An Epidemiologic Study of Arsenic-related Skin Disorders...

HERA 2007-01-25 Text to Journal.doc

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An Epidemiologic Study of Arsenic-related Skin 1

Disorders and Skin Cancer and the Consumption of 2

Arsenic-Contaminated Well Waters in Huhhot, Inner 3

Mongolia, China 4

5 By 6

Steven H. Lamm,1,2,3,4 Zhen-Dong Luo,1,5 Fu-Bao Bo,1,5 Ge-You Zhang,1,5 Ye-Min 7

Zhang,1,5 Richard Wilson,1,6 Daniel M. Byrd,1,7 Shenghan Lai,1,8 Feng-Xiao Li,2,9 8

Michael Polkanov,6,11 Ying Tong,10 Lian Loo,10 Stephen B. Tucker,1,10 and the Inner 9

Mongolia Cooperative Arsenic Project (IMCAP). 10

11

1. Inner Mongolia Cooperative Arsenic Project Washington DC USA/Huhhot IM PRC 12

2. Consultants in Epidemiology and Occupational Health, LLC Washington, DC USA 13

3. Georgetown University School of Medicine, Department of Pediatrics Washington, DC USA 14

4. Johns Hopkins University – Bloomberg School of Public Health Baltimore, MD USA 15

5. Huhhot Center for Disease Control and Prevention Huhhot, Inner Mongolia PRC 16

6. Harvard University, Department of Physics Cambridge, MA USA 17

7. Consultants in Toxicology, Risk Assessment, and Product Safety McLean VA USA 18

8. Johns Hopkins Medical Institute, Department of Pathology Baltimore MD USA 19

9. University of Calgary Calgary, AB Canada 20

10. University of Texas, Department of Dermatology Houston, TX USA 21

11. Now at ESRI Redlands, CA, USA 22

23


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Submitted to: 1

2

Human and Ecological Risk Assessment 3

January 3, 2007 4

Revision based on comments of Feng, Wilson, Lai, Byrd, and Reviewers 5

6

7

Corresponding Author: 8

9

Steven H. Lamm, MD, Director 10

Inner Mongolia Cooperative Arsenic Project (IMCAP) 11

3401 38th Street, NW #615 Washington, DC 20016 12 13 14 Tel: 202/333-2364 e-mail: [email protected] 15

16

Running Head: Skin Cancer and Arsenic Dermatosis in Inner Mongolia 17

18


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ABSTRACT: 1 Well-use histories were obtained and dermatological examinations were 2

conducted for 3,179 of the 3,228 (98.5%) residents of three villages in Inner Mongolia 3

with well water arsenic levels as high as 2,000 ppb (ug/L). Eight persons were found to 4

have skin cancer, 172 had hyperkeratoses, 121 had dyspigmentation, 94 had both 5

hyperkeratoses and dyspigmentation, and, strikingly, none had Blackfoot disease. All 8 6

subjects with skin cancer also had both hyperkeratoses and dyspigmentation. 7

Arsenic levels were measured for 184 wells, and individual well-use histories 8

were obtained. Arsenic exposure histories were summarized as both highest arsenic 9

concentration (highest exposure level for at least one-year duration) and cumulative 10

arsenic exposure (ppb-years). 69 % of the participants had highest arsenic concentrations 11

below 100 ppb; 71 % had cumulative arsenic exposures below 2,000 ppb-years. 12

Exposure-response analyses included frequency-weighted, simple linear regression, and 13

most-likely estimate (hockey-stick) models. 14

Skin cancer cases were only found for those with a highest arsenic concentration 15

greater than 150 ppb, and those with exposure less than 150 ppb had a statistically 16

significant deficit. A frequency-weighted model showed a threshold at 150 ppb, and a 17

hockey-stick model showed a threshold at 122 ppb. Considerations of duration, age, 18

latency, and misclassification did not appear to markedly affect the analysis. The non-19

malignant skin findings showed thresholds of 40-50 ppb in the hockey-stick models. 20

Application of these analytic models to the data from other epidemiological studies of 21

arsenic ingestion and malignant and non-malignant skin disorders can be used to examine 22

patterns of arsenic carcinogenicity. 23


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1

Key Words: Arsenic-related skin effects; Skin cancer risk; Inner Mongolia; Threshold 2

(Hockey-stick) model 3


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1. INTRODUCTION: 1

2

In May 1990, the Sanitation and Anti-Epidemic Station (public health 3

department) in Huhhot, Inner Mongolia (China) sought to determine why the public 4

health clinic for the village of Zhi Ji Liang requested more dermatological medications 5

than other clinics. Examination of the dermatological cases in this village, and in two 6

additional villages (Tie Men Geng and Hei He), led to the identification of chronic 7

arsenicism. Investigations found that the local wells providing the water supply were the 8

source of the arsenic exposure. The arsenic levels of the wells in the three villages were 9

determined, and a dermato-clinical assessment of the villagers was conducted. 10

Well-water samples from the wells in the three villages revealed arsenic above the 11

Chinese (and World Health Organization) health standard of 50 ug/l levels in 12

approximately two-thirds of the wells. The well-waters were also tested for other water 13

quality and inorganic measures. Additional sources of arsenic exposure were sought, 14

including occupational, therapeutic, and dietary sources, but no other demonstrable 15

sources were found. Environmental sampling included indoor and outdoor air, soil, and 16

water. Well-use histories were obtained from each participant with start and stop dates 17

by year for each well used. 18

The dermatological examinations were conducted independently by physicians 19

with prior diagnostic specification for the presence of hyperkeratoses, skin 20

dyspigmentation (hyper - or hypopigmentation), and skin cancer. 21

This study was the first population analysis conducted on arsenic ingestion and 22

the prevalence of arsenic dermatoses (including skin cancer) that collected individual-23


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specific historical data on arsenic consumption and current dermatological findings. This 1

paper updates earlier, less detailed, descriptions of the study (Luo et al. 1993a, Luo et al. 2

1993b, Luo et al. 1997) and compresses the extended study report (Tucker et al. 2001). 3

The study was undertaken as an opportunity to seek replication of the SW Taiwan study 4

of arsenic skin lesion and cancer prevalence (Tseng et al. 1968) and because of our 5

interest in the mechanisms of arsenic carcinogenicity. 6

The well-use histories and well water arsenic measures for each of approximately 7

three thousand subjects were summarized as two exposure metrics - highest arsenic 8

exposure [HAC] in ppb and cumulative arsenic exposure [CAE] in ppb-years - and used 9

in the analytic description of the shape of the dose-response relationship. The 10

individualized exposure assessment allowed for the reasonable examination of dose-11

response relationships, without the additional assumptions that use of ecological 12

exposure assignments would have necessitated and without the a priori assumptions of a 13

non-threshold model. 14

Previously published analysis of ecological data about the relationship between 15

skin cancer and arsenic ingestion, particularly the Tseng et al. (1968, 1977) studies of the 16

Blackfoot-disease endemic area of SW Taiwan, had suggested a threshold model for skin 17

cancer prevalence (Byrd et al. 1996). For skin cancer mortality, the data provided a good 18

fit to a cubic model rather than to a threshold model (Byrd et al. 1996). The present 19

study, however, provided an opportunity to examine the same models now with an 20

epidemiological database that contained individualized exposure and outcome data. 21

The dose-response literature on arsenic and skin cancer is scant, and almost all of 22

it is from Taiwan. Guo et al. (1998), in an analysis of skin cancer incidence in the 243 23


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townships of Taiwan, showed a significant rate increase only for townships with wells 1

having arsenic levels greater than 640 ppb. Cancer-registry based studies do not include 2

information on non-malignant arsenic skin disease. Guo et al. (2001) demonstrated that 3

the association between arsenic and skin cancer incidence was limited to squamous cell 4

carcinoma and basal cell carcinoma but did not include malignant melanoma. Hsueh et 5

al. (1997), in a study of three arseniasis-endemic villages in Taiwan, found the incidence 6

of skin cancer to be significantly associated with average artesian well arsenic 7

concentrations greater than 700 ppb. This study from the arseniasis area did not report on 8

signs of arsenicosis other than skin cancer. While Mukherjee et al. (2005) reported for 9

one district of Bangladesh prevalences of 4.7 % for carcinoma-in-situ (Bowen’s disease) 10

and 0.6 % for cancer among arseniasis-affected adults, these data are not dose-related. 11

