The International Agency for Research on

Cancer (1980, 1987) identifies inhalation of

inorganic arsenic as a cause of lung cancer in

humans. The evidence has come primarily

from epidemiologic studies of miners and

smelter workers who inhaled high levels of

inorganic arsenic in airborne dust. Although

increased risk of lung cancer from inhaled

arsenic appears related to direct effects on the

respiratory tract, there may be a systemic

effect as well, because ingestion of high

amounts of inorganic arsenic in drinking

water has been associated with lung cancer

mortality at levels comparable with occupational risks from inhaled arsenic (Bates et al.

1992; Lubin et al. 2000). Information on the

mathematical relationship between cumulative

inhaled arsenic exposure and respiratory cancer mortality is limited. Although a comparative evaluation of several occupational cohorts,

mostly smelter workers, suggested a concave

relationship between respiratory cancer mortality and cumulative arsenic exposure (HertzPicciotto and Smith 1993), a subsequent

update of one large study revealed that its

apparent nonlinearity was an artifact of exposure assessment and that relative risk (RR)

trends with cumulative arsenic exposure were

consistent with a linear relationship (Lubin

et al. 2000). Previous analyses have focused on

cumulative arsenic exposure and the shape of

the exposure–response relationship, without

considering the possible influence of arsenic

concentration.

In the present analysis, we applied a novel

methodology used previously in a study of

smoking-related lung cancer (Lubin and

Caporaso 2006) to model cumulative arsenic

exposure and arsenic concentration. In particular, we examined the consequences of exposure “delivery,” that is, whether the RR of

respiratory cancer at a fixed cumulative exposure delivered at higher arsenic concentration

for shorter duration was greater than, equal to,

or less than the RR from an equal cumulative

exposure delivered at lower concentration for

longer duration. We refer to these patterns as

direct (increasing), constant (no variation), or

inverse (decreasing) “exposure rate” effects. 

Standard analyses of joint RRs by duration

of arsenic exposure and arsenic concentration

with nonexposed individuals as the referent

group are problematic for characterizing the

effects of arsenic concentration, because cumulative exposure varies. For illustration, consider

duration of cigarette smoking, smoking intensity (cigarettes smoked per day), and total

pack-years (years of smoking times cigarettes

per day divided by 20). In a standard log-linear

RR regression model that includes duration of

smoking and cigarettes per day, the intensity

parameter represents the ln(RR) per cigarette

per day at a fixed duration. Interpretation of

the duration parameter is similar but at a fixed

intensity. A comparison of RRs at two different intensities reflects not only different intensities, but also different total pack-years. For

example, at 30 years’ duration, RRs at 20 and

30 cigarettes per day reflect different intensities

and different total pack-years—namely, 30

and 45 pack-years, respectively. Thus, RR patterns with duration and intensity cannot be

viewed as “independent” because of the intrinsic confounding of pack-years. In contrast, in a

model that includes pack-years and cigarettes

per day, the parameter for cigarettes per day

represents the comparison of risk for exposure

delivered at lower intensity for longer duration

with risk for an equal total exposure delivered

at higher intensity for shorter duration (Lubin

and Caporaso 2006). We use this framework

to analyze inhalation of airborne inorganic

arsenic in a cohort mortality study of smelter

workers conducted in Montana (Lee and

Fraumeni 1969; Lee-Feldstein 1983; Lubin

et al. 1981, 2000).

Our goal of modeling the cumulative airborne arsenic exposure and arsenic concentration is to estimate a descriptive function that

defines the modifying effect of concentration

on the association between cumulative exposure and respiratory cancer. This effect modification is conceptually similar to the “dose

rate effective factor” used in radiation epidemiology that describes for a specific type of

radiation the effects on disease risk of total

radiation dose delivered at lower (protracted

or highly fractionated) dose rates compared

with the effects at higher (or acute) dose rates

(National Research Council 2006).

