ML20004B342

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Speech Entitled Regulatory Implications of Radiation Dose-Effect Relationships, Presented at 810331 Meeting in Atlanta,Ga
ML20004B342
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Issue date: 05/21/1981
From: Hironori Peterson
NRC OFFICE OF STANDARDS DEVELOPMENT
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{{#Wiki_filter:1 THIS DOCUMENT CONTAilfS /{') P00R QUAUTY PAGES REGULATORY IMPLICATIONS OF RADIATION DOSE-EFFECT RELATIONSHIPS PP .. _ _. - 1,. Harold T. Peterson, Jr. Senior Environmental Health Physicist Nuclear Regulatory Commission Office of Standards Development Washington, D. C. 20555 e< tr> e 2 7 5 I'IAY 2 U.s, 3 I,L k ~ M,4% g 4 Disclaimer: The views expressed in this paper are the personal opinions of the author and should not be interpreted to represent the official position of the Nuclear Regulatory Commission nor the views of other members of its staff. Paper presented at the Symposium on tJ e Health Effects of Ionizing Radiation, American Chemical Society 181st National Meeting, Atlanta, Georgia, March 31, 1981. 810528007&it

i l e INTRODUCTION The health consequences of exposure to ionizing radiation fall into two general classes: stochastic effects and non-stochastic effects. The stochastic effects are manifested as an increased risk of cancer, teratrogenic damage, or genetic effects. The probability of the occurrence of these effects is proportional to the radiation dose, but the severity of the consequences is not related to the . cumulative radiation" dose received. These effects cannot be distinguished froe .~ similar effects,arisi6g from causes other than ionizing radiation. Because of this and the statistical nature of thase effects, a direct cause-effect relation-ship cannot be established for individuals. These effects are generally discernable only from studies of largn populations which received individual doses in the intermediate to high doso range. Non-stochastic effects of ionizing radiation are generally manifested as observat le injury in the exposed individual. These effects include cataracts, temporary depression of the leveis of certain blood cell types, and temporary or permanent reduction in fer tility. For these effects, the severi6y of the consequences is proportional to radiation dose and there is an apparent thres-hold dose, below which no injury is discernible. Both stochastic and non-stochastic effects must be considered in developing radiation protection standards. Because of the apparent threshold dose for the non-stochastic effects, adequate protection of the individual can be assured by setting an allowable dose limit somewhat bele1 the dose where these effects are anticipated to occur. The International Commission on Radiological 4

s [ i Protection [ICRP-77] has recommended that an annual dose equivalent of 0.5 sieverts (Sv) (50 rem) would ensure protection against non-stochastic effects for all tissues except the lens of the eye. For the eye, a limit of 0.15 Sv (15 rem) is recommended. i The development of standards for limiting the stochastic effects is more diffi- ~ cult as there is no proven threshold dose. Consequently, ar!y radiation expo-sure should be assumed to increase the risk of adverse health effects. The'.._. risk of stochastic effects associated with individual radiation doses below about 10 rems (0.1-Sv) is sufficiently small that detection of an increase above the normal incidence of these effects generally cannot be deconstrated with statistical precision even in large exposed populatinns. Because of this difficulty, the risks from low-level ionizing radiation cannot be p * -isely quantified. Estimates of these risks at low doses and low dose rates are based primarily upon extrapolations from effects observed at higher doses and higher dose rates. The assumed dose-effect relationships and associated para-meters that are used for this extcapolation are the focus of much of the con-l troversy concerning the public impact of low-levef'Yn6itir.g-radiation exposyre. i ~ ~ r 2 s

l l RADIATION DOSE-EFFECT MODELS The biological effects of ionizing radiation can be characterized by several relationships. In general, these relationships predict an increasing effect with increasing radiation dose until moderately high doses (200-500 rem or 2-5 SV)* are reached. Above this dose range, the incidence of the effect generally decreases, presumably due to increased cell mortality. A generalized model for such behavior is given by [BR0-77]: ~ 2 2 R(D) = (a,+ ay g D ) exp - (p D+E D ), (y) D4 o y 2 where R(D) is the probability (r..n) of a specific bioeffect occurring at dose D. The exponential term describes the effects of cell killing and the quadratic term describes the production of the biological effect of interest. Within the quadratic expression, o represents the normal.ncidence** of the g effect. The second term, which varies linearly with dose, represents sesions produced from single damage events (hits). The dose-squared term is believed to represent lesions formed by pairs of events [KEL-78]. The application of equation (1) to actual epidemiological studies of human radiation exposure is limited by the number of parameters which must be deter-eined. The small number of observed excess effects generally requires data to be grouped in only a few dose intervals. This restricts the number of distinct 1 A This dose range depends upon the type of radiation, the type of effect, and the species of animal irradiated, among other factors. This " normal incidence" would include any radiation induced effects result-ing fron. " background radiation" of natural origin or anthropogenic sources such as medical diagnostic radiation which wera not included in the speci-fied dose. 3