In contrast, the dose-response literature on arsenic and skin lesions is large and 12

comes mainly from SE Asia. The studies from Bangladesh (Guha Mazumder et al. 1998; 13

Ahsan et al. 2000; Ahsan et al. 2006) and West Bengal (Haque et al. 2003) have 14

examined dose-response relationships for arsenic ingestion and arsenicosis 15

(hyperkeratoses and dyspigmentation) with a variety of arsenic exposure metrics, but 16

they have not presented skin cancer dose-response data in the same populations. The 17

terms arsenicosis, chronic arsenicism, arseniasis, arsenic dermatosis, and arsenic skin 18

lesions refer to the same condition, though the specific criteria may differ between study 19

areas. Early reports on arsenical skin lesions reported increased prevalence at exposures 20

of 200 ug/L and greater in West Bengal (Chakraborty and Saha 1987) and at greater than 21

350 ug/L in Bangladesh (Tondel et al. 1999). More recent studies find cases at lower 22

exposure levels. 23


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In a case-cohort analysis of newly diagnosed cases of skin lesions in the HEALS 1

(Health Effects of Arsenic Longitudinal Study) in Bangladesh (Hall et al. 2006) where 2

arsenic exposure measures included blood, urine, and drinking water samples, an 3

increased incidence of skin lesions appeared among those with baseline water arsenic 4

levels at 39-94 ppb and was significantly increased only among those at greater than 113 5

ppb (in the groups with mean water arsenics of 138 and 312 ppb). Similarly, a case-6

referent study from Matlab, Bangladesh showed an increase in skin lesion cases in both 7

males and females at 50 + ug/L (Rahman et al. 2006), and an ecological study in 53 8

widely-scattered villages of Bangladesh showed a dose-response among women at > 50 9

ug/L (McDonald et al. 2006). A study in a village in another area of Inner Mongolia 10

found an increase in dyspigmentation cases with arsenic levels greater than 50 ug/L but 11

no association for keratosis and no skin cancer (Guo et al. 2006). 12

Other than the Tseng et al. (1968; 1977) study, this paper presents the only 13

epidemiological study that provides dose-response information simultaneously on both 14

arsenic dermatosis (hyperkeratoses and dyspigmentation) and skin cancer. It is most 15

striking that these studies from Bangladesh and West Bengal do not report skin cancer 16

findings. 17

18

19

2. STUDY POPULATION: 20

21

The three study villages are in the Huhhot region of Inner Mongolia, south of the 22

Daqing (Great Green) Mountains and along the northern coast of the Yellow River. The 23


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participants comprised 3,228 of the 3,229 residents of the three villages. Well-use 1

histories were obtained on all but 45 study participants; Dermatological skin disease 2

diagnostic data were recorded for all but 4 of the study participants. Thus, both well-use 3

history data and dermatological findings were obtained for almost all participants 4

(3,179/3,228 = 98.5%). Eight individuals had well-use histories that included use of 5

unmeasured wells (0.2%). Their arsenic exposure estimates were based on their use of 6

the wells with measured arsenic levels. The average well-use history was greater than 25 7

years. 8

Table 1 shows demographic distributions in the three villages of the individuals 9

with known exposure and outcome status, as well as for those with either unknown use or 10

outcome status. Forty-five of the 49 participants with unknown exposure or outcome 11

status were children less than 10 years old. The proportion male were similar in the 12

three villages, and almost all study subjects identified themselves as being of Han 13

(Chinese) origin rather than of Mongolian origin. The seven individuals of Mongolian 14

ethnic origin lived in Hei He. 15

Insert Table 1 16

Subsequent analyses were limited to the 3,179 persons with both known exposure 17

and outcome status. Table 2 shows the age distributions in the three villages with age 18

being obtain for all but four study participants (one from Tie Men Geng and three from 19

Hei He). The median age was 29 years. Participants from Hei He tended to be older 20

than those from Tie Men Geng and Zhi Ji Liang. All subjects with a known occupation 21

were either students or farmers, whether male or female. 22

Insert Table 2 23


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1

3. EXPOSURES: 2

3.1. Wells and Arsenic (As) concentrations

The village water supply came from underground waters that were in a Q4 earth

stratum with a local rock having a high concentration of arsenic. Groundwater arsenic in

Inner Mongolia is 85% soluble with two-thirds of the soluble arsenic being As+3 (Gong et

al. 2006). The Huhhot Sanitation and Anti-Epidemic Station collected and analyzed well

water samples, using a silver diethyl-dithiocarbamate colorimetric methodology with a

10 ug/l detection limit (Fan et al. 1993; Zhang et al. 1994). The Chinese Academy of

Preventive Medicine Laboratory of Environmental Engineering supervised quality

control with split samples at the National Taiwan University laboratory.

Arsenic measurements were available for 184 of the 187 wells cited in the well-

use histories. The three other wells had been closed in 1957-1959 and were not available

for testing. Use of these wells was reported by only eight participants and had occurred

beginning as early as 1920.

The frequency distribution of the wells by villages, by the number of sample

taken, and by As concentration groups are presented in Table 3.1. The three villages had

45, 60, and 79 sampled wells. Most wells (n = 165) had only one sample, while some (n

= 18) had two samples, and one well had three samples. The geometric mean was used

for wells with multiple measurements. The As concentrations for the 184 wells varied

widely from non-detectable (


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Insert Table 3.1

The data on the 19 wells with more than one measurement were examined for

their variation over time, ranging from 1 to 6 years with a mean of 3 ¼ years. The

distributions of paired measurements were examined (Figure 1). Paired t-test analysis of

the log-transformed well water arsenic levels found no significant difference between

those for the first and the second samples (p = 0.30; Pearson correlation = 0.65). The

geometric means were 83 and 111 ppb, respectively.

The original Chinese studies used a minimum exposure duration of 6 months in

their reports, based in part on legal criteria for compensation. This report includes

exposures based on years of water consumption with a minimum exposure period of 12

months.

3.2. Measurement of exposure

Arsenic exposures of the subjects were analyzed using two different measures, the

highest arsenic concentration (HAC, in ppb) of the well waters ever consumed [minimum

duration = one year] and the cumulative arsenic exposure (CAE, in ppb-year) determined

from the individual’s history of wells used. The highest arsenic concentration (HAC)

was the highest arsenic level for which the participant had at least one year of exposure.

The well-use histories of the participants included as many as five different wells for a

single individual.

The individual’s complete well-use history while resident in these villages was

utilized in calculating the cumulative exposure. The cumulative arsenic exposure was


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calculated using the following formula: CAE = sum of (arsenic concentration X exposure

years) for each well use. For purposes of calculation, the samples with non-detectable

arsenic levels were set at 5 ppb, half of the detection limit. The descriptive statistics of

the highest arsenic concentration and the lifetime cumulative arsenic exposures are

displayed in Table 3.2. The numbers in the two groups differ, because the well use

history of one individual identified the wells used but not the time periods. Thus, for one

person a highest arsenic concentration could be calculated but not a cumulative arsenic

exposure.

Insert Table 3.2

Data do not exist on the daily water consumption rate of the participants from

these villages. However, as all three villages are similar agrarian communities in close

proximity to each other, we assumed that water consumption rates in the villages were

similar. This should not affect analyses when exposures are reported as either ppb

arsenic or ppb-years arsenic. However, such an assumption would affect analyses where

exposures are reported as either milligrams of arsenic per day or cumulatively in

milligrams or grams.

3.3. Categorization of exposure

We categorized the study population into eight highest arsenic concentration

(HAC) groups to obtain subgroups with similar numbers of subjects, in all but the end

exposure group. The descriptive statistics for the eight groups are displayed in Table 3.3.

The arithmetic means, the geometric means, and the medians for each HAC group were


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compared. The three summary statistics of HAC exposures are similar both in

distributive pattern and in size (Table 3.3 and Figure 2). The arithmetic mean was used as

the representative exposure measure in the group analyses.

