CARCINOGEN ASSESSMENT GROUP'S

                   PRELIMINARY REPORT ON

       POPULATION RISK TO AMBIENT COKE OVEN EXPOSURES
This document is being released by EPA for external review
                                         MAR 2 4 1973
                                        ALBERT,M.D
                                 CHAIRAN   .,.
Elizabeth L. Anderson, Ph. D.
I. Nathan Dubin, M.D.
Charalingayya Hiremath, Ph. D.
Robert McGaughy, Ph. D.
Lakshmi  Mi shra, Ph. D. ..
Ruth Pertel ,• Ph. D.
Wade T.  Richardson, J. D.
Dharm Singh, OVM, Ph.. 0.
Todd W.  Thorslund,  Sc. D.
Adrienne Zahner, Ph. D.

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  Introduction and Summary


       As requested in a memo from Joseph Padgett dated April 5, 1977

  the CAG.;.has. done an analysis- of the possible excess lung cancer risk


  in populations living in the vicinity of coke ovens..  There is very

  substantial epidemiological evidence that exposure to coke oven

    ;                     "     •'                   *
  emissions can induce- an excess risk of cancer in workers engaged in the


.  production of coke or in the manufacturer of coal gas.  This excess risk

  is mainly due to a higher than expected death rate from lung cancer and

  to a much lesser extent from a higher than expected death rate from'


  cancers of-the bladder.,, prostate, pancreas and large intestine.

       There are major difficulties in predicting- the cancer risk to

  populations living in the vicinity of coke ovens.  The chemical com-


  position of emissions from-.coke ovens is exceedingly complex.  These


  effluents do contain known carcinogens, particularly those belonging to

  the class of polycyclic organics.  However, by analogy with cigarette


  smoke, the overall carcinogenic effect of the mixture may be greater

  than that ascribible to identified carcinogens.  As the emissions move


  away from the coke ovens, it is possible that the chemical composition


  and the associated carcinogenic potency of the material may change

  significantly.  The best that can be done under- the circumstances is to
        "•,''"          -   "       -     ••'"•»
  use an indicator such as benzene-soluble organics (BSD) as a guide to

  the exposure of populations living near coke ovens.  Unfortunately,


 - there are no animal inhalation studies with coke oven emissions and

  consequently no evidence on the extent to which dilution and aging

                            i
  of emissions affects its carcinogenic potential or the most appropriate

  indicator to use for carcinogenic effect.

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                               -2- .


Furthermore, there are no- epidemiological studies of  the cancer

exposure of populations, living near coke,ovens or gas works as a

basis-.- for. estimating the: magnitude of the excess cancer risk,, although

such studies would necessarily provide only a crude measure of the

carcinogenic effects of coke oven emissions.
              .                                  s
     In the analysis presented here,, we 'have used the data from a

series of studies by Lloyd, Redmond, Mazuiridar and co-workers- (1-4)

on; cancer mortality in relation to ESO exposure in coke oven workers

and other workers in the steel industry.  Tnis epideir.iological dose-

response data for lung cancer is extrapolated on the  basis of a linear

non-threshold/ dose-response relationship to .provide estimates of 'the

excess risk of lung cancer in populations living at various distances

from, coke ovens-  The. exposure estimates and the size of the population

groups- are taken from an assessment by the Stanford Research Institute

(7).  The validity of.the exposure assessment is outside the CAG's purview.

     The risk assessment for lung cancer is summarized in Table 4.  The

number of people exposed to coke oven emissions is on the order of

fifteen million.  About fourteen million people have  a lifetime excess

lung cancer risk of about 6 chances in 10,000.  About one million have

a lifetime excess lung cancer risk of 1 chance in 1,000.  The remaining

100,000 people have a lifetime excess lung cancer risk which ranges from

about 2 chances in a 1000 to 6 chances in 1000 depending on where they

live.  Without any significant coke oven exposure, che lifetime chance

of dying, of lung cancer is 3.29%.  For the  100,000 highest  exposed people,

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                            -3-




there is a.:0.3% to.0.6£ excess chance of dying of  lung  cancer.