1 l l data points available for analysis and increases the uncertainty in the resulting parameter vclues.* t t For saany biological effects, the exponential term'in equation (1) appears to { be negligible fur doses below 200 ren '? Sv) and equation (1) reduces to a I quadratic or " linear-quadratic" dose-ef fect relationship: f 2 i -.7._ _ _i 11" %.. _ R_ (D)..= a,;+ ay D+a8 () g 2 ,m.y .~ y w.s a,- > o ;,w.su,w m, ques-a t ~n~ ~ w w, x m mun

mwr, a-.s w ea..unuw,w,

- ~ L.;- a. .~.n... .:. ~ "~*~~~' ::.C ;72 ~~'~ ~ ~ ~ ~ ' ~ ~ ' ~ At still lower doses, (D,< a /a ), the dose-squared term becomes small compared . y 2 to the linear. term. As o is generally of the order of 0.005-0.02.r, this j g y occurs typically in the region below 50 to 200 rem. The dose-response at lower I i doses is would be essentially a straight line and could be represented by a linear dose-effect relationship: m.. i t D (3) ] Rt (D) = a,+ a3 i Equations (2) and (3) form the basis for most of the dose resranse relationships '" [ which have been used at lower-to-moderate doses, below levels where appreciable i cell killing occurs. Higher doses would not be attained in routine radiation exposure situations and, consequently, are not of primary interest for radiation l i protection standard setting. However, the decrease in response due to cell i death, at high doses can be important in interpreting the data f ron past high do'se situations. Neglect of this effect might lead to underestimates of the A The variance of the least-squares regression parameters is inversely propor-tional to the number of degrees of freedom, DF, where DF = n-m-2, n being the nu;nber of data points and m is the number of parameters to be estimated. t 4 i s

I L risk coefficients derived from these dna and, thereby, lead to underestimates of the risks at lower doses. In addition to the relationships which are derived from the general form of equation (1), a fractional power model has been proposed, notably by Baum -[BAUM-73, BAUM-76] of the form: . RSRD(D).- ,1 M. .a a nm, .. > = ~ , n.w.,n. ,r .. =n n.4.y. m.& r.- ... nu .aw a:p.x.m ~ -.a.u -. . ~.. u - a, ^ T'1:% -*";~"-"""r7 ~ !"10 TZrl ^/.?JEC":T"' . T ~~ 1 ~ - ~~"7~ ~J l where a,is '. positive number greater than unity. Log-log regression analyses - of the incidence of several types of cancer in the Japanese atomic-bomb survivors population gave exponents in the range of 0.19-1.0 (m = 1.0-5.3) [BAUM-73], Theoretical explanations for these findings include a heteroDeneous population with subgroups having different sensitivities to radiation [BAUM-73) and the influence of cell killing at high doses [BAUM-76]. If the latter effec.i is.esponsible for the apparent less than linear dependence, the dose-effect relationship could be described by general equation (1) and the fractional power model would not constitute a separate dose-effect relationship. A frac-tional power model with m=2 bas been assumed for the purpose of analyzing possible implications of a less than linear dependence on dose. .-. N l l 5 h

I l RISK OF CANCER FROM LOW-LEVEL IONIZING RADIATION PREDICTED BY VARIOUS DOSE-EffECT RELATIONSHIPS Human epidemiological data which clearly show a statistical excess of radiation induced cancer are generally restricted to doses above 25 rem (0.25 Sv) and to intermediate to high dose rates. Extrapolation of risks calculated from these data outside of this range is tenuous at best, particularly for radiation m or, _ exposures delivered'at much lower dose rates. However, such extrapolations- ... w onuw.. am.a.. m.u a n J,. o,' "i.... provide the_.only available basis for_ estimating the risks which may be asso i n.-. -. ~ ~.~ ciated with doses and dose rates found in the workplace and elsewhere in man's environment. In order to compare the relative predictions of the various dose-effect relation- ~4 ships, the models were normalized to produce an annual risk of 6.5 x 10 at s. 200 rem.* These parameters are shewn in Table 1. Although the point at which the responses were normalized is arbitrary, the resulting risk coefficients are similar to those obtained by the National Acsdemy of Sciences - National Research Council Advisory Committee on the Biological Ef fects of Ionizing Radiation in ti.eir 25B0 report [NAS-80] from actual epidemiological studies as shown in Table II. Figure I shows the relative response of these nornalized relationships olotted on log-log coordinates i.n order to emphasize the divergence of the extrapolated risks at low doses. This divergence is highlighted in Figure 2 which compares the various models to the linear extrapolation model. Note that at a dose a liacause of rounding off of the coefficients, the 1*near quadratic model produces this risk at a dose of 210 rem, however, this dif ference is negligible. 6 N