Thirty-five percent of the study population (1,104/3,179 = 35%) had a HAC

exposure of less than 50 ppb, 86% (2,721/3,179 = 86%) of the study population had a

HAC exposure of less than 150 ppb, and only 1 % of the study population had a HAC

exposure of 500 ppb or greater.

Insert Table 3.3

The study population by the cumulative arsenic exposure (CAE) was

approximately log normally distributed. The study population was categorized into eight

exposure groups of equal intervals (0.5 log units) on the logarithmic scale (Table 3.4;

Figure 3). For each CAE group, there was little difference between the arithmetic mean,

the geometric mean, and the median.

Insert Table 3.4

The cumulative study population percentage against the means of the arsenic

exposure intervals showed that the study population was predominantly in the lower

exposure levels, whether expressed as HAC or CAE. The CAE ranged from 5 to 20,372

ppb-yrs with 52% of the study population having a CAE or less than 1,000 ppb-years and

71% of the study population having a CAE of less than 2,000 ppb-years.

3.4 Alternative sources of arsenic


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Alternative sources of arsenic exposure were sought. The western Huhhot basin

is an agricultural area where wheat, millet, corn, green beets, potatoes and sunflowers,

are raised, but without the use of arsenical pesticides. There are no local factories, mines

or other industries that discharge arsenic into the local air, water, or soil. Examination of

the surface soils, air, fish and crops revealed arsenic levels similar to those of the general

Chinese culture. The smoking habits in Huhhot do not differ from those of the general

Chinese culture (Luo et al. 1997). While the use of coal for household heating and

cooking is another potential source of arsenic exposure, its use, though unquantified, was

not identifiably different across the three villages. Well-water was the sole identified

source of arsenic exposure, and it was found to relate to the prevalence of both skin

cancer and non-malignant skin effects.

4. OUTCOMES:

Three dermatological disorders were recorded - hyperkeratoses, dyspigmentation

(hyperpigmentation/hypopigmentation of the trunk) and skin cancer – as prevalence cases

from the clinical surveys. Chinese physicians conducting the original survey diagnosed

hyperkeratoses, dyspigmentation, and skin cancer, using their established clinical criteria

(Luo et al. 1997; Niu et al. 1997). No cases of Blackfoot disease were observed. The

term hyperkeratoses in this report referred to obvious thickening of skin on the palms and

soles in palpable and/or wart-like bumps ranging in size from about approximately 0.2 to

1.5 cm over large areas, whether separated or coalesced. Dyspigmentation referred to

coarse skin with moderately-sized spots of pigmentation, distributed in a web-like form.


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The diagnosis of dyspigmentation was made on the basis of findings on the trunk of the

body rather than on findings on the extremities, as they were unlikely to be confounded

by solar (actinic) exposure. Clinical skin cancer diagnoses were independently

substantiated clinically and histologically (both basal cell and squamous cell carcinomas)

by a US participant (SBT), who also verified the non-malignant cutaneous findings.

The analyses have been conducted for non-malignant arsenic dermatosis [i.e.,

hyperkeratoses; dyspigmentation; hyperkeratoses with dyspigmentation] that are

commonly attributed to arsenic exposure (Luo et al. 1997; Niu et al. 1997; Cebrian et al.

1983; Tseng et al. 1968) and for malignant arsenic dermatosis (i.e., skin cancer).

Hyperkeratoses was the most prevalent skin disease in the study population (5.4%), skin

dyspigmentation was second (3.8%). Combined hyperkeratoses and dyspigmentation had

a prevalence of 3.0%. Of the observed skin conditions, skin cancer was the

dermatological finding with the lowest prevalence (0.3%). The prevalence of

hyperkeratoses without dyspigmentation can be calculated in each dose group from the

difference between the prevalence of hyperkeratoses and the prevalence of

hyperkeratoses with dyspigmentation. An analogous statement holds for the prevalence

of dyspigmentation without hyperkeratoses. Additionally, all eight study subjects with

skin cancer had both hyperkeratoses and dyspigmentation.

Table 4.1 shows the number and prevalence of each type of skin disorder in the

study population and prevalence of skin cancer in each clinical group.

Insert Table 4.1

All eight cases of skin cancer occurred among the 94 persons with both

hyperkeratoses and dyspigmentation (prevalence = 8.5%). While no skin cancer


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occurred in this study among those without both dermatological effects, skin cancer was

observed in only one-twelfth (8.5%) of the subjects who had both hyperkeratoses and

dyspigmentation. Most persons (92%) with both hyperkeratoses and dyspigmentation did

not develop skin cancer. While in this study the data seem to suggest that the combined

clinical findings of hyperkeratoses and dyspigmentation was a necessary but insufficient

condition for skin cancer, in SW Taiwan only two-thirds of the skin cancers occurred in

those with both hyperkeratoses and dyspigmentation.

5. RELATIONSHIPS BETWEEN EXPOSURES MEASURED AS HAC AND

OUTCOMES:

Three formulae (models) were used to analyze the data. The first two, a

frequency-weighted model and a simple linear regression model, are described because

they are simple and widely used as initial models by epidemiologists. The third, a

“hockey stick model”, is a more rigorous model. It is an unconstrained extension of

EPA’s “multistage” model and permits the demonstration of an inflection point within

the data wherein the slope and above and below are not identical, i.e., an apparent

threshold. Such models cannot ‘prove” the presence of a threshold, but they are able to

demonstrate that a simple straight line relating the outcome at high exposures uniformly

to zero overstates the outcome at low doses. This is shown in different ways in the three

models.

5.1. Frequency-weighted model


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The relationship between highest arsenic concentration and the prevalence of each of the

four dermatological outcomes was examined using a frequency-weighted model (Table

5.1), generally showing a monotonically increasing exposure-response pattern.

Hyperkeratoses, dyspigmentation, or both combined were generally observed in all

exposure groups. In contrast, skin cancer cases were observed only in the two highest

arsenic concentration groups, giving the impression of a threshold effect of arsenic

concentration on skin cancer at a level of about 150 ppb or greater.

A frequency-weighted model was constructed as follows: The predicted numbers

of cases were obtained for each outcome and for each HAC exposure group assuming (a)

that the total number of cases expected in the population was equal to the total number

observed in the study population, (b) that none of the observed cases were background

(actinic) cases, and (c) that the distribution of the cases were expected to be similar to

the distribution of the exposure in units of ppb-person (Table 5.1). The formula used for

the calculation was:

Ni × Xi Ni: the number of subjects in the ith strata

Nexpected = --------------------- × Nt Xi: the mean of HAC intervals in ith strata

∑i=18 (Ni × Xi) Nt: the total number of cases observed

Table 5.1 shows the relationship between highest arsenic concentration and the

four skin conditions with the observed and predicted prevalences demonstrated by the

means of the HAC intervals for each of the four skin disorders.

Insert Table 5.1

The observed case counts and prevalences for the non-malignant skin disorders

(hyperkeratoses, dyspigmentation, or both) seem to be better predicted by the frequency-

weighted model than are those for the skin cancers. Overall comparison of the observed


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and the predicted number of cases found no significant difference in their distribution for

either the non-malignant skin disorders ( Χ2df=7=11, p=0.13 for hyperkeratoses;

Χ2df=7=10, p=0.17 for dyspigmentation; Χ2df=7=5.3, p=0.62 for both combined) or the

skin cancers ( Χ2df=4 = 6.3, p = 0.18). Skin cancer, however, tended to be over-predicted

for exposures below 150 ppb and to be under-predicted for exposures of 150 ppb or

greater. The number of observed lesions lay significantly below the expectation for the

low HAC groups, suggesting the possibility that better fits may be obtained for models

that permit a threshold or other sub-linear dose-response relationship.

5.2. Simple linear model

The prevalences (P) of the four skin disorders were formally fitted to a simple

linear function of the means of the HAC intervals (P = α+ β*exposure) that included a

possible background term, α, using an unconstrained least squares linear model (MS

Excel) that gave equal weight to each exposure group. α was not constrained to be

positive, and P was not limited to be below unity. The fitted parameters of the model for

each of the four outcomes are presented in Table 5.2.