For., the. rase of the 15 million people the excess is about. 0.1%.




The total, number of excess,  lung cancer deaths is about  150 cases




per year.    •



     These estimates should, be regarded as crude and probably.



conservative; i.e., on the  high sida.

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              Estimated  Risk  Models  Based -on Steel  Workers'

                     Exposure Co  Coke Oven  Emissions


     • •   '    ••                                                          1-4
         A.  series- of  studies-  by. Lloyd,  Redmond,  Mazumdari  and  co-workers



has  established a strong. relationship, between  the. total  exposure  to  coke



oven- emissions- and an increased  risk of death, due  to  lung cancer.



        'Land'  in an  OSHA hearing on coke oven  standards  used  the  most  up-

                                                   f-

to-date  summary of the  data  based on. the cited  studies to estimate risk



to coke  oven, workers.   These data were supplied to EPA and. were further



summarized and adjusted' in- a manner shown  and  explained,  in Table  1.



         These-, data represent changing  exposures to individuals over  seg-



ments of their life  spans with an observation of mortality over a dif-




ferent and, at  least, partially  overlapping additional segment of their



life spans.   From this  fragmented type of  data, we wish  to predict the



effect, of  a constant exposure over  the individual's entire life span to



the  probability .of the  individual'^ ultimate death being  due  to lung



cancer.





Weibull Model



         To obtain a  lifetime probability estimate, it is  necessary to



relate the "instantaneous probability" of  death due to lung cancer to


                                               6
exposure and  age.  Following Armitage and Doll we assume that the instantane-



ous  probability of death may be  expressed  as





         h(t)
where t is the age of the individual, x is  the  level of  exposure,  and



Y, ct, & and a are unknown parameters  to be  estimated from  the data shown



in Table 1.



        The parameters were estimated using weighted nonlinear  least

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                                  _ 5 -
squares where  the  weights  were taken equal to the person-year's of obser-




vation.  This  resulted in  estimates






        Y - 3..76934  '".-'''      .                •




        a -  .27750 x 10"8




        B--  .19248 x. 10~19'

                                                  *•


        ra = 1.21110






        Oftea  for  theoretical reasons  the parameter m is  taken as an




integer, giving an approximate "m-hit"  model.  If we restrict m = 1, the




best fitting integer,  we have






        Y = 4.32324




        a »  ,2142  x  10"9




        0 -  .63192 x, lO'11






        In Appendix  I  the  lifetime  risk of lung cancer and  the expected




lifespan were derived, giving
        n V»i        (a + Sxm)     _ a + PY   o + PY
        Q r») =	= 	 	1—


         2     '.(a + 6xm + PY)       1+_6jsL


                                         a + pY
and
            (a + Sx  4- p)





                                         Y
respectively.  The only unknown term is p  ,  the  term  for non-lung-cancer


                          Y

deaths.  We-can estimate p  in the follox^ing manner.  The median  survival




time may be expressed as t  where

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                                  -6-
       , 1/2'
                            pY)tY
or
        pv
               tf
      x is, in  this; case-,  the  national average ambient exposure.  In the


SRI Report  on  coke oven exposure,,  it. is- estimated  that the 3SO Levels


arer


        4.20 in cities  containing coke- ovens


        3.75 in cities  not  containing coke  ovens


          .95, in rural areas



        Assuming- a locational  distribution  of  the average American of


10%, 65%  and 25%, respectively,  in  the three areas,  the. national average


is estimated as x = 4.2(.l) -f-  3.75(.65) •+ .95(.25)  => 3.095, so chat
            65..36T
where 65.36 is the median life  span  of  the  U.S.  nonwhite  male based on

   ...             8            Y                                -9
1971. Vital Statistics.   The  term- p1  is  estimated  to  be 9.60347  x 10  ,

        • •-'-'               —8            • ':•
when m = 1,. and 9.65501 x 10    when  tn »  1.2111.


        Substituting  the estimated parameters  into  the  risk  and  expected


lifetime equations, we obtain the results shown  in  Table  2,  column 1.