TABLE I. Dose-Effect Relationships Normalized to Equivalent Risk at 200 rem. Linear Model- -6 RL = 3.25 x 10 D a :r.w.; 2sm:s. Linear-Quadratic

  • Model' i7 4

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,,.,,...,m.; _..c .. y.. Dose-Squared Model J -8 2 R05Q = 1.62 x 10 0 Fractional Power Model -5 D *5 0 RSRD = 4.60 x 10 A This model gives an annual risk of 6.5 x 10 4 at approximately 210 rem. The precise equivalent relationship is 1.0833 (10 8 D + 10.s D2), 7 s

i-TABLE II. Comparison of Normalized flodel Parameters and Recommended Parameter Values in the 1960 BEIR-III Report NAS-BEIR-III Recommended Parameters Normalized Parameter [NAS-80] Model Value Giving a Risk of 6.5 x 10 4 at 200 rem Leukemia Bone Cancee Other Cancers -6 -6 -8 -6 Linear a1 = 3.25 x 10 2.239 x 10 5 x 10 3.47 x 10 -6 -6 -8 -6 Linear-Quadratic ay = 10 0.989 x 10 2.21 x 10 1.397 x 10 -8 -8 -10 ~0 2 = 10 0.85 x 10 1.9 x 10 (1.20) x 10 a a /a2 = 100 rem 116 rem 116 rem 116 rem y -8 -8 -10 -8 Dose-Squared b = 1.625 x 10 1.37 x 10 3.1 x 10 1.825 x 10 The fract'onal power model was not examined by the BEIR Committee. P 4

~ s !i I l L comparable to the average annual dose from the natural background radiation (0.1 rem), the dose-squared relationship predicts a risk over a factor of 1000 4 0 lower than the linear model, wh'ile the fractional power model (D *5) gives a y v e t risk which would be almost u factor of 50 greater than the linear model. s Below 10 rem, the linear quadratic relationship gives.a response which remains [ about three-tenths (1/3.25) of the linear risk. w:.. w 4 e

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w.. - - + m .,..-,....., _.i 1./.L.I...f/s ' ' ; I C.: _ _1 C7C*i,. - ~ A L J,Ca M.;, . e.l.F. 3 . ho n m y- _;l- %L ~- s w,s+w. N- =..: ~ r eretx==: =- 1 = .=:: - .D r,,~, 2 .; - e. 2, ... w 4 s h10-3 2 3D +a2 D2 k ~ ~ .I 104 i ' ' il i el e ,,,,1 ~" 104 10-3 10-2 0.1 1 10 102 103 ^ RADIATION DOSE EQUIVALENT (rem) 4 Figure 1. Projected potential health risk versus radiation dose for four differ-ent dose effect relationships. The models were normalized to give a risk of 6.5 x 10 4 yr 1 at 200 rem. The predicted risk can be obtained by multiplying the ordinate by 6.5 10 4 yr 2 and the length of period over which the elevated t risk persists (nominally 30-50 years). The letters on tre abscissa denote various whole body dose limits and reference doses: A -maximum allowable life-time occupational dose, 225 rem (5 rem / year x 45 years). 8 - average lifetime l dose from natural background radiation," 7 rem (0.1 rem / year x 70 years); C - allowable average annual dose for occupational radiation workers, 5 rem; D allowable annual limit for exposure of a member of thc general public, 0.5 rem; E - approximate average annual radiation dose from natural background i radiation, 0.1 rem; F current annual radiation dose limit applicable to urapium fuel cycle facilities, 0.025 rem; G - design objectives for nuclear power reactor effluent releases, 0.01 rem for liquid effluents, and 0.005 rem for noble gas airborne effluents. The average annual dose to individuals resid-ing within 50 miles of a nuclear power reactor is approximately 0.01 millirem or 10 5 rem and would be off the scale to the left. a If medical diagnostic radiation and other man-made sources (except nuclear energy) were included the lifetime dose would be closer to 12 rem. 10 w

