RADIATION RESEARCH 174, 72–82 (2010)0033-7587/10 $15.00
g 2010 by Radiation Research Society.
All rights of reproduction in any form reserved.
DOI: 10.1667/RR2083.1

Radiation and Smoking Effects on Lung Cancer Incidence among Atomic
Bomb Survivors
Kyoji Furukawa,a,1 Dale L. Preston,b Stefan Lönn,c Sachiyo Funamoto,a Shuji Yonehara,d Takeshi Matsuo,eHiromi Egawa,f Shoji Tokuoka,a Kotaro Ozasa,a Fumiyoshi Kasagi,a Kazunori Kodamaa and Kiyohiko MabuchigaRadiation Effects Research Foundation, Hiroshima and Nagasaki, Japan; b Hirosoft International, Eureka, California; c Karolinska Institutet,
Stockholm, Sweden; d Welfare Association Onomichi General Hospital, Japan; e Nagasaki Health Promotion Corporation, Nagasaki, Japan;f
Hiroshima City Asa Hospital, Hiroshima, Japan; and g Division of Cancer Epidemiology & Genetics, National Cancer Institute,
Bethesda, Maryland

INTRODUCTION
Furukawa, K., Preston, D. L., Lönn, S., Funamoto, S.,
Yonehara, S., Matsuo, T., Egawa, H., Tokuoka, S., Ozasa, K.,
Kasagi, F., Kodama, K. and Mabuchi, K. Radiation andSmoking Effects on Lung Cancer Incidence among Atomic
Bomb Survivors. Radiat. Res. 174, 72–82 (2010).

Lung cancer is the most common cancer worldwide (1).
While lung cancer rates are largely determined bysmoking patterns, medical, occupational and environmental radiation exposures have also been shown toincrease risks of lung cancer (2). There is considerable
interest from both biological and practical perspectives in
the joint effect of radiation and smoking on lung cancer.
The Life Span Study (LSS) is a long-term continuing
follow-up of a cohort of atomic bomb survivors in
Hiroshima and Nagasaki, Japan, comprising a large
number of men and women exposed at all ages to a rangeof radiation doses from 0–4 Gy, mostly c rays, from thebombs. With detailed information on smoking available
for most cohort members, the LSS offers one of the best
opportunities for investigating the joint effects ofradiation and smoking on lung cancer risks.
Lung cancer is the second most common cancer in the
LSS, with world-population age-standardized rates of
76.8 per 100,000 persons for men and 25.1 for women.
LSS lung cancer rates are also strongly associated withradiation, with an estimated excess relative risk (ERR)
per Gy of 0.81 and excess absolute risk (EAR) of 7.5 per
10,000 person-year Gy (3). Those estimates do not take
into account a possible modifying effect of smoking on
the radiation risk. While earlier analyses (4, 5) of the
LSS data were unable to describe sufficiently the natureof the interaction between radiation and smoking forlung cancer risk, due mostly to limited numbers of cases,
the latest analysis by Pierce et al. (6) suggested that theinteraction was sub-multiplicative and consistent withadditivity.
The present study was based on lung cancer incidence
data from a special pathology review that provided
diagnostic confirmation on cases diagnosed between
1958 and 1999. A reassessment of smoking history dataassembled from multiple sources provided enoughdetailed information to consider models in which the

While radiation increases the risk of lung cancer among
members of the Life Span Study (LSS) cohort of atomic bomb
survivors, there are still important questions about the nature of
its interaction with smoking, the predominant cause of lungcancer. Among 105,404 LSS subjects, 1,803 primary lungcancer incident cases were identified for the period 1958–1999.
Individual smoking history information and the latest radiationdose estimates were used to investigate the joint effects ofradiation and smoking on lung cancer rates using Poissongrouped survival regression methods. Relative to never-smokers,
lung cancer risks increased with the amount and duration ofsmoking and decreased with time since quitting smoking at any
level of radiation exposure. Models assuming generalizedinteractions of smoking and radiation fit markedly better thansimple additive or multiplicative interaction models. The joint
effect appeared to be super-multiplicative for light/moderatesmokers, with a rapid increase in excess risk with smoking
intensity up to about 10 cigarettes per day, but additive or subadditive for heavy smokers smoking a pack or more per day,with little indication of any radiation-associated excess risk. The
gender-averaged excess relative risk per Gy of lung cancer (atage 70 after radiation exposure at 30) was estimated as 0.59
(95% confidence interval: 0.31–1.00) for nonsmokers with afemale:male ratio of 3.1. About one-third of the lung cancer
cases in this cohort were estimated to be attributable to smoking
while about 7% were associated with radiation. The joint effectof smoking and radiation on lung cancer in the LSS is dependent
on smoking intensity and is best described by the generalizedinteraction model rather than a simple additive or multiplicativemodel. g 2010 by Radiation Research Society