Materials and Methods

Data. The cohort study enrolled workers

employed at a Montana copper smelter for ≥ 1

year before 1957, with follow-up starting 1 year

after initial employment or 1 January 1938,

Environmental Health Perspectives • VOLUME 116 | NUMBER 12 | December 2008 1661

Research

Address correspondence to J.H. Lubin, Biostatistics

Branch, Division of Cancer Epidemiology and

Genetics, National Cancer Institute, 6120 Executive

Blvd., Room 8042, Rockville, MD 20852 USA.

Telephone: (301) 496-3357. Fax: (301) 402-0081.

E-mail: lubinj@mail.nih.gov

This research was supported by the Intramural

Research Program of the National Cancer Institute,

National Institutes of Health.

The authors declare they have no competing

financial interests.

Received 27 March 2008; accepted 23 July 2008.

Respiratory Cancer and Inhaled Inorganic Arsenic in Copper Smelters

Workers: A Linear Relationship with Cumulative Exposure that Increases

with Concentration

Jay H. Lubin,1 Lee E. Moore,2 Joseph F. Fraumeni Jr.,3 and Kenneth P. Cantor2

1Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA; 2Occupational

and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, Rockville, Maryland, USA; 3Office of the

Director, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA

BACKGROUND: Inhalation of high levels of airborne inorganic arsenic is a recognized cause of

respiratory cancer. Although multiple epidemiologic studies have demonstrated this association,

there have been few analyses of the mathematical relationship between cumulative arsenic exposure and risk of respiratory cancer, and no assessment as to whether and how arsenic concentration may modify this association.

OBJECTIVES: The objective is an evaluation of the shape of the relationship between respiratory

cancer mortality and cumulative inhaled arsenic exposure among copper smelter workers, and the

modification of that relationship by arsenic concentration.

METHODS: We used Poisson regression methods to analyze data from a cohort of arsenic-exposed

copper smelter workers under a linear-exponential model for the excess relative risk.

RESULTS: Within categories of arsenic concentration, the association between respiratory cancer

and cumulative arsenic exposure was consistent with linearity. The slope of the linear relationship

with cumulative exposure increased with increasing arsenic concentration category. 

CONCLUSIONS: Our results suggested a direct concentration effect from inhaled inorganic arsenic,

whereby the excess relative risk for a fixed cumulative exposure was greater when delivered at a

higher concentration and shorter duration than when delivered at a lower concentration and

longer duration.

KEY WORDS: arsenic, dose–response relationship, lung neoplasms, occupational diseases. Environ

Health Perspect 116:1661–1665 (2008). doi:10.1289/ehp.11515 available via http://dx.doi.org/

[Online 23 July 2008]
whichever was later (Lee and Fraumeni 1969).

The present analysis included 8,014 workers

followed through 1989 (Lubin et al. 2000). We

determined vital status from the National

Death Index (http://www.cdc.gov/nchs/

ndi.htm) and other sources, with causes of

death recorded from death certificates. A total

of 4,930 (62%) workers were deceased.

Following previous analyses of this cohort, we

consider respiratory cancer mortality as the

primary outcome.

In the 1960s, investigators ranked each

work site from 1 to 10 for its potential arsenic

exposure and classified each site as “heavy”

(ranks 8–10), “medium” (ranks 4–7), or

“light” (ranks 1–3) arsenic exposure areas (Lee

and Fraumeni 1969). We classified unspecified or unknown work areas as light exposure

areas. We subsequently updated employment

information through September 1977 (LeeFeldstein 1986). Using 702 measurements of

airborne arsenic from 1943 through 1958 and

estimates of workers’ exposure times, investigators computed time-weighted average

arsenic concentrations for light, medium, and

heavy arsenic-exposed areas as 0.29, 0.58, and

11.3 mg/m3, respectively (Morris 1978). For

each year of follow-up, we computed a cumulative exposure index in mg/m3-year, denoted

d, as the product of years worked in light (L),

medium (M), and heavy (H) exposure areas

and the corresponding concentration—that is,

d = 0.29 × L + 0.58 × M + 1.13 × H—where

we down-weighted heavy exposure areas by a

factor of 0.1 to reflect use of air filtration

masks. This a priori choice of weight was

similar to an empirically derived, risk-based

estimate of weight (Lubin et al. 1981).