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INDIVIDUAL RISK AND POTENTIAL POPULATION HEALTH IMPACT The risk projections in Figures 1 and 2 could lead to a conclusion that, if the risk from low level ionizing radiation were described by a fractional { power relationship, current radiation protection standards should be reduced because the risk at low doses is greater than given by the linear model which ~ is 65ually used for'this ex'trapolation. Converscy; if the dose-squared _ una. <m. ~ ___; _ _, relatfonship is valid, then_the linear extrapolation model would result in_ _ ' +. I ~ w.x..awwewwmmm r.e =~ unm. s a- -. =.. + .-n g. . ~ ;~, considerable overestimates of the risk at low doses, and current radiation e C ~ CC%T;,T':723'^?"T:Q-u -.........a. m:::L"~":;'"' :. 7~T T~;.. X T :~; .? C~.::~""

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protection.standa ds could be raised or maintained at their current levels., y, A likely consequence of a reduction in radiation protection standards would be the use of mere workers to perform necessary functions, such as nuclear power plant maintenance. Such dose sharing would be expected in situations s. where additional protection or shielding could not be readily provided to reduce individual radiation exposures. necause of this possible consequence, the effects of spreading acute radiatiun doses over an increasing number of e individuals was examined for the four different dose-effect relationships. Dose Relationships 1 It was assumed that there was an operation which resulted in a fixed total dose D,. If n workers performed this operation each worker would recieve a N l radiation dose, D, where 6 = D,/n (5) 12

This situation could result if there were an operation that required a fixed amount of time and was in a radiation area where the radiation dose rate was uniform. The total dose received by all of the workers, the population collective dose, S, would remain constant S=n6=0, (6) ... o,2 __;m.., .....__n. Theabove situations, where th'e individual doses are precisely inversely ~ n+% ARM L A..J m n x x n

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>= a ~ proportional to the number of workers used, represents an ideal hypothetical: - ma - ^^ n:.:"*; X : r :T.. r ::~T:.*72 7.~;._w.x. ~ ~. ~ ~.n. .:~ ' ' ' ' " ~ ~ " situation. Actually, a certain fraction of the total dose wculd be received. v. 2:1... as the worker approached the work area, got into position, set up the necessary tools, prepared to work, and left the work area. Assuming that this entry-exit dose for each worker represents a fixed fraction (20) of the total dose 0,, then the average individual dose to each worker would be: 6*D o (1 + 2 0) (7) +20D =D n o n where 2 0 is the entry-exit dose fraction or EEDF. (It is further assumed that an equal fraction e is received upon entering and upon leaving the work area which results in the factor of 2). The magnitude of this entry-exit dose cc:ittibution would be expected to increase in situations where there was a high dose rate in the work area so that the actual productive working time was short compared to the entry, setup, and exit times. The collective dose in situations where there was an entry-exit dose contribution would increase with the number of workers used: S = n D = D,+ 20nD,= D,(1 + 20n) (8) 13 l N

Both equations (7) and (8) can be expressed as fractions of the total dose by dividing both sides of these equations by D, so the relative effect of the number of workers and '.he entry-exit dose fraction, or EEDF, can be analyzed parametrically, independently of the initial total job dose D. Figure 3 g shows the effect of increasing the number of workers used on the fraction of the total dose delivered to each worker. These were calculated for entry-exit ((f..~. _ _ _ _..tive..or. population. dose delivered to the en dMU."trilb"ti"5 f6,~~.;2,o.2o',and0.20. ~ The. Chi Qu.EG%,Gu;&u,a,ami, E ~ ~w n . u.x2;u la,..~, e L ~ ", n n ctive dose rapidly increases as the number of workers is increased and as - colle Or,' T"l" .1 ..x._ _ _ _ m=n ;,, _ .m

the entry-exit dose fraction increases._,

.~,...s%,.. .~ The predicted individual health risk is a function of the average individual dose and the choice of the dose effect relationship f(6) so that ~ 5 = f (6) (3) The potential population health impact, ;, is defined as the total risk imposed upon a population. For a population of n individuals, each.having the same l individual risk, the health impact is simply: I = n 5 = nf (6) (10) for the linear dose-effect relationship, the potential population health impact ~., is directly proportional to the collective (population) dose: I = n (a 6) = a n 6 = a 5 (11) + + 14 N