1 Address for correspondence: 5-2 Hijiyama Koen, Minami-ku,
Hiroshima, Japan, 732-0815; e-mail: furukawa@rerf.or.jp.

72

RADIATION AND SMOKING EFFECTS ON LUNG CANCER INCIDENCE

effect of cumulative amount smoked could be modified
by smoking duration and intensity and to consider boththe additive and multiplicative interaction models used inmany analyses of the joint effects of carcinogenic agentsand some useful generalizations of these models. In thisstudy, we present the results from the effort to evaluate alltypes of lung cancer as a group. We will report separately
on results regarding different histological types.
MATERIALS AND METHODS
Study Population and Case Ascertainment
The LSS cohort includes 120,321 residents of Hiroshima andNagasaki who were born prior to the atomic bombings in August1945 and were still alive on October 1, 1950. Additional details about
the cohort can be found in ref. (7). For the present analyses, we
excluded cohort members who could not be traced, had died or were
known to have had cancer prior to January 1, 1958 (8,396 subjects) or
those with radiation dose estimates not available (6,521 subjects),
resulting in a total of 105,404 eligible subjects. A special pathology
review provided diagnostic confirmation for lung cancer cases. The
primary sources used to identify potential cases for pathology review
were the Hiroshima and Nagasaki tumor and tissue registries.
Additional sources included the RERF autopsy program and deathcertificate data routinely obtained for LSS follow-up. The review
considered cases diagnosed through the end of 1999, resulting infollowed-up ages ranging from about 12 to more than 100 and ages atdiagnosis from 27 to 104.
An initial screening identified 5,711 LSS cohort members who werecoded as having tumors of the lung or related regions. Three study
pathologists independently reviewed those cases and developed aconsensus diagnosis for each potential case. The reviews were basedon all available information, including tumor tissue slides, pathology
and clinical records, and death certificates. Lung tumors werediagnosed using the latest WHO diagnostic criteria (8). The review
identified 2,446 lung tumors, including 2,368 cancers.
Cases were ineligible for the analyses if they were not the firstprimary tumors (242 cases) or were not classified as malignant tumors(52 cases). Cases were excluded if the individual was not resident inHiroshima or Nagasaki prefecture at the time of diagnosis (171 cases
diagnosed primarily from death certificate information), was diagnosed or lost to follow-up prior to 1958 (47 cases), or did not have aradiation dose estimate computed (131 cases). The primary analyses
described herein considered 1,803 primary lung cancer cases
diagnosed among 105,404 cohort members, including 40,980 subjects(677 cases) with no information on smoking status prior to thediagnosis date.
Radiation Dose and Smoking Information
Weighted DS02 (9) lung dose estimates computed as sum of the cray dose and 10 times the neutron dose were used for these analyses.
As in ref. (3), cohort members who were not in either Hiroshima or
Nagasaki at the times of the bombings were included in the analysis toimprove the characterization of the variation in the baseline (zerodose) lung cancer rates by age, gender and birth cohort. However,radiation effects were quantified in relation to rates for survivors who
were in the cities at the time of the bombings but received negligible
radiation from the bombs, due to their large distances from the
hypocenter.
Most of the data on smoking habits of LSS cohort members
comes from a series of mail surveys conducted between 1965 and1991. The 1965 survey was limited to men who were between ages
40 and 69, and the 1969 survey included only females. The 197873

and 1991 surveys included all surviving cohort members who werein the cities at the time of the bombings. Information on smoking
habits included amount smoked, duration of smoking, and, forpast-smokers, when he/she stopped. Additional information on
smoking was available from a series of questionnaires administered
to members of a fixed subset of the full LSS cohort known as the
Adult Health Study (AHS), who participated in biennial clinicalexaminations (10). We used summary information on smoking
history that made use of the AHS data for the previous analyses by
Pierce et al. (6).
Smoking history was summarized by an indicator of smoking status(never-smoker, past-smoker or current-smoker) at the time of thelatest information, age started smoking, age stopped smoking, theaverage number of cigarettes smoked per day, and the year in whichdata on smoking were first obtained. Age started smoking was
defined as the minimum starting age reported from all surveys towhich a person responded; a person was taken to be a past-smoker
only if they indicated that they had quit smoking at the time of theirmost recent survey response, and the number of cigarettes per day wasdefined as the average of the numbers of cigarettes per day over all
surveys in which a person reported having smoked. In some cases,
smokers did not answer questions about the amount smoked or age atstart of smoking. Values were imputed for those subjects as thegender- and birth cohort-specific mean values among smokers withcomplete information.
Smoking information was available for 62% of the eligible cohort
members. The smoking summary data were based on one or more of
the four mail surveys for 92% of the subjects with smoking data. The
amount smoked was imputed for 4% of those who indicated that they
had ever smoked while the age at the start of smoking was imputedfor 9.5% of this group. Additional information on smoking status in
the cohort is given in the Results section with further details
concerning the creation of the smoking summary variables in the
Supplementary Information.
Data Organization for Analyses
The risk analyses were based on incidence rates computed from atable of person-years and lung cancer cases stratified by generalfactors, radiation exposure-related factors, and smoking-related
factors. The general stratification factors included city (Hiroshima
and Nagasaki), gender, attained age (5-year categories from age 15 to
85 with categories for subjects less than 15 or 85 or more), and period(nine 5-year categories from 1961 through 1999 plus a category for
1958–1960). The radiation exposure-related stratification factors wereage at exposure (14 5-year categories to age 70 and a category for
subjects aged 70 or more), exposure status (within 3 km of the
hypocenter, 3–10 km from the hypocenter, or not in city), andradiation dose (14 categories with cutpoints at 0.005, 0.05, 0.1, 0.15,
0.2, 0.25, 0.5, 0.75, 1, 1.5, 2, 2.5 and 3 Gy).
The time-dependent smoking-related stratification factors used inanalyses were smoking status (unknown, never-, current- and pastsmoker), average number of cigarettes per day (seven categories with
cutpoints at 0, 7.5, 12.5, 17.5, 22.5 and 27.5 and a category for
unknown smoking status), years smoked (six categories with
cutpoints at 0, 5, 10, 20 and 30 for ever-smokers and categories fornever-smokers and unknown smoking status), and years sincequitting smoking (four categories with cutpoints at 5, 10 and 15 forpast-smokers and categories for unknown smoking status andcurrent-/never-smokers). Note that a smoker was considered as acurrent-smoker from the date at which he/she first provided
information on smoking habits to either the date of the end offollow-up or the date of reported cessation of smoking, whichevercame first. All cohort members were classified as unknown smoking
status prior to the date at which they first provided information on
smoking habits to avoid biasing risk estimates by overcounting
person years in known smoking-status categories.

