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 cancermortality at levels comparable with occupational risks from inhaled arsenic (Bates et al. 1992; Lubin et al. 2000). Information on the mathematical relationship between cumulativeinhaled arsenic exposure and respiratory cancer mortality is limited. Although a comparative evaluation of several occupational cohorts, mostly smelter workers, suggested a concaverelationship between respiratory cancer mortality and cumulative arsenic exposure (HertzPicciotto and Smith 1993), a subsequent update of one large study revealed that itsapparent 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 arsenicexposure and arsenic concentration. In particular, we examined the consequences of exposure “delivery,” that is, whether the RR ofrespiratory 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 theeffects of arsenic concentration, because cumulative exposure varies. For illustration, considerduration 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, 30and 45 pack-years, respectively. Thus, RR patterns with duration and intensity cannot beviewed 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 concentrationon the association between cumulative exposure and respiratory cancer. This effect modification is conceptually similar to the “doserate 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

**1 Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA;**  **2 Occupational**  **and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, Rockville, Maryland, USA; 3**  **Office 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.

O BJECTIVES : 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.

R

ESULTS : 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.

C ONCLUSIONS : 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.

K

EY 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 (Leeand Fraumeni 1969). We classified unspecified or unknown work areas as light exposure areas. We subsequently updated employmentinformation through September 1977 (LeeFeldstein 1986). Using 702 measurements of airborne arsenic from 1943 through 1958 andestimates 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/m 3 , respectively (Morris 1978). Foreach 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 included261 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 survivaltime 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), calendaryear (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/m 3 ; 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 c i , i = 1, . .

. , I , for I concentration categories where c i = 1 for intensities within category i and zero otherwise, and modeled disease rate r (x , d

, c

) as

r ( x

, d

, c

)

= exp( α T x ) RR( d

, c

)

= exp( α T x )(1 + Σ γ i c i d

), [1]

where d is cumulative arsenic exposure and cis arsenic concentration. The vectors of variables x and parameters α , where T denotesvector transpose, describe disease rates in nonexposed workers. Within concentration category i , RRs are linear in d ; that is, RR( d

, c

) = 1

+ γ i d , where γ i is the slope of the linear exposure–response, or ERR/mg/m 3 -year (ERR,excess relative risk). We used Wald-type confidence intervals (CIs) for γ i after reparameterizing γ i as exp(γ i *) to avoid range restrictions. Factoring out γ 1 , we rewrote RR( d

, c

) as

RR( d

, c

)

= 1 + γ 1 d Σ (γ i /γ 1 ) c i,

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 modifyingeffects 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 (novariation), 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 γ i c i d exp(δ i 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 data a

No. of workers 8,014 7,887 b Person-years 256,850 144,851 Follow-up years 1938–1989 — Mean cumulative arsenic exposure c (mg/m 3 -years) 3.7 5.4
Mean airborne arsenic concentration c (mg/m 3 ) 0.35 0.36
Mean duration c (years) 10.3 14.9 Respiratory cancer cases No. 446 261 Mean cumulative arsenic exposure (mg/m 3 -years) 7.0 10.4 Mean airborne arsenic concentration (mg/m 3 ) 0.41 0.40 Mean duration (years) 17.4 26.0 Crude respiratory cancer mortality rate × 1,000 1.74 1.80

a Person-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. c Person-years weighted mean.

Table 2. Summary data, respiratory cancer mortality rates, unadjusted and adjusted SMRs relative to U.S. white males by cumulative arsen ic exposure (mg/m 3 -years) and arsenic concentration (mg/m 3 ) for the Montana cohort study of copper smelter workers.