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=,- p.g ag. ~, m # c., -., ~ w-,w. m g. s.c t-% p.4-4.4 v . v. < ". %. La w. -e,wam_. eri-goz E E D F = 0.20 n . s, y,e,, xm e., ..ym m m -.. Z.2 ~.. n q

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.-~..-3 O >o E ED F = 0.10 usu "O az 0.1 O i 2.. 1 0 i E4 i k E ED F = 0.02 i O-( et + ~ / EEDF=0 .e O.01 8 I f f f IIII I f f I I fI 1 1 10 100 NUMBER OF WORKERS 1 i i N i Figure 3. Variation of individual radiation doses as a function of the number of individuals who are exposed to a fixed total dose. The four curves rtpre-sent different fractional entry exit dose contributions (see text). The marks on the right-hand margin indicate the Ifmiting individual doses which are asymptotically approached as the number of workers is continually increased. 15 N, i . * ^

t I ~l c,;;:y. , h -;~ .e w ~ ~ 4 T M e ENTRY. ~.x... EXIT w l 7 DOSE ~~ .. ;c. FRACTION J. .c... - ..,,;x. < m c w mc. ce... (EEDF). mt : L.. - -,t ew .o.. _. - ...-.,;-,...~- .."- ". wm&.~ w + a*'* 't'e. N '+7 0.20 e p 9 ;..a: u e y.. p.- 6

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_'E*-" "- m.r. w.'+. n:.. mem.i. v on w a/ w.>v ,. "v. . ~ ~ - - u+ .s ..D._,__ y.e,v,,,y w.. .,s., g. -,..,.s.e........3,,.,._._.. + w... ...~.. - t -.-y c -o .u c,,,,,$ . n c:. .7,,n., a,,,. ,s ... w wy y ~ .,w- ~. v, 5 w s m w i e O Q-4 I Z 0.10 0 P 3 J x n. O E 2 w 2 -- 0.02 l ~ b _. w 1 0 e + a O u 0 l I I i I ~ t 5 10 15 20 25 NUMBER OF WORKERS r v r t s I Figure 4. Relative collective (population) dose resulting from increasing the number of individuals exposed to radiation. The four lines represent different contributions from fixed fractions of the total dose received during entry, work set up, and exit from a radiation area. W s 16 i l T i I .g ~ s e

l ? However, for the other dose effect relationships, there is no simple direct relationship between the collective doses and the potential dose and the potential population health impact must be evaluated from the individual risks as indicated by equation (10). This was done for'each of the dose-effect relationships shown in Table 1. ~" The results wire expressed in terms of the individua1' cancer mortality risk u ~,. ~ ~.. -. ~. .. ~ ~ and population health, impact resulting from_n workers relative to, the_ individual.. L

~' L. &, 5 % :w a 6:5;%u%;E%~;;&Gk2EXG nlin5&;iaGaa~::.h.kX% avw. -

h ,. risk and total impact of having one worker perform the entire' operation. For. - .c. 7 C; '7:: r :r t r r t*C n _. m;Z: 5 r C ": O L _ m. _._ _. Z T ~ ~ 2 r ? *:f T r "1~ 2 % 7 C T % Z ! L most of the dose effect relationships, expressing the risks and impact relative m,, ' ~ to one worker removes any depen'dence"on the total job dose, D, which is .1 ~ o cancelled out of the expression. For example, in the dose-squared relationship the individual risk for n workers is: ~ -Rn (D) = b D-2 = b (D, [1 + 2 0 ] )2 = b Do (- + 2 0)2 2 1 f and (12) n the risk for 1 worker is it d)=bD,2(1+20)2, so that the ratio is y independent of D,: j -R (D ) - (" + 2 0)2 + (1 + 2 0)2 (13) j = Ry (D) The population health impact relative to one worker is also independent of the total job dose: N ) In"" n (D) = b D n(1 + 2 0)2 and 17=bD2 (1 + 2 0)2 3, I" = n(1 + 2 e)2 (14) I) (1 + 2 0)2 i 17 s \\