        However, this equation,  is really strictly  applicable only to a.


small subset of the U.S. population,  namely the  black northern raale who


was healthy enough to have a  physically  demanding  job such as that of  a

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                               •   - 7  -






steel worker.  This.is  true  since these  are  essentially  the  characteris-



tics that the sample of workers  in the epidemiological study possesses.



In making a prediction  about the effect  of coke  oven emissions  on the



U.S. population; in general,  one  can either use  the. equation  based on this



highly nonrandom sample or attempt to  extrapolate  the equation  in. some
                                      *


reasonable manner to the U.S.  population..          ^
Method for Adjusting- Equations  for One

Population to Apply to Another
        For small exposures, x,  the lifetime probability of a  tumor  may



be expressed as
                a + p'
In, this: case the relative ratio of  total  to "non-BSO-caused"  tumors  can



be obtained by dividing -Q-C00) by a/ (a -I- p^) , giving
                 a


                                                             •(•



If we assume that'this ratio is constant between race-'sex-ragion, etc.,



then
        a   a
             n
where S  and a . are national rates.  If we denote the national race as
       n      n


Q («) due to national average exposure of x  , we have that
 n                                         n

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                                   -8-
         ••  .    '•. ' 'a + B' xm'          1 '-t-  9x™

         ^r\ -           ••  m    -\s-        m     \
                .a  +&x+p    L-h.9x4.p
                  a    nan-     .   n    n   n
 The Cera 0 («) can be calculated from.Vital  Statistics  by the segmented
           n            ..'.-'        •'     .

 model discussed in Appendix I.. . Assuming Q 0=°)  to- be known,  we solve for
                                                      +•
                '   '            -                 •      *     .
 the unknown
         s-
             a
               n,
                                     n
         Thus the adjusted"lifetime probability  of  an  individual randomly

 selected from the U.S. population exposed  to  a  level  x of BSO may be

 written as
         - f ,     1 + 9xm   '  1 + s + 1 -i- s

          2    = 1 + 9xm + s = 	8?	
                                     1 + s
         Using 1971 Vital Statistics for total U.S.  population,  ICD Code

 160-163, for respiratory system cancer rates, we  calculated  that Q C05)  =

 .0343.   However, since lung cancer: ICD 162 makes  up only  a  fraction,

-68,^17/72,898 = .9413, of the total respiratory cancer  in 1971,. the

 adjusted rate is estimated to be Q (°°) =  .03"43 x  .9413  =  .0323.   Thus

 we .estimate s = (.0323*  - 1)(1 + 3.095m9>, and the equations  for the

 national average are obtained by substitution of  the appropriate terms

 and  are shown in Table 2, column 2.

         Under the assumption that the fraction  (E ) of  total average life

 span lived, given the same exposure, is the same  for black  males and the

 total general population, we have that

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                                 - 9 -
        E = E. (a.+ 8xm + pY / a H-B-X™ + pY)1/Y
             n.    ;  n

where x  ..Is 3.095 and E  is calculated  Co be 70.96.  The expected life
       n         .  , •  . n,
span equations are derived by substituting the appropriate numerical

values into the equations and are shown in Table 3.
                                 /                    * '
                                                      »
Effects o£ Coke Oven Emissions on U.S. Population

    .•   In the recent SRI Report, exposure of the U.S. population to coke

oven emissions was estimated.  The number of people in an exposure gradi-

ent is given in columns 1. and 2 in Table. 4 and is. taken from the SRI

Report.  The lifetime probability and the average length of life are

also shown in Table 4, based on the equations in Tables 2 and 3 where

the case m = 1, general U.S. population, was used.  The other terms were

obtained by simple arithematic in an obviously straigbtforvard manner,

where length of a- life span is assumed to be 70.96 years.  It is esti-

mated that about .2% of all lung cancer deaths per year are due to coke

oven emissions.

        It would be possible to generate tables similar to Table 4

using the other three equations; however, the results would be only mar-

ginally different.