Cumulative arsenic exposure (mg/m 3 -years) Mean arsenic concentration (mg/m 3 )

< 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 SMR a 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 est imated respiratory cancer rates in a hypothetical group of nonexposed workers.to evaluate departure from linearity, where δ i reflects concavity (δ i < 0) or convexity (δ i > 0) of the ERRs with cumulative exposure for the

ith concentration category. The null hypothesis δ i = 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 thusrepresented 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/m 3

- 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 cancermortality rates for U.S. males and a hypothetical group of nonexposed workers. We fitted model 1 with continuous cumulative arsenicexposure 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. Weincluded 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 γ i (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/m 3 -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 plotsthe slope estimate by mean arsenic concentration for six concentration categories (squaresymbols, 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/m 3 -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 relationshipincreased with arsenic concentration. This pattern implied that for equal cumulative arsenicexposure, 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/m 3 -years) and arsenic concentration (mg/m 3 ) 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/m 3 ( A ), 0.30–0.39 mg/m 3 ( B), 0.40–0.49 mg/m 3 ( C ), and ≥ 0.50 mg/m 3 ( D). Estimates of the ERR per mg/m 3 -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/m 3 -years) Total As exposure (mg/m 3 -years)

Total As exposure (mg/m 3 -years) Total As exposure (mg/m 3 -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

0RRRRRRRR A B

C D As: 0.29 mg/m 3 As: 0.30–0.39 mg/m 3

As: ≥0.50 mg/m 3 As: 0.40–0.49 mg/m 3duration. The increase in the exposure–response relationship within each arsenic concentration category occurred for the full rangeof 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 respiratorycancer may be found in the concentrationdependent ability of cells to methylate anddetoxify 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% dimethylarsinicacid [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 ingestedarsenic, 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 certainareas 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 anumber of processes, including gene transcription, glutathione synthesis, DNA methylation, DNA repair, cell division, generation of reactiveoxygen 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; Stybloand 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 drinkingwater was reduced to lower levels (HopenhaynRich et al. 1996a). These findings suggest that the mechanisms through which arsenic ismethylated and excreted can become ratelimited at higher concentrations, perhapsthrough 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. Ourfindings 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 individualsexposed 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 RR a 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 Deviance b

B0 c 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-R c 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 Deviance b

B0 c 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-R c 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 θ f exp{φ f ln( c)}, where d is cumulative arsenic exposure in mg/m 3 -years, and c is airborne arsenic concentration in mg/m 3 . Subscript f denotes categories of TSLE or attained age and represents multiple parameters either multiplying the ERR per mg/m 3 -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/m 3 -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 (dashedlines), and model 2 omitting variation with concentration (dotted line; ERR/mg/m 3 /year = 0.04756). 0.3

0.2

0.1

0.0

0.0ERR per mg/m3-year

Arsenic concentration (mg/m 3 ) 0.2 0.4 0.6 0.8 1.0 1.2A 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 limitedby 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 andurinary 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 thelifetime 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 relationshipbetween 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. R EFERENCES

Ahsan H, Chen Y, Kibriya MG, Slavkovich V, Parvez F, Jasmine F, et al. 2007. Arsenic metabolism, genetic susceptibility, and risk of premalignant skin lesions in Bangladesh. Cancer Epidemiol Biomarkers Prev 16:1270–1278. Ahsan H, Chen Y, Parvez F, Zablotska L, Argos M, Hussain I, et al.2006. Arsenic exposure from drinking water and risk of premalignant skin lesions in Bangladesh: baseline results from the Health Effects of Arsenic Longitudinal Study. Am J Epidemiol 163:1138–1148. Aposhian HV, Gurzau ES, Le XC, Gurzau A, Healy SM, Lu X, et al. 2000. Occurrence of monomethylarsonous acid in urine of humans exposed to inorganic arsenic. Chem Res Toxicol