P The above simplification does not exist for the linear quadratic relationship f as the total job dose, D,, cannot be factored out of the relationship. For this relationship, actual risks were calculated for total job doses of $0 millirems (0.05 rem), 100 millirem (0.1 rem), I rem, 5 rems and 10 ras. The results vary slightly with this initial dose and, consequently, the results are presented as a band representing the range of doses listed above rather thanasasinglecurvei. h5 general,theresultsofthelinearquadraticmodel ~ 4..- j . n.c m m. lie sF.ghtly below the values calculated for the linear model. The values for, i s ~-..7= .. - -... -., = - 7. ..- -. 3. - pjf Q, "~th'e lowestfinitial] dose 35'0[di1OrEmd ar'eTalmoshiiEls55e as those of the 11'nead ~ r.~. o.-,,~.___.-_m._~~__.- . s b '.C. [,m model. ThediscrepancyjbEtweenthe,two} mode _1s'incieaseswithincreasingtotalf,, ) ~ dose. The 10 rem total" dose values' for th'e linear quadratic model represent the I ~ lower extent of the band and the largest deviations from the linear model curve. Figures 5 through 8 present the results of these calculations for various entry exit dose fractions. Figure 5 represents the situation where there is no entry exit dose contribution. Figure 5a shows the effect r' spreading the dose over a number on workers on the potential individual risk to each worker. For the dose-squared relationship, the individual risk decreases as the square of the number of workers (n~2). For both the linear and linear quadratic relationships, the individual risk is approximately an inverse function of the number of workers (n" ), while the individual risk predicted by the fractional power relationship would be expected to fall off as the square-root of the number of workers used (n-0.5) This last relationship indicates that going ~ from 1 to 25 workers only reduces the individual risk by a factor of 5. i i figure 5b shows the potential population health impact relative to the impact received by having only I worker exposed. For the dose-squared relaticnship, 18

the potential population health impact decreases inversely to the number of ~ workers (n ): In _ nb(D /n)2 _1 (15) o I1 - bD z ii o The collective dose and, consequently, the population health impact predicted .:- ~... . ~ by the linear model remains constant (equation 11). The linear quadratic model .a -. ~. .also predicts'a' relatively constant potential health. impactL If_ the f ractional;_ _.. w u.:, w n <..-... .a. v.m. x, w x m.w e. m w w na m n= m m a c w m a x power relationship is assumed, then the potential "p~opulation' health ' impact would - m 4 1 7'i r/ T :I r "-~~7' T .._ _ _ _. _ _ u,m._. , w_' ..-.--m_& be expected to increase as the square root of the number of workers Lsed:. - o J x ~ D nc ( 05=cD 0*5 n(n -0*5 = c0 0*5 0*5 I =nR = n A n o o -I" = c D 0. ! n. 5 0 = n.5 (16) 0 and 05 I cD 1 o As the entry-exit dose fraction increases, substitution of additkaal workers results in less reduction in individual risks and proportionally greater pre-4 dicted potential population health impacts as shown in Figures 6 through 8. For a high entry-exit dose contribution (EEDF = 0.20), and an assumed frac-tional power dose-effect relationship, the use of 25 workers rather than I worker would be expected to decrease the individual worker risk by only 55% and would increase the total predicted population health impact 11-fold. ._.s- \\ For the quadratic or dose-squared relationship, there is an apparent minimum population health impact. This minitum is reached when j i 1 n=2e (17) 19 N

.~ i I . o ag -.y i a l ' r 'd. < 3N ,6 v. i l y ;; y is s 1 a. ..~. . m. . a s'11 -may be verified by taking the derivative of equation (14). This indicates ~ (,a 77.. s v o,' v,y, th5t.. if a dose-squared dese effect relationship were valid, it would be 43 _.,m m 1 theoretically possible to minimize the potential health impact by using an \\ A _ optimal number of workers indicated by equation (17). i a. v f, a t 5 & i ...n... e i _. 2.. =.:.,- o .s % -,,e.a ,a,p w.. ,,,e m,e C - -e v

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'l T s Ii i'I=l'a iiI'I 0 801 8' ' ' ' ' ' ' ' ' ' ' ' ' ' ' a' ' ' ' ' a' ' '~ 0.001 i ='s 10 is 20 25 1,' c s < ; i 10 Il , ;r NUMBER OF WORKERS ~ k ; NUMBER OF WORKERS Figure 5. Variation of the potential individual risk (a) and the potential health impact on the exposed population (b) as a function of the assumed dose-effect relationship and the number of individuals exposed. The results are plotted relative to the individual risk and health impact if only one person were exposed. The bands depicted for the lineardquadratic relationship (as D + a2 D2) represent initial radiation doses ranging from 0.05 rem (upper bound) to 10 rem (lower bound). The calculatibns' are for the case where there ' are no entry-exit doses. F l 's 4Mf f 3 j 2 4: =. e

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v5 p, [if 4 -- c' ^ p' ',f } g y,, 4.,w.t, p: - a a i j pt. 4 ( 1 5 0.001 I'I'Ii'Ii'IiIi' O.001 'I'II'I1 I'M 1 5 10 15 20 3 1 -6: it 15 3 3 30 NUMBER CF WORKERS NUMBER OF WORKERS y Figure 6. Variation of the potential individual

  • risk (a) and the potential health impact on the exposed population (b) as a function of the assumed dose-effect relationship and theinumber of iridividuals exposed.