Many workers had recorded ages at last

employment of < 40 and 50 years. Because

there was no information on exposures after the

workers left the smelter, we repeated analyses

using person-years and respiratory cancer deaths

in current workers, recent former (< 5 years)

workers, and workers with last employment at

≥ 50 years of age to minimize effects of unmeasured exposures. The restricted subset included

261 respiratory cancer cases and 144,851 person-years. The number of respiratory cancer

cases differed from a previous report (n = 252)

(Lubin et al. 2000) because of a modified

definition of the restrictions.

Statistical analysis. We assumed survival

time follows a piecewise exponential distribution and applied Poisson regression methods.

We summarized deaths from respiratory cancer

and person-years of follow-up in a multiway

contingency table defined by attained age (nine

levels: < 45, 45–49, . . . ,75–79, ≥ 80), calendar

year (10 levels: 1935–39, . . . ,1985–90), cumulative arsenic exposure (mg/m3-year; 18 levels:

< 0.25, 0.25–0.49, 0.50–0.74, 0.75–0.99,

1.0–1.4, 1.5–1.9, 2.0–3.9, . . . , 22–24.9,

≥ 25), mean arsenic concentration (mg/m3;

eight levels: 0, 0.01–0.29, 0.3–0.39, 0.4–0.49,

0.5–0.59, 0.6–0.79, 0.8–0.99, ≥ 1.0), years

since last arsenic exposure (three levels: < 5,

5–14, ≥ 15), and place of birth (two levels: U.S.

or foreign born). For each cell of the cross-classification, we computed person-year–weighted

means for the cross-classification variables.

Because all workers were exposed to arsenic, we

used age-specific and calendar-year-specific U.S.

mortality rates for respiratory cancer in white

males as a nonexposed referent population

(Breslow and Day 1987).

We defined indicator variables ci

, i = 1, . .

. , I, for I concentration categories where ci =

1 for intensities within category i and zero

otherwise, and modeled disease rate r(x,d,c) as

r(x,d,c) = exp(αT x) RR(d,c) 

= exp(αT x)(1 + Σ γi ci d), [1]

where d is cumulative arsenic exposure and c

is arsenic concentration. The vectors of variables x and parameters α, where T denotes

vector transpose, describe disease rates in nonexposed workers. Within concentration category i, RRs are linear in d; that is, RR(d,c) = 1

+ γi d, where γi is the slope of the linear exposure–response, or ERR/mg/m3-year (ERR,

excess relative risk). We used Wald-type confidence intervals (CIs) for γi after reparameterizing γi as exp(γi

*) to avoid range restrictions.

Factoring out γ1, we rewrote RR(d,c) as

RR(d,c) = 1 + γ1 d Σ (γi/γ1) ci,

which suggested the following model for continuous concentration:

RR(d,c) = 1 + β d g(c), [2]

where β defined ERR/mg/m3-year at g(c) = 1.

The g(.) function represents the modifying

effects of arsenic concentration on the exposure–response relationship. Preliminary analysis suggested using the power form g(c) =

exp{φ ln(c)}, where φ > 0, φ = 0, or φ < 0

denotes a direct (increasing), constant (no

variation), or inverse (decreasing) concentration effect, respectively. Model fit did not

improve with the addition of c or ln(c)

2.

We extended model 1 to

RR(d,c) = 1 + Σ γi ci d exp(δi d) [3]

Lubin et al.

1662 VOLUME 116 | NUMBER 12 | December 2008 • Environmental Health Perspectives

Table 1. Descriptive statistics for the Montana cohort study of copper smelter workers.