        One potential criticism, of the use of the Weibull Model is that,

unlike other types of cancer,, the lung cancer rates have a tendency to

flatten out or even drop for older ages.  However, while this does appear

to be the case in the low exposure groups, it does not in the highly
                                             9
exposed group.  Also  as  noted  by Cook,  et  al.   this  flattening

appears to be explainable by a nonuniform historical exposure to envi-

ronmental .carcinogens and could be expected to. follow the power law in

the future if exposures were constant.

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                                 - 10.
        A: potential' method chat could be used  to overcome  this  difficulty



is to assume that the total, lung cancer rates, h-Ct),.  are  made  up  of  two



components:  a, a rate, of an age cohort which is not  Weibull;  and 9  ,



which, is..  We write
where a  is the nanexposed rate for the t   interval and T  is  the  average
       d               '                                   W



age of individuals, in the t   interval.  We are given 'the rates
                      .OgS)^-1






from 1971 Vital. Statistics tables so that we estimate





      ,. a.  --r  - YSCl.OgSoVf"1 ,
         C. -  - u. -      -       C





and the total rates given an environmental exposure of x are
        h2t = Et + Y6Tj"1(xm - 3,095m) .
        Using the constant segmented model discussed in Appendix  I,  the



increased lifetime probability where x = 10.9, m = 1,  total U.S.  popula-



tion, is.calculated to be 6.7 x 10  .  This is_yery close  to 6.04 x  10   ,

•      •           .


the value calculated for the full Weibull model.  Thus it  does not appear



that the fall'of cancer, rates for high age groups has  a major impact on



overall lifetime probability of lung cancer.

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                                    -XX-
                                 TABLE 1

                     SUMMARIZED DATA, OF STEEL WORKER
                     EXPOSURE TO COKE OVEN EMISSIONS
Defined
Age at
Entry;
25-34




35-44




45-54




55-69 •




Group
Dose Range
Nb-C 'exposed
0-99
100-199
.200-299
300+
Npt exposed
0-149
150-299
300-499
450+
Not exposed
. 0-249
250-449
450-699
700+
Not exposed
0-249
250-449
450-749
. 750+
Lung Cancer
Rate/Year
x 105
13.4
31, 2r .
0. .
99.0
130.6
2.4.7
0
67 . 2
110.0
246.7
150.4.-
'S5-.5
234.5
258.9
601.5
70.0
203 . 7
167.8
558.7
2,222.2
Person-
years of
Observation
22,045
3,202
2,658
, 3,030
3,06.2
16,277
2,388
2,976
.2,727
2,027
11,306
1,527-
1,706
1,545
1,330
5,713
491
596
716
450
. Average Age
During
Observation
Periodb
36.4
,, 35.1
36.0
36.3
37.7
46.3
45.2
46.1
46.1
47.4
55.8
55.3
55.9
56.0
56.2
64.9
65.6
64.9
64.5
65.9
Life time-
Aver age
Exposure
to BSOC
4.2
20.5
67.2
109.1
160,5
4.2
28.4
82.3
130.1
201.5
4.2
47.9
107.4
169.4
263.3
4.2
43.7
89.8
160.5
681.7
     Units, are the sum,, for all jobs, of the products of the mg/m  of BSO
in the air associated with the job and the length of time in months worked
at the job.

     The- term t is the average age at the start of the observation period
+ 1/2 total years observed -r total number of individuals alive at the
start of the observation period.

     The term x is the total ng/m -months T total months lived by the end
of the observation period • fraction of year spent on the job + 4.2 = the
background BSO levels in cities containing coke ovens.  Units are
BSO.                                            •

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                    -12-
                  TABLE 2'
 LIFETIME PROBABILITY OF LUNG CANCER GIVEN
AVERAGE EXPOSURES OF x Ugm/m3 OF ESO
Case
m = 1
m .= 1.2111-
Population
Black-Urban-Hale
.02164 + .0006438*
1 > .0006438x
.02794 + .0001938X1'2111
1 + .0001938X1'2111
General U.S. Population
.02966
1 +
.03147
1 + .
> .OOOSS24x
.0003824*
•h .0002183K1'2111
0002183X1'2111