74

FURUKAWA ET AL.

Since cancer case ascertainment is incomplete for subjects who haveleft the tumor registry catchment areas, it is necessary to allow for theeffects of migration on risk estimates. As in all recent analyses of the
LSS cancer incidence data, the analyses were limited to cases
diagnosed among residents of the tumor registry catchment areas.
Because individual residence history data were not available for all
cohort members, city-, gender-, age- and time-dependent residence
probabilities estimated from AHS clinical contact data were used tocompute migration-adjusted person years (11).
Statistical Analysis
1. Smoking and radiation joint effects
These analyses focused on the joint effects of radiation andsmoking in terms of risks relative to attained age (a), gender (g) andbirth cohort (b)-specific baseline rates for nonsmokers with noradiation exposure. That model can be written as l0(a,g,b)RR(C,D),
where RR is a relative risk function that depends on smoking-related
variables (C) and radiation dose-related variables (D). The smokingrelated variables included years smoked (y), cigarettes smoked per day(c), and years since last known quitting (q) for past smokers and otherfactors such as gender and birth cohort. The radiation-related
variables included dose (d) and effect modifiers such as age at
exposure (e), gender and attained age.
The simplest joint effects model is the additive excess relative risk
(ERR) model:RRðC,DÞ~1zwðC ÞzrðDÞ, ½Additive
where w and r are functions that describe the ERRs for smoking- andradiation-related variables, respectively. Under this model, smoking
and radiation have independent effects on the baseline rate fornonsmokers. The most commonly used alternative to the additive
ERR model is the multiplicative ERR model:

simplest cases, is defined as the product of intensity and duration ofsmoking. In this study, we expressed the cumulative amount by packyears (p), defined as the product of packs smoked per day (one pack5 20 cigarettes) and years smoked (defined as the maximum of 0 and‘‘attained age – age started smoking’’). Then we described thesmoking ERR as the product of a function of pack-years and an effectmodification term that was allowed to depend on smoking intensity(c), duration (y), time since quitting (q) with allowance for birthcohort (b) and gender (g). Note that time since quitting is defined as
the maximum of 0 and attained age – age at quitting smoking.
After preliminary analyses, we decided to use a model for thesmoking ERR w(C) of the formnowsmk ðC Þ~w0g pexp w1g bzl1 logð yÞzl2 logð yÞ2 zn logðqz1Þ
~w0g exp w1g b cy1zl1 zl2 log (y) (qz1)nfor follow-up periods during which smoking history information wasavailable or wunksmk ðg,bÞ~jg,b for periods during which smoking
status was unknown. In this model, smoking duration (y) and birthyear (b) were centered so that w0g is interpreted as the gender-specificrisk for a current smoker who was born in 1915 and smoked a pack ofcigarettes per day for 50 years. The smoking ERR included a genderdependent birth-cohort effect (exp{w1gb}), a time-since-quitting
effect ((q z 1)n), and a smoking-duration effect (yl1 zl2 log (y) ). We
considered more general models for the smoking-intensity effects,
such as those considered in ref. (12), but did not find any evidence
that the power of the intensity effects differed from one in the data.
We also found no indication of gender dependence in the duration or
intensity effects. For follow-up during which smoking status is
unknown, the ‘‘smoking’’ effect was allowed to depend on gender andbirth-cohort strata.
4. Radiation effect models

RRðC,DÞ~½1zwðC Þ½1zrðDÞ~1zwðC ÞzrðDÞzwðC Þ rðDÞ:½MultiplicativeWith this model, a given radiation exposure (or a given smoking
history) increases the risk by the same proportion for any smoking
history (or radiation exposure).
These two models are special cases of more generalized joint effectmodels, which we call the generalized additive and multiplicative ERR
interaction models:RRðC,DÞ~1zwðC ÞzrðDÞ vðC Þ;½Generalized additiveRRðC,DÞ~½1zwðC Þ½1zrðDÞ vðC Þ,½Generalized multiplicative

where v is a function of smoking variables with v(C) 5 1 for lifelong
nonsmokers. In these models, the effect of smoking on the radiation dose
response is neither independent of dose (as in the simple additive model)
nor constrained to be proportional to the main effect of smoking, w(C)(as in the simple multiplicative model). A form of the generalized
additive interaction model was considered by Pierce et al. (6).
2. Baseline rate (zero-dose, nonsmokers) model
The baseline rate model allows for gender-specific rates. The log rate
was assumed to be proportional to a quadratic function of log attained
age with a birth-cohort effect that is proportional to the year of birth.
The baseline rate model also included a multiplicative city effect.
3. Smoking effect model
Analyses of smoking effects on lung cancer risk often describe theeffects in terms of the cumulative amount of smoking which, in the