Insert Table 5.2

The four simple linear regressions are all statistically significant, i.e there is a

non-zero slope showing that the lesions depend upon arsenic concentration in the well

water (Table 5.2). The measure of arsenic contamination (i.e., the mean of the HAC

intervals) explains about 99% of the overall variation of prevalence for the four skin

disorders. The unit risk (change of the prevalence with each unit change of the mean of


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the HAC interval), or the slope calculated by this model, is similar for the non-malignant

skin disorders and is about an order of magnitude higher than the slope for skin cancer

(Figure 4).

The p-values in Table 5.2 are the probabilities that there is no true exposure

dependence (i.e., β = 0) and that the events are random samples. The p values for the X-

intercepts have been calculated and represent the probability that the true threshold is

zero. The X-intercepts for the non-malignant skin disorders are not significantly

different from zero in the simple linear model, though the observed value of 43 ppb for

skin cancer cannot be explained by chance (i.e., p < 0.05).

In order to determine whether the relationships between the exposures to arsenic

and the four outcomes are consistent with a non-threshold linear model or with a

threshold linear model, the x-intercept (-α / β) of each fitted linear model was determined.

The x-intercept and its 95% confidence interval were calculated for each outcome

examined using STATA and considered to be the best estimate and range of the potential

HAC threshold values (Table 5.2). Skin cancer had the greatest x-intercept (43 ppb with

95% CI 0.4 – 96) as compared to hyperkeratoses (4.9 ppb with 95% CI -19 – 33) or

dyspigmentation (1.4 ppb with 95% CI -24 – 31), or both non-cancer skin lesions

combined (14 ppb with 95% CI -4.8 – 33) (Table 5.2). With simple linear analysis, only

the data for skin cancer showed a 95% confidence interval of the x-intercept which

excluded the value of zero ppb. Thus, based on simple linear analysis, only the data for

skin cancer were inconsistent with the non-threshold linear model.

5.3. Hockey-stick model


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The simple linear function unrealistically allows P (prevalence) to be negative at low

doses and to be greater than unity at high doses which would slightly understate the case

for a threshold. (Cox 2002) A more realistic function, the hockey-stick function, was

used which does not permit P to be negative at low doses or greater than unity at high

doses. Specifically: P = 1-exp(-α ) for exposures (d) less than a threshold (dt), and P = 1-

exp( - ( α + β*(d-dt) + γ*(d-dt)2 +.. ) for d > dt where α (alpha) , β (beta) and γ (gamma)

etc. are constrained to be positive. The ‘multistage” formula used by US EPA is the

special case of this model in which dt=0, the doses in the study represent stages, and α , β,

and γ are constrained to be positive.

The hockey-stick model extends the “multistage” model by allowing for the

possibility of a non-zero intercept (threshold or no increased risk). The data were fitted to

this function by a maximum likelihood model, using the minimization routine in

QUATTROPRO, and using a specific program that was provided by Dr Edmund A. C.

Crouch of Cambridge Environmental Inc.

The prevalence of each of the four skin disorders was fitted to this hockey-stick

model. In no case were powers of dose higher than linear significant. Inclusion of these

possible terms did not appreciably affect the derived parameters. 1-exp( -α ) ~ α is the

“background” of the lesion at zero exposure. The parameters of these models fitted for

each of the four outcomes are presented in Table 5.3, and the graphic representation of

this model is displayed in Figure 5.

Insert Table 5.3


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In a maximum likelihood model with constraints, multiple minima can occur that

may have a reasonable goodness-of-fit. The minima with a reasonable goodness-of-fit

that exclude zero from their 95% confidence intervals are presented with the regression

coefficients and the goodness-of-fit (GOF) test for the four skin conditions, using the

HAC exposure measurement [Table 5.3]. The data for all four groups showed acceptible

fits to the hockey-stick model using only a linear term in dose (i.e., p for goodness-of-fit

test > 0.05). The threshold level (dt) for skin cancer (122 ppb, 95% CI 88 - 137) is two

to three-fold those for hyperkeratoses (42 ppb, 95% CI 34 – 46 and 30 ppb, 95% CI 23 -

32), dyspigmentation (50 ppb, 95% CI 40 – 57 and 47 ppb, 95% CI 38 - 53), or both

combined (42 ppb, 95% CI 30 – 50) [Table 5.3]. There was a second fit to the skin

cancer data with a lower GOF p-value (0.12) but with a threshold value at 5 ppb that was

not significant.

The range of uncertainty for the threshold (non-zero intercept) was found by

plotting Χ2 values against the assumed threshold achieved when the model parameters

were readjusted to get the best fit. The X 2 value was increased above the minimum value

(Table 5.3) by +2 for the two threshold exposure values that differ from the best value by

two standard deviations (approximately the 95% confidence intervals) and by +1 for one

standard deviation (not shown).

The threshold values (dt) for the hockey-stick models in Table 5.3 exceed the X-

intercepts for the simple linear fit models in Table 5.2, as the computational routine of

the model does not have to try unsuccessfully to fit the zero lesions at exposures below

the threshold.


3/14/2007 Page 22

All the fits of the model to the data were acceptable (p > 0.05), but the fits for

hyperkeratoses and dyspigmentation were not very good. Additional parameters usually

improve a fit, but when we added extra terms with powers of the exposures greater than

one, the coefficients of these terms were zero and the goodness-of-fit was only slightly

improved. As is usual in fits to cancer models, the coefficients were constrained to be

positive. Not all analysts regard tests of higher powers within a hockey-stick model to be

a reliable test of the existence of a threshold. Nonetheless, neither a quadratic nor a cubic

or higher term would improve the fits.

None of the skin cancers were observed at exposure levels below the calculated

skin cancer threshold level, and only one case of hyperkeratoses with dyspigmentation

was reported below 30 ppb.

6. RELATIONSHIP BETWEEN CUMULATIVE ARSENIC EXPOSURE (CAE)

AND OUTCOME:

6.1. Frequency-weighted model

The relationships between cumulative arsenic exposure and each of the four skin

disorders were also examined. As with the highest arsenic concentration, a general

exposure-prevalence pattern (higher prevalence for higher cumulative arsenic exposure

group) was also seen for all four skin disorders. A test for linear trend in proportions

was highly significant for the prevalences of each of the four outcomes examined (all p <

0.01). (Armitage 1955) Skin cancer cases occurred only in the three highest cumulative

arsenic exposure groups (>1000 ppb-years), also suggesting a threshold for cumulative


3/14/2007 Page 23

exposure to arsenic on skin cancer, but consistent with both a threshold and a non-

threshold linear model.

The expected number of cases for the four outcomes was calculated using the

same formula for CAE as for HAC exposures (Nexpected = ((Ni x Xi)/ ∑ i=1 (Ni x Xi)) x

Nt). The frequency-weighted approach predicts both non-malignant skin disorders and

skin cancer, with some over- and under- predicting measures [Table 6.].

Insert Table 6.1

A comparison of the observed and the expected number of cases showed

insignificant differences for the non-malignant skin disorders ( Χ2df=7=1.7, p=0.78 for

hyperkeratoses Χ2df=7=1.7, p=0.80 for dyspigmentation; Χ2df=7=1.7, p=0.79 for both

combined) and for skin cancer ( Χ2df=4=1.1, p =0.77).

6.2. Simple linear model

As with the HAC exposure measure, the prevalence of the four skin disorders was

also linearly fitted against the mean of the CAE intervals. The parameters of the least

squares fit for each of the four outcomes are presented in Table 6.2. The p-values

represent the likelihood that the slope is not different from zero.