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                 TABLE 3
EXPECTED AVERAGE LIFE SPAN GIVEN AVERAGE

  LIFETIME EXPOSURE TO x ugm/m3 OF BSO
Case
m 1
m — i
«i 1 0 1 1 1
ra — 1 . /Ill
Population
Black-Urban.-Male •
i
109.907
93131
, (9.81589 + .0063192x) A
119.663
(9.93251 + .00192A8X1'2111)'26526
Total U.S, Population
120.408
OOl -11
(9,81589 t .0063192x) A
130. A92
(9.93251 -t- .0091248X1-2111)-26526
»»
                                                                              co
                                                                               I

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TABLE 4- ESTIMATED EFFECTS OF COKE OVEN EMISSIONS ON U.S. POPULATION UNDER
         WEIBULL PROBABILITY KOOCL HMERE "HIT PARAMETER" nv=l MO ADJUSTMENTS
         FCR TOTAL POPULATION RATES USED         '            ,'
.X =•
Exposure to
BSD irt/
-------
                              -15-'






 References:               .




 1..  Lloyd J.W.,..-Lvindin F. E. Jr., Redmond C.K.  al.:  Long, term




     mortality study of steel workers. IV.  Mortality  by  work area..




     J. Occup.;. Med. 12: .151-157.  1970  .




 2.  Lloyd J.W. r  Long tern mortality study of  steelv.-orkers.  V.




     Respiratory cancer in coke plant workers.  J. Occup.  Med.  13:.




     53-68.  1971




 3.  Redmond C.K., Ciocco A:.,. Lloyd J.W..,. and Rush'II.W. :   Long-term




     mortality study of: steelworkers.  VI.  Mortality  from malignant




     neoplasms among coke overt workers.  J. Occup. lied. 14:   621-629.




     1972                             .




4.    Mazumdar S. , Redmond C.K.., Sollecito W. and Sussman  N.:   Aa




     epidemiolog.ical study of exposure- to coal  tar pitch,  volatiles




     among coke oven workers.  J. Air Pollution Control Assn.   25:




     382-389.  1975




 5.  Land C.E.:  Presentation for OSHA hearings on coke oven  standards,




     May 4, 1976




 6.  Armitage, P. and Doll R:  The age distribution cf cancer  and




     a multi-stage theory of carcinogensis.  British J. Cancer, Vol 18:




   :  1-12, 19\54                        ..




 7. .SRI draft report by Suta B.E.:  Human population  exposures to  coke




     oven atmospheric emissions, June 1977

-------
                               -16-




8.  U.S. Dept. of Health, Education and "elfare:  Vital  Statistics  of




  .  the United States, 1.971, Vol. II—Mortality, Part A.  Supt.  of



    Documents, U.S. Gdvt. Printing Office.  Stock. No. Ol'7-022-00378-3



9.  Goolc P.J.., Doll R. and Eellingham S.A.:  A raathematicai model




    for the age distribution, of cancer in aanr Int. J. Cancer,  4



   ' (1969),. pp 93-112

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                                 -  17 -


COKE OVENS   .      .


        .  •    •;.  .- . •  :     .. •••.. APPENDIX: I
             ESTIMATION OF 'LIFETIME RISK OF DEATH AND YEARS
                  OF LIFE LOST DUE TO A SPECIFIC CAUSE
General Approach                  •                 •  *

        The estimation of the lifetime probability of a disease  in  the

presence- of competing causes of death and  the resulting life-shorting of

the disease is a problem that has received considerable attention.  Chiang

(1968, pp. 242-68) has given a general solution  to the problem using stan-.

dard methods in competing risk analysis.  Gail (1975),  using, these trethods,

derives explicit "figures of merit"  for measuring  the benefit of  reduced

exposure to environmental carcinogens.

