast, thyroid, ovary, testes, lungs and the skin. The study assumes that an average person starts work at the age of 18 years and retires at the age of 64 years. Table 2 summarizes the probability of aggregated detriment in individual organs and tissues upon radiation exposure.

c is f  c  . The prior

distribution summarizes what is known about c before the experiment is carried out and chosen by the researcher. The prior distribution is subjective and may vary from one researcher to the other.

 

The likelihood function is represented by f x c The

likelihood

 

function f x c provides

.

the Radiation detriment quantifies the harmful effects of radiation exposure in different parts of the body and is determined from the nominal risk coefficients. The radiation detriment takes into account the severity of the disease in terms of lethality and years life lost [ICRP, 2007].

distribution of the data, c, given the parameter value x. The research assumes that the likelihood function for the data obtained from RPI is based on the normal distribution and is represented in equation 3: 2 2 n n 1   x    1   n  xi   (3) f  x1,x2,xn   exp i 2  exp     2  i1  2  2   2  i1 2 

Table 2: Nominal risk coefficients for individual tissues and organs after exposure at low dose rate

Where, the standard deviation of the population (i.e. the standard deviation of the observed data), the mean of the population (i.e. the mean of the observed data), = continuous individual dose, n = the population size.

 

The posterior distribution f c x summarizes the information in the data, x, together with the information in the prior distribution, f  c  . Thus,

f  c x  summarizes what is known about the

Organ or tissue

Probability of fatal cancer (10-2Sv-1)

Aggregated Detriment (10-2Sv-1 )

Red bone marrow Female breast Thyroid Ovary Testes Lung Skin

0.23 0.49 0.09 0.07 0.88 0.127 0.670

0.06 0.08 0.01 0.02 0.16 0.23 0.01

Cancer Risk Estimation for Individual Organs: The cancer risk estimation for occupationally exposed workers can be expressed mathematically as in equation 5 as:

parameter of interest c after the data are collected. The Bayes’ theorem will be used to calculate the conditional distribution of the parameters given in the

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Research Journal in Engineering and Applied Sciences (ISSN: 2276-8467) 2(4):292-297 Bayesian Estimation Of Organ Doses And Risks From Personnel Monitoring Data In Ghana

Annual Cancer Risk  Dorgan  Nor min al Risk Coefficient (organ of int erest )

(5)

Where, Dorgan is the mean annual organ dose in (mSv) RESULTS AND DISCUSSIONS Figure 1 indicates that gender ratio of most of the workforce in the medical facilities is not uniformly distributed. Out of sample of 158 occupationally exposed workers male and female add up to 132 and 26 representing 14% and 73% respectively.

26 (14%) 132 (73%)

Male Female

Figure 1: Percentage Distribution of Gender from the Personnel Monitoring Data Figure 2: Comparison of mean annual whole body dose distribution in selected medical facilities from 2007 to 2011

The Upper East Region (UER) in Figure 2 indicates the highest mean annual whole body dose, followed by Volta Region (VR) representing approximately 2.5 mSv and 1.70 mSv respectively. The lowest dose (0.80 mSv) was recorded in the Brong-Ahafo Region (BA). The difference in mean annual whole body dose in the various regions in Ghana is due to the distribution of the workforce in the medical facilities.

In Figure 3, the highest dose in red bone marrow (RBM) was recorded in Eastern Region (ER) which is  0.20 mSv/5years, female breast was recorded in Ashanti Region (AR) which is  1.2 mSv/5years, thyroid was recorded in Volta Region(VR) which is  1.3 mSv/5years, ovary was recorder in Ashanti

2 1.8 ) 1.6 v S m 1.4 ( es 1.2 o D ang 1 r 0.8 O na 0.6 e 0.4 M 0.2 0

BA ER CR

RBM

Female Breast

Thyroid

Ovary

Organs Considered

Testes

Lungs

Skin

Figure 3: Comparison of the regional contribution to the mean annual organ dose distribution for selected medical facilities Ghana from 2007 to 2011 recorded in Ashanti Region (AR) which is  1.48 Region (AR) which is  1.2 mSv/5years, thyroid mSv/5years, lungs was recorded in Volta Region was recorded in Volta Region(VR) which is  1.3 (VR) which is  0.57 mSv/5years and skin was mSv/5years, ovary was recorded in Ashanti Region recorded in Volta Region (VR) which is  1.77 (AR) which is  0.34 mSv/5years, testes was mSv/5years. The reason for these differences in mean 295