13:693–697. Bates MN, Smith AH, Hopenhayn-Rich C. 1992. Arsenic ingestion and internal cancers: a review. Am J Epidemiol 135:462–476. Breslow NE, Day NE. 1987. Statistical Methods in Cancer Research: The Analysis of Cohort Studies. Lyon, France:International Agency for Research on Cancer. Cantor KP, Lubin JH. 2007. Arsenic, internal cancers, and issues in inference from studies of low-level exposures in human populations. Toxicol Appl Pharmacol 222:252–257. Chen YC, Guo YLL, Su HJJ, Hsueh YM, Smith TJ, Lee MS, et al.2003a. Arsenic methylation and skin cancer risk in southwestern Taiwan. J Occup Environ Med 45:241–248. Chen YC, Su HJJ, Guo YLL, Hsueh YM, Smith TJ, Ryan LM, et al. 2003b. Arsenic methylation and bladder cancer risk in
Taiwan. Cancer Causes Control 14:303–310.Cullen WR, Reimer KJ. 1989. Arsenic speciation in the environment. Chem Rev 89:713–764. Engel RR, Hopenhayn-Rich C, Receveur O, Smith AH. 1994. Vascular effects of chronic arsenic exposure: a review. Epidemiol Rev 16:184–209. Enterline PE, Henderson VL, Marsh GM. 1987. Exposure to arsenic and respiratory cancer. A reanalysis. Am J Epidemiol 125:929–938.Hertz-Picciotto I, Smith AH. 1993. Observations on the doseresponse curve for arsenic exposure and lung cancer.
Scand J Work Environ Health 19:217–226. Hopenhayn-Rich C, Biggs ML, Kalman DA, Moore LE, Smith AH.1996a. Arsenic methylation patterns before and after changing from high to lower concentrations of arsenic in drinking water. Environ Health Perspect 104:1200–1207. Hopenhayn-Rich C, Biggs ML, Smith AH, Kalman DA, Moore LE. 1996b. Methylation study of a population environmentally exposed to arsenic in drinking water. Environ Health Perspect 104:620–628. Hsueh YM, Chiou HY, Huang YL, Wu WL, Huang CC, Yang MH, et al. 1997. Serum beta-carotene level, arsenic methylation capability, and incidence of skin cancer. Cancer Epidemiol
Biomarkers Prev 6:589–596. International Agency for Research on Cancer. 1980. Evaluation of the Carcinogenic Risk of Some Metals and Metallic Compounds. Monogr Eval Carcinog Risk Chem Hum 23. International Agency for Research on Cancer. 1987. Overall Evaluations of Carcinogenicity: An Updating of IARC Monographs, Volumes 1 to 42. Monogr Eval Carcinog Risk Chem Hum Suppl 7. Karagas MR, Tosteson TD, Morris JS, Demidenko E, Mott LA,Heaney J, et al. 2004. Incidence of transitional cell carcinoma of the bladder and arsenic exposure in New Hampshire. Cancer Causes Control 15:465–472.Kitchin KT, Wallace K. 2008. The role of protein binding of trivalent arsenicals in arsenic carcinogenesis and toxicity. J Inorg Biochem 102:532–539.Kumagai Y, Sumi D. 2007. Arsenic: signal transduction, transcription factor, and biotransformation involved in cellular response and toxicity. Annu Rev Pharmacol Toxicol

47:243–262. Lamm SH, Engel A, Penn CA, Chen R, Feinleib M. 2006. Arsenic
cancer risk confounder in southwest Taiwan data set. Environ Health Perspect 114:1077–1082. Le XC, Ma M, Cullen WR, Aposhian HV, Lu X, Zheng B. 2000. Determination of monomethylarsonous acid, a key arsenic methylation intermediate, in human urine. Environ Health Perspect 108:1015–1018. Lee A, Fraumeni JF Jr. 1969. Arsenic and respiratory cancer in man: an occupational study. J Natl Cancer Inst 42:1045–1052. Lee-Feldstein A. 1983. Arsenic and respiratory cancer in humans—follow-up of copper smelter employees in
Montana. J Natl Cancer Inst 70:601–610. Lee-Feldstein A. 1986. Cumulative exposure to arsenic and its relationship to respiratory cancer among copper smelter employees. J Occup Med 28:296–302. Lin S, Cullen WR, Thomas DJ. 1999. Methylarsenicals and arsinothiols are potent inhibitors of mouse liver thioredoxin
reductase. Chem Res Toxicol 12:924–930. Lin S, Del Razo LM, Styblo M, Wang CQ, Cullen WR, Thomas DJ.
2001. Arsenicals inhibit thioredoxin reductase in cultured rat hepatocytes. Chem Res Toxicol 14:305–311. Lubin JH, Caporaso N. 2006. Cigarette smoking and lung cancer: modeling total exposure and intensity. Cancer Epidemiol
Biomarkers Prev 15:517–523. Lubin JH, Pottern LM, Blot WJ, Tokudome S, Stone BJ, FraumeniJF Jr. 1981. Respiratory cancer among copper smelter workers: recent mortality statistics. J Occup Med 23:779–784. Lubin JH, Pottern LM, Stone BJ, Fraumeni JF. 2000. Respiratory cancer in a cohort of copper smelter workers: results from more than 50 years of follow-up. Am J Epidemiol 151:554–565.Mandal BK, Ogra Y, Suzuki KT. 2001. Identification of dimethylarsinous and monomethylarsonous acids in human urine of the arsenic-affected areas in West Bengal, India. Chem Res
Toxicol 14:371–378. Mass MJ, Tennant A, Roop BC, Cullen WR, Styblo M, Thomas DJ,et al. 2001. Methylated trivalent arsenic species are genotoxic. Chem Res Toxicol 14:355–361. Morris HF. 1978. Testimony on proposed standard for exposure to inorganic arsenic (exhibit 28B) in Department of Labor,Occupational Safety and Health Administration: occupational exposures to inorganic arsenic—final standard. Fed Reg 43:19584–19629. National Research Council. 2006. Health Risks from Exposure to Low Levels of Ionizing Radiation (BEIR VII Phase 2). Washington, DC:National Academy Press. Petrick JS, Jagadish B, Mash EA, Aposhian HV. 2001. Monomethylarsonous acid (MMA(III)) and arsenite: LD50 in