Characteristic All data Restricted dataa

No. of workers 8,014 7,887b

Person-years 256,850 144,851

Follow-up years 1938–1989 —

Mean cumulative arsenic exposurec (mg/m3

-years) 3.7 5.4

Mean airborne arsenic concentrationc (mg/m3) 0.35 0.36

Mean durationc (years) 10.3 14.9

Respiratory cancer cases

No. 446 261

Mean cumulative arsenic exposure (mg/m3

-years) 7.0 10.4

Mean airborne arsenic concentration (mg/m3

) 0.41 0.40

Mean duration (years) 17.4 26.0

Crude respiratory cancer mortality rate × 1,000 1.74 1.80

aPerson-years in current and recent former (< 5 years) workers and workers with last employment at ≥ 55 years of age.

bNumber of workers contributing person time. cPerson-years weighted mean.

Table 2. Summary data, respiratory cancer mortality rates, unadjusted and adjusted SMRs relative to U.S. white males by cumulative arsenic exposure (mg/m3-years)

and arsenic concentration (mg/m3) for the Montana cohort study of copper smelter workers.

Cumulative arsenic exposure (mg/m3-years) Mean arsenic concentration (mg/m3)

< 0.75 0.75–1.99 2.0–4.9 5.0–9.9 10.0–14.9 ≥ 15.0 0.29 0.30–0.39 0.40–0.49 ≥ 0.50

Cases 62 96 74 83 84 47 239 74 29 104

Person-years 71424.0 66757.4 55332.2 39257.0 16804.7 7274.7 167583.2 39757.0 14853.6 34656.4

Mean 0.47 1.24 3.43 7.27 11.9 21.9 0.29 0.34 0.45 0.65

Crude rate × 1,000 0.87 1.44 1.34 2.11 5.00 6.46 1.43 1.86 1.95 3.00

SMR 0.97 1.50 1.35 1.49 2.40 3.62 1.30 1.61 1.81 2.59

95% CI 0.8–1.3 1.2–1.8 1.1–1.7 1.2–1.8 1.9–3.0 2.7–4.8 1.1–1.5 1.3–2.0 1.3–2.6 2.1–3.1

SMRa 0.84 1.28 1.08 1.11 1.68 2.35 1.02 1.32 1.47 2.00

90% CI 0.6–1.1 1.0–1.6 0.9–1.4 0.9–1.4 1.4–2.1 1.8–3.1 0.9–1.2 1.1–1.7 1.0–2.1 1.7–2.4

aSMRs adjusted for differences by calendar period and country of birth between rates of respiratory cancer in U.S. males and estimated respiratory cancer rates in a hypothetical group

of nonexposed workers.
to evaluate departure from linearity, where δi

reflects concavity (δi < 0) or convexity (δi > 0)

of the ERRs with cumulative exposure for the

ith concentration category. The null hypothesis δi = 0 specified no departure from linearity.

We applied a similar extension to model 2.

Because all workers were exposed, we

included the natural logarithm of the age- and

year-specific respiratory cancer rates of U.S.

males as a fixed offset variable (x) to represent

rates in nonexposed workers. The model thus

represented a regression equation for standardized mortality ratios (SMR) (Breslow and

Day 1987). Although model 2 reflected

relative SMRs, we refer to quantities as RRs.

For analyses, we used the Epicure suite of

programs (Preston et al. 2004).

Results

The person-year–weighted mean cumulative

arsenic exposure was 3.7 mg/m3-years, with a

crude respiratory cancer mortality rate of 1.73

per 1,000 person-years, based on 446 total

respiratory cancer deaths (Table 1). The SMR

for respiratory cancer was 1.56 (95% CI,

1.4–1.7). In the restricted data, the mean

cumulative arsenic exposure was 5.4 mg/m3-

years, the crude respiratory mortality rate per

1,000 person-years was 1.80, and the SMR

was 1.87 (95% CI, 1.7–2.1). 