As in most recent work on risk modeling in the LSS (3, 13),
radiation main effects, r(D), were modeled as a product of a genderspecific dose–response shape function, g(d,g), and an effect-modification function e(a,e) 5 ab exp(ce). In fitting the models, attained age
and age at exposure were scaled so that the gender-specific dose effectg(d,g) corresponds to the risk for a 70-year-old survivor who was
exposed at 30 years of age. The excess risk is often summarized by agender-averaged value, which is defined in this paper as theunweighted mean of the dose–effect parameters for men and women.
The shape of a dose response, g(d,g), considered in the modelingincluded linear (dgd ); linear-quadratic (dg(d z dd 2)), linear-spline (dg[dz h(d 2 d0)I(d . d0)]), and linear-threshold (dg (d 2 d0)I(d . d0))models.
Poisson regression maximum likelihood methods were used forparameter estimation, hypothesis testing and the computation ofconfidence intervals (CI) for specific parameters. Model fitting was
carried out using Epicure (14) and the generalized non-linear model
package (gnm) in R (15). We also used the Akaike Information
Criteria (AIC) (16) for comparison of non-nested models involving
different numbers of parameters. The program codes used to draw
graphs in the Results section are available upon request from theauthors.

RESULTS

Data on smoking were available for about 60% of
men and 64% of women. Roughly 85% of the men and
18% of the women who provided information on
smoking habits indicated that they had ever smoked.
The proportion of ever-smokers among men was similar

RADIATION AND SMOKING EFFECTS ON LUNG CANCER INCIDENCE

over birth-cohort and radiation dose categories. Women
who were over 20 at the time of the bombings were
somewhat more likely to have smoked than younger
women, and the proportion of ever-smokers increasedslightly with decreasing distance from the hypocenters(and hence with increasing dose). Men reported smoking
about twice as many cigarettes per day (mean 19.6) as
women (mean 10.6) and tended to start smoking
younger (mean starting ages of 21 and 31.6, respectively). About one-third of ever-smokers reported having
stopped smoking prior to the last survey to which they
responded. By the end of follow-up, those who reported
having stopped had smoked for roughly 20 years lessthan those who did not. Additional information on
smoking status and the availability of data on smoking
is given in the Supplementary Information (Table S1).
Table 1 summarizes the distribution of the first primarylung cancers, study population and crude incidence rates
by smoking status at the end of follow-up, age at exposureand radiation dose by gender. Incidence rates were higher
for current smokers than for never- or past-smokers.
Crude rates for a given smoking category were about twice
as high for men as for women and increased withincreasing age at exposure (or decreasing calendar yearof birth) as well as with radiation dose.
Nonsmoker Baseline Rates and Smoking Effects
Smoking effects were modeled using ERR models andexpressed relative to gender-specific baseline rates fornonsmokers with allowance for radiation effects. As
discussed later, neither the baseline rate nor thesmoking-effect parameter estimates were particularly
sensitive to the form of the radiation-by-smoking
interaction, and therefore the main results in this section
were based on the generalized multiplicative interaction
model unless otherwise noted.
The nonsmoker baseline rates for men and women
increased markedly with attained age. There was astatistically significant gender difference in the attainedage trend (P 5 0.05). The increase was well describedusing a simple Armitage-Doll-like model in which therates were proportional to attained age to the power 5.6(95% CI: 5.2, 6.1) with age-specific rates for womenbeing 61% of those for men (95% CI: 48%, 80%). Agespecific rates exhibited a statistically significant (P ,0.001) increase of 17% (95% CI: 10%, 25%) per decadein year of birth. The final baseline rate model allowed amore rapid increase in the risk at younger ages, which
resulted in a statistically significant improvement in thefit (P , 0.001).
Table 2A presents smoking-effect parameters estimated using the generalized multiplicative and simple
additive radiation-smoking interaction models, together
with modifying effects of birth year and smoking
duration. Assumptions about the nature of the interac-

75tion had little effect on the smoking risk estimates. With
the generalized multiplicative model, the gender-averaged ERR associated with smoking 20 cigarettes per day
for 50 years (i.e., 50 pack-years) for an unexposed
individual born in 1915 was estimated to be 4.7 (95% CI:
3.6, 5.9). This was only slightly lower than that of 5.3
based on the additive model. The ERR associated withsmoking was statistically significantly higher for womenthan for men (P 5 0.02), with the gender difference
being greater with the additive model estimates. Agespecific ERRs for a given smoking history exhibited astatistically significant increase (P , 0.001) withdecreasing birth year without an indication of a genderdifference (P 5 0.4, data not shown).
In our modeling of the smoking ERR, the pack-year
effect was allowed to vary depending on smoking
duration and/or intensity. The addition of linear andquadratic effects of smoking intensity (packs per day)did not significantly improve the basic pack-year effectmodel (P 5 0.2). However, the addition of both linear
and quadratic log-linear terms in log smoking duration
significantly improved the basic model (P 5 0.001).
While the pack-year effect was highly statisticallysignificant, the negative coefficients for the duration
effects imply that the increase in rates is not linear inyears smoked and that this departure from linearity
becomes more marked at longer durations. Under thisduration-modified pack-year model, smoking had little
impact on lung cancer rates for the first 20 years ofsmoking, after which the effect of smoking increases
dramatically (Fig. 1A). This model implies a reduced
potency at higher smoking intensities (Fig. 1B) like thepattern suggested in refs. (12, 17), where the departuresfrom a linear pack-years effect were attributed tomodifying effects of intensity and not duration.
There was a statistically significant time-dependent
reduction (P , 0.006) in the ERR for those whoreported that they had stopped smoking. The estimateddecline for past smokers (relative to that for nonsmokersof the same age) was approximately proportional to one
over the square root of time since quitting. While thesmoking ERR declines after smoking cessation(Fig. 2A), lung cancer rates for past-smokers neverreturn to the level for nonsmokers (Fig. 2B). Although
women have larger ERRs than men for those smoking
patterns, the absolute rate estimates for male and female
smokers are similar (Fig. 2B).
Radiation Effects and Radiation-by-Smoking Interactions
Table 2B presents parameter estimates for radiation
effects and their risk-modifying factors, together with
95% CIs and information about the fit, from the ERR
interaction models described earlier. The table also
includes results from a model in which the radiationeffect parameters were estimated without allowance for