The results are plotted relative to the individual risk and health impact if'only one person were exposed. The bands depicted for the linear quadratic relationship (at D + a2 D2)~repr+,ent initial radiation doses ranging from 0.05 rem (upper bound) to 10. rem (lower bound). The calculations are for a 1% entry - 1% exit dose (EEDF = s.02). j4 gg: / ' j

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i i IMPLICATIONS FOR RADIATION PROTECTION STRATEGIES The policy impM cations of the preceding analyses will depend upon the desired societal objective to be attained. If the objective is to minimize individual t risk then, regardless of which dose-<"fect relationship is valid, radiation ' doses [should be spread over many individuals as this will produce the smallest *

M.m::L c w k. c z x m c m,
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.sn- .t r~ x.e.e individual risk in all cases. The consequences of this action in some of the.. % / r ~ =-~=. -.....=...:-.r_.=

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v>v~e m.s.n C.ases y ami~ned could.increa,w me*~ m ~tw ~l potential health _ impact on the exp.osed _..n' se_the overal . a., -_~..m.,~-.--..~,..~..,-m y-a ul a t i,,o nP~'~""'~m."ngm~".~*.".. ",~w~.,'~ ~~n"n-'",,~ ~~~~^n",," """ ~--- n". ~~ '- W -, n,- p,- .. mm.n ,m r - z.a w n - w r ~ ::u:~;'m gy::;m.u.;:: x L L. + 4 An alternate objective could be to minimize the total potential public health impact. As shown by the preceding analyses, this objective may not necessarily j be attained by minimizing individual risks. In fact, in some of the situations analyzed, this objective may require individual risks to be increased. If the minimization of the potential population health impact is the desired objective, then individual risks must also be controlled to ensure that the ~ distribution of risk does not fall excessively on small segments of the popu- + 4 -y lation. The appropriate radiation protection strategy to minimize the potential j health impact will be influenced by which dose effect relationship appears to best be supported 'by the available scientific evidence. The conclusions drawn from analysis of both individual risk and the potential x health impact on the exposed population differ sharply from conclusions postu-lated from individual risk considerations alone. If the health risk from t low-level ionizing radiation falls off with decreasing dose less rapidly than r the linear extrapolation predicts, individual risks will be greater than

{

25 ^ i

~ 4 j current estimates. Reduction in the 'imits for allowable radiation exposures could result in spreading these exposures over an increased number of workers. If the actual dose-effect relationship is a fractional power relationship such as D.5, this action could result in a greatly increased potential health impact 0 on the exposed population and only slight reductions in individual risks. To ' minimize the public health impact, the appropriate protection strategy would be "t6~ex' pose as few people as possible, even though in~dividual'rish would be -.. m. h...L.9Teater<' " " '~ f i - -.- - ~ ' ; " " " Nu GTSA A?hiirw:mw ha m Ed5:dTruma,mn.~w$b15.1w: :L. w+ .r ~ ~ s k u.a u L L s r, n n i, M a w ;n. k sa;, w. ~ a x n n w.., u.- -, a._.e ~ .. a..w '?' ^.T" T2~ . ~ ~ ~. ~ - n... s,m, If the, actual, risk from low-level, radiation decreases more rapidly with decreas.,. ) . _ ~ - -. _ a J -... ^ ' ~ ingidose~than predicted by a lineai' relationship, such as predicted by a quad-ratic or dose-squared relationship, then reducing individual doses by spreading exposures over c. greater number of workers would be expected to result in decreases in both individual risks and in the potential public health impact. In this case, a reduction in the allowable dose limits could result in signifi-cant benefits to health protection, even though the actual risks from low-level radiation would be much less than the current estimates. In practice, however, the use of a large number of workers would be limited by the. increased labor j costs and by the availability of skilled manpower. i ]f the linear quadratic model is the most accurate description of the relationship between ionizing radiation and health ef fects, the risks from low-level low-LET radiation are slightly overestimated by the linear assumption presently used. s Es'timates of the degree of overestimation ran[,e between 2 and 3 J [UNSCEAR-77 NCRP-80), with a value of approximately 3 drawn from this analysis and a factor of 2 conservatively astumed by the NAS-BEIR Committec [NAS-80'J. If either the linear quatatic or linear relationships best describe the actual dore-effec *. 26