Insert Table 6.2

All four simple linear regression analyses are highly significant (Table 6.2). The

slopes clearly differ from zero. The cumulative exposure to arsenic in drinking water

explains 99% or more of the overall variation of prevalence within the cumulative

exposure groups for the four skin disorders. Again, the unit risk is similar for


3/14/2007 Page 24

hyperkeratoses and dyspigmentation or both combined, while skin cancer has the lowest

unit risk at about an order of magnitude lower (Figure 6). As with HAC exposures, skin

cancer has the greatest x-intercept (313 ppb-yrs with 95% CI -100 – 641), about an order

of magnitude greater than those for hyperkeratoses (43 ppb-yrs with -42 – 141),

dyspigmentation (35 ppb-yrs with 95% CI -115 – 175), or both combined (25 ppb-yrs

with 95% CI -143 – 211). As none of these x-intercepts are significantly different from

zero, these analyses do not show evidence of a threshold with respect to the cumulative

measure of arsenic exposure.

Cumulative measures of exposure include time, in addition to well water

concentration, so HAC and CAE exposures are not necessarily comparable and their

analyses are not necessarily in conflict. (Rozman 1998) The toxicological concept of a

threshold implicit in an acceptable daily intake estimate does not include duration of

exposure.

6.3. Hockey-stick model

The prevalences of the four skin disorders were also fitted as functions of both the

means of CAE intervals and potential thresholds (i.e., non-zero intercepts), using the

same hockey-stick model and procedures used for the HAC exposures. The parameters

of the hockey-stick model for each of the four outcomes are presented in Table 6.3 with

the regression coefficients and the goodness-of-fit test.

Insert Table 6.3


3/14/2007 Page 25

The fits are very good (p for goodness-of-fit test >> 0.05 and close to unity) for

all the four disorders with statistically significant threshold values for the three non-

malignant disorders [hyperkeratoses (353 ppb-yrs, 95% CI 213 – 459 and 135 ppb-yrs,

95% CI 50-172), dyspigmentation (440 ppb-yrs, 95% CI 263 - 560), or both combined

(406 ppb-yrs, 95% CI 206 - 532)].

The skin cancer data analysis revealed three minima including one at 617 ppb-yrs

that was significant on a one-tailed test (95% CI 179-1019) but not a two-tailed test (95%

CI –33 - +1168). These analyses did not include models anchored at any well water

concentrations above zero. Therefore, these analyses cannot exclude non-zero

thresholds. The observed prevalences versus the means of the CAE intervals for skin

cancer are displayed in Figure 7.

If dt is set to equal zero and the number of stages and direction of parameters are

constrained, the EPA “multistage” model is obtained. The “multistage” model using CAE

provides excellent goodness-of-fit P-values of 0.84, 0.48, 0.77, and 0.98 for

hyperkeratoses, dyspigmentation, hyperkeratoses with dyspigmentation, and skin cancer,

respectively but requires terms in d2 and d3

7. AGE-ADJUSTMENT OF SKIN FINDING PREVALENCES BY EXPOSURE

GROUP:

Age is a confounder of prevalence from chronic exposure, because susceptibility

can change with age. (Rozman 1998) Application of age-stratification of the crude rate

to a standard population distribution adjusts for the degree of confounding contributed by


3/14/2007 Page 26

the differences in age structure of comparative populations. The age-sex adjusted

prevalence rates were calculated by applying the age-sex exposure group-specific

prevalences to the age-sex proportional distribution of the full study population. The age-

sex distribution of the full study population (ages < 20, 20-39, 40-59, and 60 +; sex male

and female) was used as the standard population distribution. Both crude prevalences and

age-adjusted prevalences for the various dermatological conditions when examined by

HAC exposure group are shown in Table 7.1. The age-adjusted prevalences for the

various dermatological conditions stratified by the HAC exposure differ little from the

crude rates (Table 7.1), suggesting no major confounding by age for the HAC exposure.

Insert Table 7.1

Similarly, Table 7.2 presents the crude and age-adjusted prevalences for the

various dermatological conditions when examined by CAE group. The CAE analysis

shows that age-adjusted prevalences for the non-malignant skin conditions tend to be

greater than the crude rates, while the age-adjusted prevalence rates for the malignant

skin condition tend to be lower than the crude rates.

Insert Table 7.2

8. DISCUSSION OF THE RELATIONSHIP BETWEEN SKIN DIAGNOSES

AND ARSENIC EXPOSURES:

This report examines the relationship between chronic arsenic exposure and four

skin conditions (hyperkeratoses, dyspigmentation, hyperkeratoses with dyspigmentation,

and skin cancer), using findings for 3,179 residents of three villages in Huhhot, Inner


3/14/2007 Page 27

Mongolia, China. The arsenic exposures arose mostly from the consumption of local

arsenic-contaminated well water. The exposures were estimated, both using their highest

arsenic concentration (ranging from non-detect to 2,000 ppb As) and using their

cumulative arsenic exposure (ranging from non-detect to 20,372 ppb-yrs). A generally

monotonically increasing exposure-response pattern was found for all four skin

conditions and for both indicators of exposures. However, in some circumstances (Table

8.1; Figure 8), comparison of the actual number and predicted number indicates that the

model fail to fit the data unless a threshold is assumed. With HAC < 150 ppb, 4.55 cases

were expected, while none were observed (p = 0.02).

Insert Table 8.1

8.1. Model comparisons

The interpretations of the three analytic models [frequency-weighted, simple

linear regression, and hockey-stick] are similar. The frequency-weighted model analysis

suggests that the skin cancer risk is non-linear with respect to the arsenic exposure level

(i.e., observed number of cases with exposure < 150 ppb was significantly fewer than

predicted by the linear model). This observation is consistent with the thresholds

indicated by the least squares linear models and the hockey-stick models. The least

squares linear model and the hockey-stick model are both maximum likelihood estimate

models. The simple linear model is an analysis of the set of prevalence points, without

consideration of the sample size of each prevalence point. In addition, it incorporates the

somewhat unrealistic possibilities of P < 0 or > 1 at exposure extremes. The hockey-stick


3/14/2007 Page 28

model has the advantage over the simple linear model in that it is sensitive to both the

numerator and the denominator of each prevalence point and thus considers the weight of

evidence at each point. Thus, the hockey-stick model is more likely to approximate the

central tendency of the underlying data.

8.2. Study site comparisons (Taiwan and Inner Mongolia)

The analytic results with this dataset from Inner Mongolia in the 1990s are

remarkably similar to the ecological studies from Southwest Taiwan in the 1960s, with

respect to hyperkeratoses, dyspigmentation, and skin cancer; however, the study from

Taiwan reported Blackfoot disease, a condition not seen in the Inner Mongolia study

population. Both are studies of Han peoples. Tseng et al. published their skin cancer

prevalence data with well-water arsenic exposures in 1968. Byrd and coworkers

analyzed those data (Table 8.2). The weighted means of the exposure intervals produced

an x-intercept of 118 ppb by the simple linear model (Figure 8) [or 119 ppb by the

hockey stick model] (Byrd et al. 1996).

A risk assessment prepared by the U.S. Environmental Protection Agency of skin

cancer prevalence, based on data from Taiwan, used a non-threshold generalized

multistage model that did not permit examination for a threshold (i.e., is linear to low

doses) (US EPA 1988). The exposure groups for the Taiwan analysis and the Inner

Mongolia analysis differ but their findings can be compared. Both for Taiwan at

exposures < 300 ppb and for Inner Mongolia at exposures < 500 ppb, the skin cancer

prevalence is 0.2 %. The skin prevalence rate in the two highest arsenic concentration


3/14/2007 Page 29

exposure groups in the Inner Mongolian data (150 +) is 1.75 %, and the skin cancer

prevalence rate in the highest two arsenic concentration exposure groups in the

Taiwanese data (300 +) is 1.79 %. Thus, the exposure-related skin cancer prevalences in

Inner Mongolia and Taiwan are roughly similar.

Insert Table 8.2

With similar skin cancer prevalences in the two data sets, one might predict

similar Blackfoot disease (BFD) prevalences. The SW Taiwan dataset had a BFD

prevalence of 0.9%. If the same rate were applied to the 3,179 subjects in the Inner

Mongolia study, twenty-eight cases of BFD would be predicted. However, none were

observed. The absence of BFD cases in the Inner Mongolia study population is striking

and raises again the question as the role of arsenic exposure in the etiology of BFD.