76

FURUKAWA ET AL.

TABLE 1
Distribution of Cases, Study Population and Crude Incidence Ratesa in Final Smoking Status Categories by Age at
Radiation Exposure, and Radiation Dose by Gender
Smoking status
Never

Past

Current

Unknown

Total

Total

Cases
PeopleRate

304
35,555
4.88

142
9,022
9.85
Age at exposure, years

680
19,84722.90

677
40,9803.89

1,803
105,404
6.43

0–9

Cases
PeopleRate
Cases
PeopleRate
Cases
PeopleRate
Cases
People
Rate

17
8,160
1.25
49
8,654
2.99
150
12,700
6.00
88
6,041
11.89

7
1,930
2.83
29
2,363
8.33
60
2,991
9.63
46
1,738
20.56
Men by dose, Gy

21
4,974
3.26
85
4,675
11.94
346
6,181
29.55
228
4,016
51.65

31
7,617
0.7
87
7,365
1.93
178
8,200
3.6
381
17,79811.6

76
22,6811.1
250
23,057
3.48
734
30,072
7.98
743
29,593
15.87

Cases
Rate
Cases
Rate
Cases
Rate
Cases
Rate
Cases
Rate
Cases
Rate
Cases
People
Rate

7
7.28
14
7.74
14
8.67
0
0.00
1
4.96
0
0.00
36
3,623
6.58

15
15.66
49
12.82
33
9.70
11
8.01
3
7.43
5
15.88
116
6,578
11.29
Women by dose, Gy

143
25.78
172
25.85
122
21.65
62
22.95
26
32.11
26
32.66
551
15,185
24.88

107
5.62
116
5.32
81
4.83
35
5.32
12
6.75
18
11.86
369
17,503
5.46

272
10.27
351
10.29
250
9.12
108
9.50
42
13.15
49
17.52
1,072
42,889
10.17

Cases
Rate
Cases
Rate
Cases
Rate
Cases
Rate
Cases
Rate
Cases
Rate
Cases
People
Rate

53
3.86
73
4.21
64
4.07
41
6.01
20
10.27
17
13.16
268
31,932
4.71

23
11.99
30
15.02
24
12.41
33
30.71
5
13.97
14
51.44
129
4,661
17.07

74
2.80
104
2.85
80
2.99
33
2.93
7
2.17
10
4.57
308
23,477
2.90

151
3.55
217
3.80
174
3.79
112
5.65
33
5.79
44
11.43
731
62,514
4.18

10–19

20–39

40z

NIC
,0.005
20.1
20.5
21
1z
Total

NIC
,0.005
20.1
20.5
21
1z
Total

a

1
2.19
10
7.49
6
4.21
5
7.56
1
5.87
3
31.58
26
2,444
6.28

Cases per 10,000 person years.

smoking effects (radiation-only) as in most LSS reports(3, 7).
Both the deviance and AIC values suggest that thegeneralized-interaction models fit better than the simple
interaction models and that the generalized multiplicative model described the data somewhat better than thegeneralized additive model. Models in which thegeneralized interaction was modeled in terms of packyears or years smoked were also considered, but they did
not describe the data any better than the smokingintensity models given in Table 2B.
Since most men smoke and most women do not,without allowance for smoking the estimated ERR/Gy
for men was similar to that for a simple multiplicative

77

RADIATION AND SMOKING EFFECTS ON LUNG CANCER INCIDENCE

TABLE 2
Parameter Estimates for Smoking Effects (Panel A) and Radiation Effects (Panel B) and Modifying Effects with 95%(likelihood-based) Confidence Intervals
A. Smoking effect
EffectSimple additive

Generalized multiplicative

Gender-average
Male
FemaleERR per 50 pack-years (born in 1915)
5.32 (4.10, 6.70)
3.48 (2.40, 5.00)
7.16 (5.20, 9.70)

4.69 (3.65, 5.94)
3.60 (2.60, 5.10)
5.77 (4.10, 7.90)

Female:Male ratio

2.15 (1.30, 3.40)

1.61 (1.00, 2.50)

Birth-cohort effect (percentage change per decade decrease in birth year)
Percentage per decade decrease in birth year
0.38 (18%, 61%)
0.33 (15%, 54%)
Log (duration/50)
Log (duration/50) squaredPower of years since quitting plus 1

Smoking-duration effect (log-linear)
20.30 (21.26, 0.60)
22.58 (25.30, 20.63)