SMRs increased with cumulative arsenic

exposure and with arsenic concentration

(Table 2). The SMR in the lowest concentration category was significantly elevated, suggesting a difference between respiratory cancer

mortality rates for U.S. males and a hypothetical group of nonexposed workers. We fitted

model 1 with continuous cumulative arsenic

exposure and six categories of arsenic concentration and estimated respiratory cancer rates

in a hypothetical group of nonexposed workers

relative to the standard population. After

adjustment for arsenic exposure, SMRs for

nonexposed workers were 3.10 (95% CI,

2.3–4.2), 2.22 (95% CI, 1.6–3.1), and 1.44

(95% CI, 1.1–2.0) for calendar periods

< 1960, 1960–69, and ≥ 1970, respectively,

and the effect of U.S. birth relative to foreign

birth was 0.67 (95% CI, 0.5–0.9). SMRs did

not vary significantly with attained age. We

included offset parameters fixed at the logarithms of these “nonexposed” SMRs to adjust

for differences between “nonexposed” workers

and the standard population. After adjustment,

SMRs increased with cumulative arsenic exposure and with arsenic concentration, but at a

lesser rate of increase (Table 2).

Within each arsenic concentration category,

RRs increased with cumulative arsenic exposure

(Figure 1). Two characteristics in the figure are

noteworthy. First, RRs by cumulative arsenic

exposure were consistent with linearity within

each concentration category. Tests of the null

hypothesis of no departure from linearity were

not rejected. Second, estimates of the slope

parameters γi (i.e., the ERR/mg/m3-year) varied

significantly (p-value for homogeneity of slopes,

p = 0.02) and generally increased, suggesting a

greater exposure–response relationship with

increasing arsenic concentration.

The change in the ERR/mg/m3-year estimate (i.e., the slope) represents the effect

modification by arsenic concentration of the

relationship between respiratory cancer and

cumulative arsenic exposure. Figure 2 plots

the slope estimate by mean arsenic concentration for six concentration categories (square

symbols, with CIs omitted for clarity), the fitted model 2 using continuous data (solid line),

its pointwise 95% CI (dashed lines), and the

model for no variation with concentration

(model 2 with φ = 0; dotted line). The ERR/

mg/m3-year estimate (β) was β = 0.115 (95%

CI 0.07–0.19) (Table 3), and varied significantly with arsenic concentration (p < 0.01).

After adjusting for cumulative arsenic exposure, the ERR/mg/m3-year increased with

increasing concentration (estimate of φ was

1.123 (95% CI, 0.41–1.84). Results in the

restricted data were similar.

Using model 2, we evaluated time since last

arsenic exposure as a modifier of the arsenic

exposure–response relationship. We focus on

the restricted data, although we show results for

all data for completeness. Although variations

were not statistically significant, the cumulative

arsenic exposure effect (β) decreased with time

since last arsenic exposure by factors of 1.0, 0.8,

and 0.2 for < 5, 5–14, and ≥ 15 years since last

exposure, respectively (Table 3; change in

deviance = 2.6 and p = 0.27 comparing models

B0-R and T1-R; or change in deviance = 1.2

and p = 0.55 comparing models T2-R and

T3-R). The arsenic concentration effect (φ) also

varied with time since last exposure, but

changes in deviance were smaller (Table 3;

change in deviance = 1.9 and p = 0.39 comparing models B0-R and T2-R, or change in

deviance = 0.5 and p = 0.78 comparing T1-R

and T3-R).

In the restricted data, there was a suggestion of declining arsenic effects with attained

age, but no preference for effect modification

of age with either cumulative arsenic exposure

or arsenic concentration.

Discussion

In our study of copper smelter workers, RRs

for respiratory cancer increased linearly with

cumulative arsenic exposure within categories

of arsenic concentration. In addition, we found

a direct concentration effect, whereby the slope

of the linear exposure–response relationship

increased with arsenic concentration. This pattern implied that for equal cumulative arsenic

exposure, the RR of respiratory cancer mortality was greater for cumulative arsenic exposure

delivered at higher concentration for shorter

duration compared with cumulative exposure

delivered at lower concentration for longer

Inhaled arsenic and respiratory cancer mortality

Environmental Health Perspectives • VOLUME 116 | NUMBER 12 | December 2008 1663

Figure 1. RRs of respiratory cancer mortality by categories of cumulative arsenic exposure (mg/m3-years)

and arsenic concentration (mg/m3

) relative to U.S. mortality rates for white males, adjusted to nonexposed

workers, and fitted linear ERR models for cumulative arsenic exposure: 0.29 mg/m3 (A), 0.30–0.39 mg/m3