8.3.Age and Time Considerations

The mean age of the subjects seems to be higher for those with greater HAC

levels. This has been adjusted for in the age-adjusted analyses shown in section 7 and are

presented here without age-adjustment. How strongly an age trend is seen depends on

how the exposure groups are grouped. The mean age for those with HAC < 30 is 27.2

years, for those with HAC between 30 and 60 is 31.8 years, for those with HAC between

60 and 150 is 35.9, and for those with HAC of 150 ppb or greater is 35.8 years.

There appears to be no relationship between the level of the highest exposure and

the duration of time between the initiation of the highest exposure and the date of

examination (duration from Highest).


3/14/2007 Page 30

Insert Table 8.3

The associations between time variables, exposure, and clinical outcome

prevalences are shown similarly in the CAE analysis as in the HAC analysis. However,

here mean age, mean years of exposure increase, and cumulative dose increase as the

mean CAE level increases, which is expected. Sensitivity to arsenic and latency may

also change. The clinical prevalences also do not differ from the predicted or expected.

Insert Table 8.4

8.4. Latency Considerations

Much of the information about latency is suggested from the preliminary review

of the data. Each exposure group in the highest arsenic concentration (HAC) analysis has

had an average of 15 to 22 years since the initiation of their highest exposure (Table 8.3).

Thus, it is quite likely that a “sufficient” latency period exists within this data set for

clinical conditions attributable to those highest exposures to be observed. Six of the eight

cases of skin cancer had their highest exposure more than forty years prior to the

dermatological examination.

Similarly, the cumulative arsenic exposure (CAE) analysis (Table 8.5) reveals

that for those with 100 ppb-year or greater cumulative arsenic exposures, the mean

duration of observation increases from 15 years to over 45 years. This period of

observation provides the opportunity for considerable latency consideration for each of

the clinical conditions observed. The group of subjects with cumulative arsenic

exposures of less than 100 ppb-years may not have had sufficient duration of observation


3/14/2007 Page 31

for certain of the clinical conditions to be attributable; however, they have shown little

evidence of disease and do not represent a large proportion (< 10 %) of the study

population.

Previously, a case-control analysis of the data from Tie Men Geng and Zhi Ji

Liang showed that the chronic arsenicism cases were statistically significantly more

likely to have had at least ten years of exposure (Odds Ratio = 4.0; 95 % confidence

limits of 1.6 and 9.9) and to have a highest exposure of greater than 200 ppb (Odds Ratio

= 13.3, 95 % confidence limits of 5.4-32.8) (Byrd et al. 1996). Cases and controls met

the same minimum exposure criteria and were matched for gender, age, occupation,

education, and living and working conditions.

A formal latency analysis of these data has been undertaken in which the

maximum likelihood hockey-stick model was examined using latencies of 0, 10, and 25

years. In these analyses, only the exposure occurring more than 0, 10, or 25 years before

the time of observation were taken into consideration. The HAC and CAE data were

separately analyzed for each of the clinical conditions. Each data set was fit to an α, β

parameter model with a determination of Χ2 and p-value. The threshold (dt) for each

good fit was identified. The 95% confidence limits of the threshold in the latency

analyses were calculated in the following way: while all other parameters were fixed, the

fitted threshold value was increased and decreased until the Χ2 changed by 2 units,

corresponding to 2 standard deviations of the parameter, or 95% confidence intervals. If

zero was excluded from the 95% confidence limit, the fitted threshold was considered to

be statistically significant. Table 8.5 presents the statistically significant fitted thresholds

from models with good fit to the data (p > 0.05) for each clinical condition and using


3/14/2007 Page 32

either the HAC or CAE level data. Where two fitting thresholds were found in a given

model, both values were presented in Table 8.5 as were their mean (arithmetic mean for

HAC; geometric mean for CAE).

Insert Table 8.5

Significant threshold values for good fitting models were found for all conditions

under zero latency conditions and for skin cancer only under either 10 year or 25 year

latency conditions for the HAC and the CAE analyses. For non-malignant arsenic skin

disorders, significant threshold values were found only for the zero latency models. For

non-malignant arsenic skin disorders using the HAC exposures, the threshold values were

between 29 and 50 ppb with a mean at 42 ppb. For non-malignant arsenic skin disorders

using the CAE, the threshold values ranged between 135 and 440 ppb-years with a mean

of 333 or 465 ppb-years.

Only skin cancers showed significant thresholds in good fitting models under 10-

or 25-year latency conditions in either the HAC or CAE analyses. For skin cancer using

the HAC exposures, the threshold values ranged between 167 and 312 ppb for the 10-

and the 25-year latency conditions with a mean of 236 ppb. For skin cancer using the

CAE, the threshold values were 5771 ppb-years under 10 year latency analysis and 1277-

2800 (geometric mean = 1890) ppb-years under 25 year latency conditions.

The lowest significant threshold in a good fitting model was found for skin cancer

in the HAC exposure analysis at 122 ppb under the zero latency condition. In the zero-

year latency analysis of the HAC exposure data, only the 122 ppb threshold for skin

cancer came from a model with an excellent fit (goodness-of-fit p > 0.90). The p-value

for the goodness-of-fit of the combined clinical finding of hyperkeratoses with


3/14/2007 Page 33

dyspigmentation was 0.33. The p-values for the models from which the remaining

thresholds in Table 8.5 for zero-year latency analysis of the HAC exposure data were

derived were in the 0.05-0.15 range. The p-values of the HAC exposure models with 10-

year latency were 0.57 for the 168 ppb threshold and 0.15 for the 299 ppb threshold. The

p-values of the HAC exposure models with 25-year latency were 0.16 for the 167 ppb

threshold and 0.08 for the 312 ppb threshold.

In the zero-year latency analysis of the CAE data, all the thresholds shown in

Table 8.5 came from models with an excellent fit (goodness-of-fit p > 0.90). In the ten-

year latency analysis and the 25-year latency analysis of the CAE data, the statistically

significant thresholds all came from models with goodness-of-fit p-values in the 0.45-

0.75 range.

The latency analyses of the HAC exposure data for the non-malignant arsenical

skin findings show a statistically significant threshold in the zero-year latency analysis

and not in the ten-year and 25-year latency analyses. The HAC exposure data analysis

for the malignant arsenical skin finding (skin cancer) also show a statistically significant

threshold in the zero-year latency analysis with an excellently fitting model that has a

threshold of 122 ppb; however, in the 10 and 25 year latency analyses they show

statistically significant threshold values with good-fitting models (lower p-values) in the

150-300 ppb range.

The latency analyses of the CAE data for the non-malignant arsenical skin

findings also show statistically significant thresholds in the zero-year latency analysis but

not in the ten-year and 25-year latency analyses. The identified thresholds for zero

latency are all at less than 500 ppb-years and come from models with excellent fits, while


3/14/2007 Page 34

those for 10 and 25 year latencies have non-significant thresholds and non-fitting models

(p ~ 0.03-0.05). This suggests that the latency for the non-malignant skin findings is

likely to be less than ten years. Thresholds for skin cancer using the CAE continue to be

identified only in the ten-year and 25-year latency analyses, suggesting a latency 10 years

or greater. After all, six of the eight skin cancer cases did have exposure histories that

exceeded forty years.

8.5. Exposure misclassification considerations

Individual well use histories have been obtained for each of the participants.

Most of the wells were still in use at the time of the study, though some (n = 3)

abandoned wells could not be sampled. Exposures preceding the beginning of the well-

use histories may have led to an underascertainment of exposure. Such misclassification

(underascertainment) of exposures would suppress the appearance of a threshold, as more

cases would be classified below the apparent threshold than actually occurred. Thus,

misclassification of exposures or of their associated skin lesions would spuriously

decrease (not increase) the evidence for a threshold.