20.24 (21.20, 0.69)
22.51 (25.20, 20.56)

Years since quitting effect20.50 (20.90, 20.29)

20.47 (20.77, 20.27)

B. Radiation effect
EffectRadiation only

Gender-averaged
Male
FemaleERR per Gy (age 70, age at exposure 30, never-smoker)
0.83 (0.55, 1.20)
0.98 (0.59, 1.50)
0.68 (0.43, 1.00)
0.65 (0.31, 1.10)
0.34 (0.15, 0.60)
0.69 (0.26, 1.30)
0.31 (0.13, 0.56)
0.30 (0.01, 0.56)
1.31 (0.83, 1.90)
1.27 (0.73, 2.00)
1.06 (0.64, 1.60)
1.00 (0.77, 2.30)

0.59 (0.31, 1.00)
0.29 (0.10, 0.62)
0.90 (0.47, 1.50)

Female:Male ratio

3.82 (2.00, 9.00)

Simple additive

Generalized additive Generalized multiplicative

3.44 (1.70, 9.00)

3.38 (1.40, 11.0)

3.13 (1.60, 7.00)

Attained age

Attained-age effect (power)
22.00 (24.0, 20.03) 22.70 (24.7, 20.6) 22.50 (24.5, 20.4)

23.05 (25.2, 20.9)

22.78 (24.9, 20.7)

Age at exposure

Age-at-exposure effect (percentage change per decade increase)
21% (26%, 55%)
31% (22%, 77%)26% (25%, 69%)
50% (9%, 111%)

30% (23%, 77%)——

Smoking-intensity effect————10.20 (4.6, 17.0)
215.30 (230.0, 5.4)

9.20 (3.8, 15.0)
216.6 (230.0, 26.6)

9815.5149843.5Model and fit summary information
9428.79425.126
269480.79477.19415.728
9471.79410.3
28
9466.3

Packs per day
Packs per day squared
Deviance
Parameters
AIC

1.85 (0.84, 5.10)

Simple multiplicativemodel while that for women similar to that for a simple
additive model. In all of the models except the simple
additive model, the ERR/Gy was significantly larger for
women than for men. The radiation-associated ERRs
declined with increasing attained age while rising withincreasing age at exposure (Fig. 3).
There was no indication of statistically significant
curvature in the radiation dose response (P . 0.5) overthe full dose range or when the data were restricted to
the 0–2-Gy range (P 5 0.3). Furthermore, the genderaveraged dose–response slope for the restricted range(0.67 per Gy) was similar to that for the full range (0.59).
There were no indications of gender dependence in effectmodification by attained age (P . 0.5) or age at
exposure (P . 0.5), nor was there evidence of
nonlinearity (on a log scale) for those effects (P 5 0.3
for attained age and P 5 0.5 for age at exposure).

Figure 4 illustrates how the ERR changes withsmoking intensity and dose under three interaction
models. The points in this figure are category-specificestimates from a generalized multiplicative model inwhich smoking-intensity categories replaced the linearquadratic function of log intensity in the radiation
model. The left panel describes the joint effect ofradiation and smoking relative to the rates for nonsmokers with no radiation exposure. The right panel
describes the radiation effect in terms of the ERR/Gy
relative to the risk for an unexposed individual with thesame smoking history. The generalized interaction
model suggests that at lower smoking intensities (#10
cigarettes per day) the radiation effect tends to begreater than predicted by either the simple additive or
multiplicative models but that there is little or noapparent radiation effect for heavy smokers ($2078

FURUKAWA ET AL.

FIG. 1. Smoking-related excess relative risk (ERR) as a function of duration and intensity. Panel A: ERR for a 50-pack-year smoker as afunction of smoking duration. The upper axis indicates the smoking intensity (packs per day) required to reach 50 pack-years for the duration
indicated on the lower axis. The fitted risk for a model that is linear in pack-years with a log-quadratic duration effect is indicated with the solidline. The fitted ERR for the pack-years-only model is indicated by the dashed line. The points are estimates of the risk in smoking-duration
categories. Panel B: Variation in the lung cancer ERR with smoking intensity (cigarettes per day) for fixed numbers of pack years.

cigarettes per day). There was no indication that the risk
pattern in the generalized interaction models depended
on gender (P . 0.5).
The upper portion of Table 3 summarizes the
distribution of observed and fitted cases over dose
categories for the generalized multiplicative model. The

fitted cases are broken down into background cases (i.e.,
those that would have occurred in unexposed nonsmokers), excess cases associated with radiation exposure,smoking or radiation-smoking interaction, and among
those with no smoking history data over predictions forunexposed nonsmokers in the same dose group. Almost

FIG. 2. Gender-specific smoking effects on the excess relative risk (panel A) and absolute rate (panel B) as afunction of age. The darker curves are for men and the lighter ones for women. The solid curves illustrate themodeled lung cancer risks for a person who smokes 20 cigarettes (one pack) per day from age 20. The longdashed lines indicate the risk for an individual who stopped smoking at age 50. The short-dashed lines in panel
B indicate the risk for nonsmokers. The curves correspond to risk for an unexposed person born in 1915.