(B), 0.40–0.49 mg/m3 (C), and ≥ 0.50 mg/m3 (D). Estimates of the ERR per mg/m3-year and 95% CIs for the

four concentration categories were as follows: A, 0.016 (–0.005 to 0.041); B, 0.067 (0.024 to 0.119); C, 0.077

(0.017 to 0.159); D, 0.072 (0.043 to 0.107).

Total As exposure (mg/m3-years) Total As exposure (mg/m3-years)

Total As exposure (mg/m3-years) Total As exposure (mg/m3-years)

5

4

3

2

1

0

0 5 10 15 20 25 30

5

4

3

2

1

0

0 5 10 15 20 25 30

0 5 10 15 20 25 30

5

4

3

2

1

0

0 5 10 15 20 25 30

5

4

3

2

1

0

RR RRRR RR

A B

C D

As: 0.29 mg/m3 As: 0.30–0.39 mg/m3

As: ≥ 0.50 mg/m3 As: 0.40–0.49 mg/m3
duration. The increase in the exposure–

response relationship within each arsenic concentration category occurred for the full range

of concentrations experienced by smelter workers, suggesting that the direct concentration

effect was not rate limited within this range.

One explanation for the impact of arsenic

concentration on the association between

cumulative arsenic exposure and respiratory

cancer may be found in the concentrationdependent ability of cells to methylate and

detoxify arsenic. Although human populations differ, most humans exposed to arsenic

excrete 10–30% inorganic arsenic, 10–20%

monomethylarsonic acid [MMA(V) and

MMA(III)], and 60–80% dimethylarsinic

acid [DMA(V) and DMA(III)]. The pentavalent metabolites MMA(V) and DMA(V)

are generally considered less toxic than inorganic arsenite or arsenate. Trivalent methylated arsenic metabolites are more genotoxic

than inorganic arsenite both in vitro and

in vivo (Styblo et al. 2002). Although

MMA(III) and DMA(III) can be measured in

urine, epidemiologic studies to date have not

measured trivalent arsenicals because of the

preservation techniques required (Aposhian

et al. 2000; Le et al. 2000; Mandal et al. 2001;

Valenzuela et al. 2005). Epidemiologic studies

have used the percent MMA of total inorganic

arsenic and the MMA:DMA ratio as surrogate

markers of methylation efficiency of ingested

arsenic, with increased levels of excreted percent MMA and MMA:DMA ratio in urine

indicating reduced methylation efficiency.

Studies of arsenic in drinking water conducted

in Taiwan, Argentina, Bangladesh, and certain

areas of the United States have reported elevated risks of bladder and skin cancers associated with an increased percent MMA in urine,

as well as a greater MMA:DMA ratio (Ahsan

et al. 2007; Chen et al. 2003a, 2003b; Hsueh

et al. 1997; Pu et al. 2007; Steinmaus et al.

2006; Yu et al. 2000). These findings are supported by toxicologic studies showing that

methylated arsenicals, particularly MMA(III),

have a greater affinity than the nonmethylated

forms for protein binding to monothiol,

dithiol, and trithiol sites, which can disrupt a

number of processes, including gene transcription, glutathione synthesis, DNA methylation,

DNA repair, cell division, generation of reactive

oxygen species, and signal transduction pathways within cells (Cullen and Reimer 1989;

Kitchin and Wallace 2008; Kumagai and Sumi

2007; Lin et al. 1999, 2001; Mass et al. 2001;

Petrick et al. 2001; Styblo et al. 2000; Styblo

and Thomas 1997). In addition, studies of urinary arsenic methylation patterns of healthy

individuals exposed to arsenic in drinking water

have shown increased percent MMA relative to

total urinary arsenic (Hopenhayn-Rich et al.