8.6. Relationships between clinical conditions

The Inner Mongolian data demonstrate that both hyperkeratoses and

dyspigmentation are observed at lower arsenic exposure levels than skin cancer. The

Inner Mongolia study found eight cases of skin cancer among 3,179 arsenic-exposed well


3/14/2007 Page 35

users. All eight skin cancer cases had both hyperkeratoses and dyspigmentation, and no

cases were observed among the 3,085 subjects who did not have both hyperkeratoses and

dyspigmentation. Further, only eight of the 94 subjects with both hyperkeratoses and

dyspigmentation developed skin cancer. These observations indicate that hyperkeratoses

and dyspigmentation are not sufficient pre-conditions for arsenic-induced skin cancer but

while they suggest here that they may be necessary pre-conditions, the SW Taiwan data

(Tseng et al. 1968) reported that one-third of the skin cancer cases occurred in persons

without hyperkeratoses and dyspigmentation. Both the Inner Mongolia and the Xinjiang

studies have demonstrated a greater prevalence of hyperkeratoses than of

dyspigmentation, while the studies from SW Taiwan and West Bengal have demonstrated

a greater prevalence of dyspigmentation than of hyperkeratoses (Tseng et al. 1968; Guha

Mazumder et al. 1988). All studies have shown a frequent co-prevalence of both

hyperkeratoses and dyspigmentation, and all studies show a much higher prevalence of

hyperkeratoses and dyspigmentation than of skin cancer.

8.7. Interpretation

The exposure-response curves in these analyses reveal that the prevalences of

hyperkeratoses, dyspigmentation, and skin cancers strongly depend on the level of

arsenic exposure. A strong increase of response with increase in exposure exists

whether the highest arsenic concentration (HAC) or the cumulative arsenic exposure

(CAE) are used as the exposure measure. The exposure-response relationship is apparent

for all four of the skin disorders examined, though the pattern of the dose-response


3/14/2007 Page 36

relationship tends to vary among the different skin disorders. A threshold below which

no arsenic skin lesions are observed seems likely but not certain. A sharp increase of

lesions above the threshold, as is a classic behavior for acute toxicity, is not apparent.

Instead there is a slow and steady rise of prevalence with increased exposure.

With respect to the highest arsenic concentrations (HAC) in the drinking water,

the prevalences for both the non-malignant disorders and the skin cancers appear to

follow a threshold model. The data for the non-malignant lesions seem to show a

threshold in the vicinity of 40-70 ppb. The data for the malignant skin lesions seem to

show a threshold at 120-150 ppb with an absence of events at lower concentrations. The

threshold for malignant lesions is higher than that for non-malignant lesions but, based on

few cases, needs independent confirmation.

With respect to the cumulative arsenic exposure (CAE) in the drinking water, the

tendency of the skin cancer prevalence to follow a threshold model is seen only in the

latency models. The evidence for a threshold effect on non-malignant skin disorders is

present only in the zero latency model and appears to be at a lower threshold level than

that suggested for skin cancer. The application of a hockey stick function, adjusted to

limit the prevalence to 100%, fit well to all the lesions. Although a threshold was

suggested for all lesions, this was statistically significant only for hyperkeratoses (about

240 ppb-yrs) and for hyperkeratoses plus dyspigmentation.

Analyses of time considerations suggest that arsenic exposure level, but not age,

exposure duration, or time since exposure, are related to the prevalence of the skin

disorders in the HAC analysis. Furthermore, sufficient latency has occurred between

time of exposure and time of observation for the clinical outcomes to be assessed as


3/14/2007 Page 37

attributable to the exposures. The latency analyses indicate that the latencies for the non-

malignant skin effects may be less than 10 years and that the latencies for skin cancer

may extend into the 10-25 year range.

9. RELATIONSHIP BETWEEN THE TWO MEASURES OF EXPOSURE:

The HAC analysis predicts that the study population is made up of two sub-

groups, the 458 residents with exposures of > 150 ppb arsenic and the 2,721 residents

without such exposures. It may be that the cumulative arsenic exposure analysis really

reflects the exposure histories of the first group

Table 9.1 presents the CAE group distribution of the 458 residents who had used

the > 150 ppb wells under two extreme assumptions. The first is that the risk in each

exposure group is proportional to the number of persons in the exposure group (i.e.,

independent of the cumulative dose), and the second is that the risk in each exposure

group is proportional to the ppb-years in the exposure groups (i.e., dependent upon the

cumulative dose). Neither the distribution for the independent model nor the dependent

model is significantly different from the observed data (Table 9.1; Figure 8); however,

the independent model appears to quite closely approximate the observations (Chi-square

of 1.0 vs. a critical level of 7.8 for dF=3).

Insert Table 9.1

Nonetheless, they do suggest (1) that a larger study may be able to distinguish

between the dependent and independent models and (2) that the appropriate area for

study of skin cancer risk with arsenic ingestion may be in the moderate to high arsenic


3/14/2007 Page 38

exposure (100 ppb-1000 ppb) rather than either in the low exposure range of < 50 ppb or

the very high exposure range of > 1000 ppb.

10. SUMMARY:

This report presents an analysis of the exposure-response data for skin cancer and

other dermatological effects of ingesting arsenic-contaminated well waters by residents

of three villages in Huhhot, Inner Mongolia, China. Each subject was examined by

physicians from the local health department. Two independent groups of physicians

recorded the dermatological findings of hyperkeratoses, dyspigmentation, and/or skin

cancer. Local Chinese authorities obtained the well-use histories for these subjects and

measured the arsenic levels in the wells.

The mean arsenic level was used to represent the arsenic level of wells that had

two measurements, and the geometric mean was used for the one well that had three

measurements. Based on the well-use histories and laboratory values of the arsenic

content of the well waters, two measures of arsenic exposure were developed for each

subject. The first measure was the highest or highest arsenic concentration (HAC) for the

individual, based on their well-use history. The second measure was the cumulative

arsenic exposure (CAE) that was the summation of the exposures and durations for each

well used, summarized as ppb-years. Exposure group were developed, and the

prevalences of dermatological findings (particularly skin cancer) across the exposure

groups were examined.


3/14/2007 Page 39

Four measures of dermatological disorder were analyzed – (1) hyperkeratoses, (2)

dyspigmentation, (3) hyperkeratoses with dyspigmentation, and (4) skin cancer.

Analyses of the distribution of the prevalence of specific dermatological disorders across

exposure groups were conducted. Analyses were conducted using a frequency-weighted

model as well as both a simple linear model and a hockey-stick model and later with a

formal latency analysis.

Eight skin cancer cases were identified, as were 172 cases of hyperkeratoses, 121

cases of dyspigmentation, and 94 cases with both hyperkeratoses and dyspigmentation.

All eight skin cancer cases occurred in individuals with both hyperkeratoses and

dyspigmentation and with highest arsenic concentrations of 150 ppb or greater. Although

cases of hyperkeratoses and of dyspigmentation occurred with highest arsenic

concentrations at less than 50 ppb, they did not reach expected prevalences until higher

highest arsenic concentrations.

The dose-response curve for skin cancer is described with respect to the highest

arsenic concentration (HAC) by a frequency-weighted model with a threshold at or near

150 ppb arsenic or by a most likely estimate hockey-stick model with a threshold at 122

ppb arsenic. These results are consistent with the threshold-model analysis of the Taiwan

data set that had showed a threshold at about 120 ppb. Analysis with respect to the

cumulative arsenic exposure (CAE) is consistent with the analysis of the highest arsenic

concentration, but less clear. Potential sensitivity, cumulative dose, duration of exposure

differences within the population may confound the data.

No skin cancer was observed among those whose highest arsenic concentration

was less than 150 ppb or whose cumulative arsenic exposure was less than 1000 ppb-


3/14/2007 Page 40

years. Issues of time consideration, latency, and misclassification have been considered,

but do not at present appear to have markedly affected the analysis. Different approaches

have been used to deal with confounding due to age, including the use of age-adjusted

rates and of stratified analyses.

Additional analyses could be considered, but the power of this study to further

describe the dose-response relationship between arsenic ingestion and skin cancer is

limited by the identification of only eight cases of skin cancer in this population at the

time of their examination. Observations made from the analyses of these data should be

used in the design of further studies. These observations should guide the selection of the

study population by exposure history. Subsequent study of this population ten years after

the initial study, or extension of this study to a larger Inner Mongolian population, should

be considered. The design of subsequent studies should be dependent upon the questions

whose answers are sought.