RADIATION AND SMOKING EFFECTS ON LUNG CANCER INCIDENCE

79

FIG. 3. Effects of attained age (panel A) and age at exposure (panel B) on the excess relative risk (ERR) per
Gy. The plots compare the gender-averaged risk estimates for three joint effect models. The generalized
multiplicative model for nonsmokers is indicated by the dark solid line while the additive model is indicated by
the long-dashed line. For both of these models the ERR is relative to the risk for nonsmokers. The short-dashedline is for a model with no adjustment for smoking. In this model the ERR is relative to the risk for an
unexposed cohort member without regard to smoking status.

half (555 of 1126 cases) of the cases among subjects withsmoking history data and 40% (270 of 677) of those
without smoking data were estimated to be associatedwith smoking (details not shown). Only about 6% of all
the cases appeared to be associated with radiation
exposure in the cohort, but this proportion increased tomore than 40% among those with lung doses of 1 Gy or
more. In the interaction models, only 2–3% of the casesand one-third of the radiation-associated cases were

attributed to the joint effect of radiation and smoking.
Regardless of the type of interaction, the total estimatednumbers of radiation-associated cases were generally
similar (102–106), but the model with no smoking
adjustment provided the largest estimate of radiationrelated excess cases (126). Supplementary Tables 2 and 3
include summaries of the estimated numbers of cases
attributable to radiation and smoking stratified bysmoking intensity and pack-years.

FIG. 4. Variation of the excess relative risk (ERR) with smoking intensity. The gender-averaged risk estimates at age 70 after radiation
exposure at age 30. Smoking was assumed to start at age 20 so that smoking duration was fixed at 50 years in this figure. Panel A describes thejoint effect of radiation and smoking relative to the baseline rate for a nonexposed nonsmoker. The thin long-dashed line is the fitted ERR for a
person with no radiation exposure. The solid line is the fitted ERR after exposure to 1 Gy under the generalized multiplicative model, the thickdashed line is the fitted risk under a simple multiplicative model, and the short-dashed line is the fitted ERR under a simple additive joint effectmodel. The points are based on a generalized multiplicative model in which smoking intensity categories replaced the linear-quadratic function oflog intensity used in the generalized multiplicative model. Panel B presents radiation-associated excess risks for an exposure of 1 Gy relative tothe risk of an unexposed person with the same smoking history.

80

FURUKAWA ET AL.

TABLE 3Observed and Fitted Cases by Dose Category for the Generalized Multiplicative Model with Totals for Alternative Models
Excess cases
Total

Total
radiation
excessa

Total
smoking
excess

Dose (Gy)

Cases

Baseline(no smoking)0 (NIC),0.005
20.1
20.2
20.5
21
22
2z
Total

423
568
424
101
119
75
69
24
1803

215.3
309.0
234.5
51.7
52.1
29.7
15.8
5.2
913.2

0.0
0.2
5.5
5.7
13.1
15.9
15.3
9.7
65.4

Generalized multiplicative model
0.0
138.6
69.1
0.1
154.6
90.3
2.3
123.7
62.1
3.0
31.0
14.0
7.3
33.7
14.5
9.2
19.5
9.6
10.2
12.5
6.7
5.4
3.9
3.3
37.2
517.7
269.6

207.7
245.2
193.7
53.7
68.6
54.3
44.5
22.3
889.8

0.0
0.3
7.8
8.7
20.4
25.1
25.2
15.1
102.6

138.6
154.7
126.1
34.0
41.0
28.8
22.5
9.3
554.9

Radiation only
Additive
Multiplicative
Generalized additive

1677.3
901.7
932.9
901.5

125.7
104.7
75.0
75.9

Alternative model (totals only)
—
—
0.0
530.5
28.1
508.9
30.5
520.5

—
901.3
870.1
901.5

125.7
104.7
103.1
106.4

—
530.5
536.9
550.9

Radiation
only

Radiation andsmoking

Smoking
only

Unknownsmoking

—
266.1
258.2
274.7

a

For the multiplicative and generalized multiplicative models there are some cases that arise due to the interaction between unknown smoking
status and radiation effects, these cases (a total of about 13 for both models) are not separated from the other cases associated with unknown
smoking status, but are included in the total radiation excess.

DISCUSSION

Questions about the joint effect of radiation andsmoking are generally framed in terms of a choice
between simple additive and multiplicative models. The
analysis by Pierce et al. (6), which found a pattern forthe joint effect that was qualitatively similar to ours,
rejected a simple multiplicative model but not a simple
additive model. With additional follow-up data from
more cohort members and more parsimonious description of the departures from simple models, we were able
to reject both the simple additive and multiplicativemodels. Under our fitted generalized multiplicativemodel, the joint effect appears to be super-multiplicative
for light to moderate smokers (smoking less than a packof cigarettes per day) but additive or even sub-additivefor heavy smokers (smoking a pack or more per day).
How smoking modifies the radiation-related risk oflung cancer has both biological and practical implications. The practical interest arises in medical, occupational and environmental exposure scenarios: namely,
implications in risk/benefit analyses of lung cancer CT
screening programs targeted at smokers (18), concern
for the risk from the increasing use of CT scans andother radiological procedures (19, 20), interpretation of
the dose response found for lung cancer in a large
population of nuclear industry workers with low-doseradiation exposure (21), and estimation of the proportion of lung cancers attributed to residential radon
exposures (22). Since c rays account for most of the dose
received by atomic bomb survivors, the LSS data may bedirectly relevant in the first three cases. To the extent
that the biological nature of the radiation-smoking
interaction is similar for c rays and radon a particles,