I 1 1 relationship, the potential public health impact would theoretically be insen-l sitive to the number of workers actually exposed. Considerations, such as l entry-exit dose contributions, would indicate that spreading radiation expo-i sures over large nu.nbers of workers would be counter productive to decreasing the public health impact. Mh.s .e .. age % y sq n. a sh 'er a v4. Wh NI* P 4 -4$ sd. ...ui.Muh Both experimental data [NCRP-80) 'and the results of human epidemiological a- ~ :=;. w- ...a ..+ .:.,.~ :.. Y .. studies [NAS-80). indicate..that T. linear-ciuadratic dose-response relationship _.i_ ~ n &Ga &:T&TG;&T,uu=%Gha 22hCkweasi!&&& %d&&MR i ~ .5 ~ n appears to best depict the data for low-LET rad *ation ~such as beta, gamma, and a-u a z., C f.*J" N C T/;;21CriI C C.Tr a l-*C25 T r %7';r C C.77*'"" r:.~> ) .X-radiation... These data also indicate that the linear dose-response relation -+- - ( 1 , s

~~
a. -

n :: =u. = ~ =m z z:.. 3 %::T: : :: ~ ship appears to best describe the effects of neutrons; at least at low-to-intermediate doses. If these relationships are correct, then both individual rrdiation doses and the r.ollective dose to the exposed population need to be kept "as low as is reasonably achievable" in order to minimize the potential health impact and individual risk. The current radiation protection philosophy is based upon these objectives. ACKNOWLEDMAENb n The author expresses his appreciation to Mr. Robert Minogue who suggested this study and to Drs. Michael Parsont, Reginald Gotchy, and Shlomo Yaniv and f Mr. John C. Guibert who provided helpful comments on the drif' ~. A e t 27 C

1 REFERENCES i 1 1. [BAUM-73] J. W. Baum, " Population Heterogeneity Hypothesis on Radiat. ion Induced Cancer"; Health Physics', 2_5: 97-104 (August 3973). 2. [BAUM-76] J. W. Baum,." Multiple Simultaneous Event Model for Radiation ..,..a

1. ' -

Carcinogenesis", Health Physics, 30:.. 85-90 (January _1976). iCan T ~T~ .= w w ;.w:

.,x::

'w a.w

. n z m a = s..L>,4:a w... z w.,h:

n .s 'u,. *"; f ju. g M. d ka,,lr G f ',,z x ; w w g Nu;: 2, =,. <, w.,>+ % d5xkisn*W d M.:::b W & bg &w.s? ' ..-,...wr~==,.,~=.,=..;~:u._u.

==

n...

.w, a -_ BRO-77]a.J M '.. Bi o.w. n, =",The, St.iap.. - .-...the D.ose-Re.s.. _o..n.s..e.. Curv.e f. o.r Ra.d. iati p-~E,wy'. .-o. ~..m. L,. z, u e- - c.wT m y 3.,. e of. on-on. w _. _..~. ~nr.n n.,..,,-~_n.~,.-...,-~,-:~-._.,-~~~n r.w ~.?~ mcf.mm,4 ~,.rcin,ogenesis - Extr'apolation,t_o low Do,ses";' Radiation Research, _71.i~~~ ' ~'"'~ ...g-

  • Ca

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- ! - r ^^ i n 4. [ICRP-77] International Commission on Radiological Protection, " Recommendations of the International Commission on Radiological Protection", ~~ Annuals of the ICRP, I (3) (1977). t n. 4 .x 5. [KEL-72] A. M. Kellerer and H.'11. Rossi, "The Theory of Dual Radiation Action", Current Top 1c.s in Radiation Research Quarterly, 9: 85-158 (1972).. 6. [KEL-78] A. 'M. Kellerer and H. H. Rossi, "A Generalized Formulation of ' ' 4 .j Dual Radiation Action", Radiation Research, 75: 471-4b8 (1978). 4 l 7. [NAS-80] National Academy of Sciences - National Research Council, ~ ~~ ' ' ' Committee on the Biological Effects of Ionizing Radiation, The Effects ( on Populations of Exposure to Low-Level Ionizing Radiation, NAS, Washington, D.C. (1980).. '.s ,r z .t [A; --

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