Research Journal in Engineering and Applied Sciences (ISSN: 2276-8467) 2(4):292-297 Bayesian Estimation Of Organ Doses And Risks From Personnel Monitoring Data In Ghana

annual organ doses is because the average personal equivalent dose changes as a consequence of medical techniques, the number of years worked, the tasks performed by each worker and time spent in the radiation field.

facilities in Ghana received small amount of exposure from 2007 to 2011. The study introduced a different method by exploiting Bayesian analysis to estimate organ doses in occupationally exposed workers. The Bayesian analysis indicated substantial differences in organ doses that where estimated from the TLD. The superficial organs and tissues, e.g. skin, thyroid and the female breast, on average recorded the highest doses of all the other organs. The estimated mean annual cancer risks for all the organs indicate that the doses incurred by occupationally exposed workers due to x-rays are stochastic and the level of exposure increases the likelihood of cancer by very low doses. The study therefore indicates that the radiation doses are permissible to the limits verifying that medical facilities and the techniques used are adequate and acceptable.

Figure 4 clearly shows a positively skewed distribution of the mean dose to thyroid in both male and female. The mass of the organ dose is concentrated within the range of (0.00 to 2.00) mSv. The positive skewness of the thyroid shows susceptibility to radiation, because the thyroid gland produces hormones that regulate rate of metabolism and affects the growth and rate of function of many other systems in the body [European commission, 1998].

In summary, the study highlights on the following main points: 1. In estimating organ doses, there is an emerging class of problems where the information about the specific organ dose follows a distribution which is often subjectively derived. 2. The distribution of specific organ dose to a whole body dose analysis is mathematically modeled using Bayesian Estimation Techniques. 3. The likelihood function which provides the distribution of the data is assumed to be normally distributed. Therefore, an extension of the study is to use other probability distributions as likelihood function or other probability models like Regression Analysis to estimate the whole body dose and specific organ doses to compare the Bayesian Estimation results.

Figure 4: Likelihood of Thyroid in both male and female The results in Figure 5 indicate that the testes, skin and the female breast stand a greater risk of cancer incidence per unit dose. The probability of incurring cancer of the testes (42%) is very high as compared with the other organs of the human body, due to the number of hours worked in the radiation field. The likelihood of incurring cancer in the ovary (0%) is negligible.

ACKNOWLEDGEMENT The study was supported by the facilities provided by the Radiation Protection Institute (RPI) under Ghana Atomic Energy Commission (GAEC) in collaboration with Graduate School of Nuclear and Allied Sciences (SNAS). REFERENCES Department of Labour United States (USDL). (2005). Occupational Safety and Health Administration 2005. Retrieved from http:// www.osha.gov on 20th May, 2013. European Commission. (1998). Thyroid Disease and Exposure to Ionizing Radiation: Lesson Learned following Chernobyl Accident Proceeding of the Scientific Seminar held in Luxembourg, Radiation protection 121.

Figure 5: Estimated risk of fatal cancer for seven most radiosensitive organs CONCLUSION The monthly monitored doses indicate that occupationally exposed workers who work in medical

Ferlay, J., Shin, H.R., Bray, F., Forman, D., Mather, C. D. and Parkin, D. (2010). Cancer and mortality, International Agency for Cancer Research on Cancer, France. 296

Research Journal in Engineering and Applied Sciences (ISSN: 2276-8467) 2(4):292-297 Bayesian Estimation Of Organ Doses And Risks From Personnel Monitoring Data In Ghana

International Commission on Radiological Protection, (2007). 2007 Recommendations of the International Commission on Radiological Protection, Publication 103. Mitchell, T. J., Ostrouchov, G., Frome, E. L. and Kerr, G. D. (1997). A method for Estimating Occupational Radiation Dose to Individuals, Using Weekly Dosimetry Data. Radiat. Res. 147, 195-207. National Council on Radiation Protection and Measurements, (1993). Limitation of Exposure to Ionising Radiation. NCRP report No. 116. National Council on Radiation Protection and Measurements, Bethesda, Maryland. National Research Council (NRC). (2006). Health Risk from Exposure to Low Levels of Ionizing Radiation, Biological Effects of Ionizing Radiation (BEIR), committee VII Phase 2, pp (1-18). Washington, D.C: National Academy of Sciences. Taulbee, T. D., Neton, J. W. and Elliot, L. J. (1980). A Method For Determining Organ Doses from External Exposure Monitoring Data, National Institute for Occupational Safety and Health, United States of America.

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