information from the LSS may also be important in the
fourth case.
Studies of Hodgkin lymphoma patients treated withradiotherapy (23, 24) suggested a multiplicative interaction between radiation and smoking effects on lungcancer risks. The doses received by cancer patients wereorders of magnitude greater than those received byatomic bomb survivors and the ERR/Gy (0.15) was
considerably lower than the LSS risk estimates. The
National Research Council reviewed data from studies
of underground miners for the joint effect of smoking
and radon exposure (22, 25) and concluded that theinteraction could be most consistently described as lessthan multiplicative, although evidence from individual
studies varied considerably. The multiplicative effect ofsmoking and residential radon exposure was suggested
in a Swedish study (26) and a pooled analysis ofEuropean residential radon exposure studies (27, 28).
None of those studies investigated the kind of departuresfrom simple interaction models that we found in the LSS
data.
Adjustment for smoking can affect the modifying
effects of gender and age factors on the radiation-related
lung cancer risk. Our estimate of the ERR/Gy of 0.59
was smaller than that of 0.89 from the previous analysis
by Pierce et al. (6) and that of 0.81 from the recent
analysis of LSS lung cancer incidence with no adjustment for smoking (3). The present estimate of thefemale:male ratio of ERR/Gy of 3.1 was smaller thanthat of 4.8 from the unadjusted analysis (3) but larger
than smoking-adjusted estimate of 1.6 in ref. (6). For
many types of solid cancers, the ERR/Gy decreases withincreasing age at exposure (3, 7), but the unadjusted

RADIATION AND SMOKING EFFECTS ON LUNG CANCER INCIDENCE

ERR/Gy for lung cancer has been found to increase withincreasing age at exposure. Although it was suggested inref. (3) that this pattern might be a consequence of the
failure to adjust for the effect of smoking, the current
analyses indicate that this may not be the case. It may bethat there is a certain pool of people who are geneticallysusceptible to lung cancer and that high levels ofsmoking have saturated that pool so that there is little
room for an additional radiation effect. Anotherpossible explanation is that radiation exposure prior to
the start of smoking may be less harmful than radiation
exposure after smoking initiation. In the LSS, age at
exposure is highly correlated with whether radiation
exposure occurred before or after initiation of smoking,making it difficult to address this question. However, inan analysis in which the radiation effect was allowed todepend on whether one reported smoking before
exposure, we found that radiation risks were notsignificantly higher for those who smoked before
exposure and that the age-at-exposure effect became
even more pronounced.
We estimated that smoking related relative risks were
4.6 for males and 6.8 for females who smoked a pack a
day for 50 years. If the smoking duration and intensity
were averaged over the general population, those values
would be close to the risk estimates of 4.5 and 4.2 formale and female smokers, respectively, from anotherJapanese cohort study (29). These values are muchsmaller than those reported from Western populations(30, 31). This may in part reflect the higher lung cancer
rates among nonsmokers in Japan and other Asian
countries than in the West. A recent study (32) suggested
that lung cancer rates might be higher among Japanese
nonsmokers and relative risks lower among Japanesesmokers compared with their U.S. white counterparts.
Our estimates of lung cancer rates for nonsmokers weresimilar to those found for Japanese and Korean
populations in an international comparison of lungcancer risks among nonsmokers (33).
The major limitation of this study arises from the useof incomplete, historical smoking data derived frommail surveys. While all of the eligible cases in the cohort
were used to estimate radiation effects, the power todescribe the radiation-smoking interaction was reduced
because smoking status was unknown for about 60% of
the total follow-up time and smoking status at the timeof diagnosis was unknown for about 40% of the cases.
Use of singly imputed values for the age at whichsmoking started or intensity of smoking can lead tosome underestimation of the uncertainty in risk estimates. Estimates of the effect of smoking cessation canbe underestimated due to smoking recidivism, because
the most recent smoking data used were obtained in theearly 1990s. However, it is also likely that many people
who reported smoking at the time of their last survey
response have since quit smoking, which would tend to81

result in underestimation of the effect of smoking in later
years. While the present analysis considered all lungcancer types together, smoking and radiation may havedifferent effects on different subtypes of lung cancer; thiswill be reported in a forthcoming paper.
Despite these limitations, we believe that this study
provides the most comprehensive characterization of thejoint effects of low-dose radiation and smoking on lungcancer in any radiation-exposed population. The results
suggest that simple additive or multiplicative models
may not adequately describe the complex interaction
between smoking intensity and radiation and that asimilar comprehensive analytical approach may beneeded in risk estimation for smokers with medical or
occupational radiation exposures. We think that further
efforts should be made to develop methods for using
generalized interaction models in radiation risk assessment. This study is also one of the most detailed
quantitative analyses of smoking effects on lung cancer
rates in a Japanese population, and whether the present
findings are duplicated in other Japanese cohorts would
be of interest because they have an important publichealth implication for one of the major cancer problems
in Japan.
SUPPLEMENTARY INFORMATION

Additional information on the smoking history data(including Tables S1 and S2). DOI: 10.1667/RR2083.
1.S1
ACKNOWLEDGMENTS
The Radiation Effects Research Foundation is a private nonprofit
foundation funded by the Japanese Ministry of Health, Labour andWelfare and the U.S. Department of Energy, the latter in partthrough the National Academy of Sciences. This research waspartially supported by the U.S. National Cancer Institute intramuralresearch program and contract number N01-CP-31012-66.
Received: November 18, 2009; accepted: February 22, 2010; published
online: April 21, 2010

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