hamsters and in vitro inhibition of pyruvate dehydrogenase.
Chem Res Toxicol 14:651–656. Preston DL, Lubin JH, Pierce DA, McConney ME. 2004. EPICURE: Risk Regression and Data Analysis Software, Release 2.0. Seattle:HiroSoft International Corporation. Pu YS, Yang SM, Huang YK, Chung CJ, Huang SK, Chiu AW, et al. 2007. Urinary arsenic profile affects the risk of urothelial carcinoma even at low arsenic exposure. Toxicol Appl
Pharmacol 218:99–106. Rahman M, Vahter M, Sohel N, Yunus M, Wahed MA, Streatfield PK, et al. 2006. Arsenic exposure and age- and sex-specific risk for skin lesions: a population-based case-referent study in Bangladesh. Environ Health Perspect 114:1847–1852. Steinmaus C, Bates MN, Yuan Y, Kalman D, Atallah R, Rey OA, et al. 2006. Arsenic methylation and bladder cancer risk in case-control studies in Argentina and the United States. J Occup Environ Med 48:478–488. Styblo M, Del Razo LM, Vega L, Germolec DR, LeCluyse EL, Hamilton GA, et al. 2000. Comparative toxicity of trivalent and pentavalent inorganic and methylated arsenicals in rat
and human cells. Arch Toxicol 74:289–299. Styblo M, Drobna Z, Jaspers I, Lin S, Thomas DJ. 2002. The role of biomethylation in toxicity and carcinogenicity of arsenic: a research update. Environ Health Perspect 110(suppl 5):767–771. Styblo M, Thomas DJ. 1997. Binding of arsenicals to proteins in an in vitro methylation system. Toxicol Appl Pharmacol 147:1–8.Valenzuela OL, Borja-Aburto VH, Garcia-Vargas GG, CruzGonzalez MB, Garcia-Montalvo EA, Calderon-Aranda ES, et al. 2005. Urinary trivalent methylated arsenic species in a population chronically exposed to inorganic arsenic. Environ Health Perspect 113:250–254 Waters SB, Devesa V, Fricke MW, Creed JT, Styblo M, Thomas
DJ. 2004. Glutathione modulates recombinant rat arsenic (+3 oxidation state) methyltransferase-catalyzed formation of trimethylarsine oxide and trimethylarsine. Chem Res
Toxicol 17:1621–1629. World Health Organization. 2001. Environmental Health Criteria 224: Arsenic and Arsenic Compounds. 2nd ed. Geneva:World Health Organization. Yu HS, Liao WT, Chai CY. 2006. Arsenic carcinogenesis in the
skin. J Biomed Sci 13:657–666. Yu RC, Hsu KH, Chen CJ, Froines JR. 2000. Arsenic methylation capacity and skin cancer. Cancer Epidemiol Biomarkers
Prev 9:1259–1262. Inhaled arsenic and respiratory cancer mortality

Environmental Health Perspectives

• VOLUME 116 | NUMBER 12 | December 2008 

### 1665