        Following.Gail, we define the following  functions:


        S.(t) = P (survive to > t/c, is only cause of death}

        S2(t) = P (survive to > t/c_ is only cause of death)  ,


where c, and c~ make up all causes of death.  The hazard  function or age-

specific or instantaneous death rate is denoted  as h (t), h  (t),  respec-

tively, for causes- of death—c, -and c^.  Under the usual assumptions of

independence of c, and.c- the total probability  of survival  until time  t

and the total hazard from both causes are:  S(t) » S (t)S-(t) ,'

h(t) =h,(t) + h (t) , respectively and
        SCO' = e
                 t
                -/ h(u)du
                 o

-------
                                  -   18-
        It can be  readily  shown  thac  the  lifetime  probability  of  dying of
cause c_.  is.
                  h2(t)S(t)dt
and the expected survival time or lifetime is
        E' = / S,(t)h(t>tdt.
            o   •'.••.
Thus, given the specific functions. Vu(t) , h.(t.),  the. quantities  of  inter-

est Q2(°°) and E can be readily derived.

        For two common assumptions concerning  the  form of h, (t), h_(t),

the terms Q-O") » E are obtained in the next  two sections.


Constant Segmemfed Model

        Often the hazard or age-specific death rate  is assumed  to be  con-

stant over different time intervals but to change  at-interval boundaries.

Formally, let us assume the entire life span is broken up into m mutually

exclusive segments or intervals and the hazard functions  are defined as  fel-

lows :
Time Interval
0 - t
: . ' 1
V^z

j-i j
t - »
m-l


^
a21
a'
?1
'V
h2(c)
a-
12
a22
a'
]2 .
\2
h(t)
a.
1
a2
a.
. J
a
m

-------
                                    19 ~
'This  is  the situation where, age-specific rates are estimated from narrow

 age intervals of typically 1 to- 10 years.

         In, this case, the function S('t) ,  t.  -  < t < t. ,  is


       •        ' '      '    •• t"~C-T 1
                           -/  a.du    -

         S(t).  = SCt.^) •  e °       = e.
 so that
                 m


                 m
                j-l
                   S(t        -  e
                        -(t.-t, ,)a.
                                     °2i/aj
        This.lifetime  probability may be viewed intuitively as the sum of

 the probability  of  m  mutually  independent events,  namely of dying of the

 specified, disease in  the  specified time interval.   Each of these m proba-

 bilities  is  the  product of  three probabilities:

     1.  Probability of death due to cause c.  given that death occurred

 in the j  ^  time  interval:
      2.. Probability  of  death between time t.  and t._.  given survival up
  to  time  t.  , :
P2.;» 1' - e

                                 ;  and
      3.  Probability  of  survival  until time t.-:

-------
                                    20
        P3i-•S(Vi)  '  n C1'P2k)
       •   3       J
 By definition  it  follows  that P_   = 1,  P   » 1,. and
      • The.expected  life  span  under  the. segmented model  may be expressed

as  '  .          •'''"•.       .  •              "    .         •
                            -Ct-t.   )o.
                                  J     J  ^t-c    )dt
                           -(t.-t.  ,)a.l      m
                                           .=   ?P   /a
Weibull Model

       Doll (1972) and others have pointed out that  the Weibull distribu-

tion often fits time- and dose-dependent carcinogen  response data.  One

form of a- generalized Weibull model is to assume that


       h2(t), = Y(u + Bx^'cT"1 ,               '


where t is the age of the individual, x is the level of exposure  to a

carcinogen and y> ct, 8, m are unknown parameters to  be estimated  from  the

data.  If we make the additional simplifying assumption that the  noncancer

cause of death c. is also Weibull in form with common parameter Y, then

-------
                                 - 21 -
        h1(t)  -
and  the quantities of, interest are
                -/Y(a + 3x
                (a + Sx^/Ca + Sxm + PT)
and'
       E - /T(a * 3xm * PY)tV(a- *  Sx  + p )cY dt
           o
                         -f. Bx"1 +  Y1/Y

-------
                           -22-
References.:



     1..  Chiang, C.L..:..  Introduction; to Stochastic Processes  in




     Biostatistics.  New York;  John Wiley,.1.968:



     2..  Dolly R.:..  The age distribution of cancer:  Implications  for




     model; of. carcinogenesis. J. Roy, Stat. Soc. A 134:   133-166,  1971



     3.  Gail, M.:  Measuring the benefit of reduced exposure to



     environmental carcinogens.  J. Chron. Dis.  28:  135-147,  1975

-------