1996b), whereas the percentage of inorganic

arsenic and the MMA:DMA ratio decreased

when the arsenic concentration in drinking

water was reduced to lower levels (HopenhaynRich et al. 1996a). These findings suggest that

the mechanisms through which arsenic is

methylated and excreted can become ratelimited at higher concentrations, perhaps

through several steps including enzymecatalyzed methylation of arsenic through

S-adenosyl methionine, or glutathione synthesis

(Waters et al. 2004).

The toxicologic effects of arsenic described

above are multifaceted. However, toxicologic

studies conducted to date support only the

notion that concentration of exposure has a

direct impact on the degree of toxicity and/or

DNA damage acquired. Total cumulative

arsenic exposure is a function of both concentration and duration of arsenic exposure. Our

findings suggest that rates of arsenic biotransformation and excretion are concentration

dependent. Toxicologic studies designed to

compare cellular and genetic toxicity at a given

total cumulative dose delivered at different dose

rates are needed to enhance understanding of

the mechanistic implications of our results.

Consistent with our findings, two epidemiologic studies have suggested that the toxic

effects from cumulative arsenic exposure in

drinking water are greater among individuals

exposed at higher, as opposed to lower, concentrations. In a case–control study of arsenicrelated skin lesions in a screened population in

Bangladesh, about 70% of subjects consumed

drinking water with arsenic concentrations

> 50 μg/L (Rahman et al. 2006). For similar

levels of cumulative arsenic exposure, the odds

ratios were higher for subjects with older ages

at first exposure compared with those younger

at first exposure. Because older age at first

exposure would reflect shorter duration of

exposure, the results suggested higher risks for

cumulative exposures with shorter durations

and correspondingly higher concentrations, as

we observed for lung cancer among smelter

workers. In a case–control study of bladder

cancer in New Hampshire, odds ratios among

smokers increased with higher concentrations

of toenail arsenic, with risks being greater

among individuals with shorter (< 15 years)

compared with longer residence in the current

home (Karagas et al. 2004).

Lubin et al.

1664 VOLUME 116 | NUMBER 12 | December 2008 • Environmental Health Perspectives

Table 3. Results of modeling the RRa of respiratory cancer by cumulative arsenic exposure and arsenic

concentration and variations by time since last exposure (TSLE) and attained age: Montana cohort study

of copper smelter workers.

TSLE (θ TSLE):

Model β < 5, 5–14, ≥ 15 φ φ TSLE Devianceb

B0c 0.115 1.123 —

T1 0.120 1.00, 0.83, 1.20 1.153 0.5

T2 0.115 0.923, 1.278, 2.077 1.3

T3 0.095 1.00, 1.02, 2.52 0.723, 1.095, 2.661 3.7

B0-Rc 0.083 0.822 —

T1-R 0.102 1.00, 0.75, 0.18 0.848 2.6

T2-R 0.085 0.632, 1.111, 3.486 1.9

T3-R 0.095 1.00, 0.99, 0.11 0.739, 1.240, 17.53 3.1

Age (θ Age):

Model β < 60, 60–69, ≥ 70 φ φAge Devianceb

B0c 0.115 1.123 —

A1 0.153 1.00, 0.88, 0.52 1.175 2.2

A2 0.115 1.285, 1.012, 1.187 0.2

A3 0.200 1.00, 0.67, 0.20 1.830, 1.153, 0.077 4.9

B0-Rc 0.083 0.822 —

A1-R 0.088 1.00, 0.88, 0.66 0.878 0.7

A2-R 0.082 1.118, 0.813, 0.678 0.3

A3-R 0.156 1.00, 0.64, 0.20 1.724, 1.001, –0.281 4.0

aRR model: RR = 1 + β d θf exp{φf ln(c)}, where d is cumulative arsenic exposure in mg/m3

-years, and c is airborne arsenic

concentration in mg/m3. Subscript f denotes categories of TSLE or attained age and represents multiple parameters

either multiplying the ERR per mg/m3

-year (β) or distinct categories for the effects of arsenic concentration. bChange in

deviance from the base model, B0. Difference in deviances provides a likelihood ratio test of the added parameters.

cModels fit to all cohort data or to restricted data, denoted by “R,” including person-time of workers with time since last

exposure < 5 years or age last exposure ≥ 50. Estimates and 95% CIs: B0 parameters (β and φ), 0.115 (0.07–0.19) and 1.123

(0.41– 1.84); B0-R parameters, 0.083 (0.04–0.15) and 0.822 (0.01–0.63).

Figure 2. Estimates of ERR per mg/m3-year based on

a linear RR model within six categories of arsenic

concentration (square symbols), fitted model 2 (solid

line), its pointwise, two-sided, Wald 95% CI (dashed

lines), and model 2 omitting variation with concentration (dotted line; ERR/mg/m3

/year = 0.04756).

0.3

0.2

0.1

0.0

0.0

ERR per mg/m3-year

Arsenic concentration (mg/m3)

0.2 0.4 0.6 0.8 1.0 1.2
A threshold for the carcinogenic effects of

arsenic in drinking water at about 150 μg/L,

below which there is no risk from exposure,

has been suggested (Lamm et al. 2006).

Although studies of noncancer outcomes,

such as arsenical dermatoses, cardiovascular

disease, and cerebrovascular disease, do not

support a threshold (Ahsan et al. 2006; Engel

et al. 1994; Rahman et al. 2006; Yu et al.

2006), studies of cancer outcomes after relatively low levels of exposure have been limited

by low power and potential exposure misclassification (Cantor and Lubin 2007).

Our results may inform the exposure

threshold issue. In areas of Taiwan with

endemic blackfoot disease, Bates et al. (1992)

estimated that consuming drinking water with

470 μg/L arsenic resulted in a lifetime intake of

30,000 mg arsenic. Ingestion of drinking water

with 150 μg/L arsenic therefore corresponds

approximately to a lifetime intake of 9,600 mg

arsenic. Measurement of urinary arsenic and its

metabolites serves as a measure of absorption of

inhaled arsenic through the respiratory tract,

because arsenic excretion occurs mainly through

the kidneys (World Health Organization 2001).

In a study of smelter workers in Tacoma,

Washington, investigators empirically derived

the following relationship between inhaled and

urinary arsenic levels: airborne arsenic in micrograms per cubic meter = 0.0064 (urine arsenic

in micrograms per liter)1.924 (Enterline et al.

1987). In our data, mean airborne arsenic concentrations for the four categories in Figure 1

ranged from 0.29 to 0.65 mg/m3, and therefore

correspond roughly to urinary arsenic levels

from 263 to 400 μg/L. Assuming 2 L urine per

day, 250 working days per year, no other

sources of arsenic and no accumulation of

arsenic in the body (Bates et al. 1992), the

mean durations of exposure, which ranged

from 9.7 to 12.1 years for the four categories,

correspond to mean total arsenic intakes of

1,315 (= 263 × 2 × 250 × 10 year/1,000),

1,420, 1,649, and 1,996 mg arsenic, respectively. Thus, cumulative arsenic exposure in the

smelter workers corresponded to < 20% of the

lifetime arsenic intake at the postulated threshold. Our finding of a linear relationship for the

RR of respiratory cancer mortality across the

full range of cumulative arsenic exposures

within each category of arsenic concentration

argues against a threshold at 150 μg/L.

In summary, our cohort study of copper

smelter workers revealed a linear relationship

between cumulative arsenic exposure and respiratory cancer mortality within categories of

arsenic concentration. In addition, we found a

direct concentration effect on the exposure–

response relationship, indicating that for a fixed

level of cumulative arsenic exposure, inhalation

of higher concentrations of arsenic over shorter

durations was more deleterious than inhalation

of lower concentrations over longer durations.

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