The evidence presented here of a threshold arsenic exposure level with respect to

drinking water arsenic concentration for skin cancer is consistent with the analysis of

southwest Taiwan data on skin cancer prevalence (Byrd et al., 1996) and on bladder and

lung cancer mortality (Lamm et al., 2006), as well as of the all Taiwan studies on skin

cancer incidence (Guo et al., 1998), bladder cancer mortality (Guo and Tseng, 2000) and

lung cancer mortality (Guo, 2004). Such evidences should be taken into consideration in

attempting to establish safe drinking water standards for arsenic exposure.


3/14/2007 Page 41

Acknowledgements:

This analysis was funded in part by a grant [# H75/ATH682885] to the

University of Texas-Houston Medical School (Department of Dermatology) from the

Agency for Toxic Substance and Disease Registry [ATSDR]. We thank the colleagues of

the Huhhot Center for Disease Control and Prevention, Inner Mongolia, China [formerly,

the Huhhot Sanitation and Anti-Epidemic Station] for their diligence and maintenance of

the study and their follow-through on the care of the patients. We thank the residents of

the three villages for providing the information upon which this study is based and the

acceptance of the investigators. We thank Katharine Shelley for assistance in

development of this manuscript. This paper has been presented in part at the American

Association for Cancer Research meeting (2006) section on chemical carcinogenesis.

We wish particularly to thank Sharon S. Campolucci, project director of the ASTDR

grant, whose personal encouragement, interest, and support has been greatly appreciated.

The findings and conclusions in this report are those of the author(s) and do not

necessarily represent the views of the Agency for Toxic Substances and Disease

Registry.


3/14/2007 Page 42

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Figure Legends:

Figure 1 Scatter of Paired Water Samples measured in ppb (ug/L) Arsenic.

Figure 2 Comparison of Arithmetic Mean, Geometric Mean, and Median of Highest

Arsenic Concentration by Highest Arsenic Concentration Groups.

Figure 3 Comparison of Arithmetic Mean, Geometric Mean, and Median of Highest

Arsenic Concentration by Cumulative Arsenic Exposure Groups.

Figure 4 Hockey-Stick Analysis of Skin Cancer Prevalence (%) by Highest Arsenic

Concentration Group (ppb).

Figure 5 Hockey-Stick Analysis of Skin Cancer Prevalence (%) by Cumulative Arsenic

Exposure Group (ppb-yrs).

Figure 6 Observed and Predicted Cumulative Skin Cancer Case Count by Highest

Arsenic Concentration (Frequency-Weighted Method).

Figure 7 Skin Cancer Prevalence by Weighted Mean Arsenic Level (Tseng, 1968).


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Figure 8 Observed and Expected Numbers of Skin Cancers for Those Exposed at 150

ppb or more, assuming that the Risk is either Dependent or Independent of the

Cumulative Arsenic Exposure.

HERA 2007-01-03 Tables.doc

Table 1. Demographic distribution among the three selected villages # of Subj. Age (SD) Gender Race Village (N) (years) M (%) F (%) Han (%) Mongolian (%) Hei He 1755 36 (19) 901 (51) 854 (49) 1748 (99.6) 7 (0.4) Tie Men Geng 257 27 (19) 128 (50) 129 (50) 257 (100) 0 (0.0) Zhi Ji Liang 1167 30 (19) 599 (51) 568 (49) 1167 (100) 0 (0.0) Three Villages 3179 33 (20) 1628 (51) 1551 (49) 3172 (99.8) 7 (0.2) Missing data* 49 7 (10) 22 (45) 27 (55) 49 (100.0) 0 (0.0) Total participants 3228 32 (20) 1650 (51) 1578 (49) 3221 (99.8) 7 (0.2) * Well-use data missing for 45 and dermatological findings missing for 4 participants. Table 2. Distribution of the study population by age group and village

Age (yrs) Hei He Tie Men Geng Zhi Ji Liang Total n % N % n % n %


Table 3.1. Frequency distribution of wells and descriptive statistics of As concentration Frq. by sample # Frq. by As concentration groups N (%) As concentration statistics (ppb)* Village One Two Three


Table 3.3. Descriptive statistics for the eight HAC groups (ppb)

HAC (ppb) N Cum % A-mean A-std G-M G-std Min P25 Med P75 Max


Table 3.4. Descriptive statistics for the eight CAE groups (ppb-year)

CAE N Cum % A-mean A-SD G-MN

G-SD Min P25 Med P75 Max


Table 4.1 Prevalences of Skin Disorders in the studied subjects and prevalence of skin cancer in each skin disorder group. All Subjects Skin Cancer Cases

Skin Disorder N %* N %** Total population 3179 100.0% 8 0.25% No arsenic dermatosis 2980 93.7% 0 0.0% Any arsenic dermatosis 199 6.3% 8 4.0% Hyperkeratoses (K) 172 5.4% 8 4.7% Dyspigmentation (P) 121 3.8% 8 6.6% Both (K) and (P) 94 3.0% 8 8.5% Skin cancer 8 0.3% 8 100.0%

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Table 5.1. Relationship between highest arsenic concentration and the four skin disorders

Keratoses (a) Dyspigmentation (b) Kera+Dysp(c) Skin Cancer (d) HAC Mean

HAC Subj Exposure Observed Predicted Observed Predicted Observed Predicted Observed Predicted

Group (ppb) N (ppb-person) n Prev n Prev n Prev n Prev n Prev

n Prev n Prev n Prev


Table 5.2. The parameters of simple linear modeling for HAC exposures Y-

Intercept Slope+ X-

Intercept Skin Disorders (α) ( β) R 2 F(1,6) p-value (-α / β) Keratoses -0.0034 0.00066 0.995 1300 3.0 E-08 4.9 ppb Dyspigmentation -0.00073 0.00046 0.995 1165 4.2 E-08 1.4 ppb Kera+Dysp -0.0055 0.00041 0.998 2611 3.8 E-09 14 ppb Skin Cancer -0.003 0.00007 0.988 473 6.2 E-07 43 ppb* + Unit risk per ppb. * p


Table 6.1. Relationship between cumulative arsenic exposure and the four skin disorders

Mean Exposure Keratoses (a) Dyspigmentation (b) Kera+Dysp (c) Skin Cancer (d) CAE CAE Subj (ppb-

person-yr Observed Predicted Observed Predicted Observed Predicted Observed Predicted

Group (ppb-yr)

N n Prev n Prev n Prev n Prev n Prev n Prev n Prev N Prev


Table 6.2. The parameters of the simple linear model for CAE Y-Intercept Slope Unit Risk X-Intercept Skin Disorders (α) (β ) R 2 F(1,6) p-value (β ) (-α / β) Keratoses -0.0017 0.000036 0.999 17720 1.20E-11 3.6E-05/ppb-yr 43 ppb-yr Dyspigmentation -0.00065 0.000025 0.999 6767 2.20E-10 2.5E-05/ppb-yr 35 ppb-yr Kera+Dysp -0.00053 0.000019 0.999 4627 6.80E-10 1.9E-05/ppb-yr 25 ppb-yr Skin Cancer -0.00051 0.000002 0.995 1168 4.20E-08 2.1E-06/ppb-yr 313 ppb-yr

Table 6.3. The parameters of the hockey-stick model for CAE Skin Disorders α β Χ2(df=5) GOF

Test p Threshold (dt)

Keratoses 0.0036 0.000043 1.3 0.93 353 ppb-yrs* 0 0.00004 1.4 0.97 135 ppb-yrs* Dyspigmentation 0.0041 0.00003 1.2 0.95 440 ppb-yrs* Kera+Dysp 0.0018 0.000024 1 0.96 406 ppb-yrs* Skin Cancer 0 0.000002 0.2 0.9998 617 ppb-yrs+

* Significantly different from zero at p < 0.05 (two-tail) + Significantly different from zero at p < 0.05 (one-tail)

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Table 7.1 Crude and (age-adjusted) Dermatological Prevalence Rates by HAC Exposure HAC Keratoses Dyspigmentation Kerato/Dyspig Skin Cancer < 10 0.4 (0.4) 1.1 (1.1) 0.4 (0.4) 0.0 (0.0) 10- 0.6 (0.6) 0.5 (0.6) 0.4 (0.4) 0.0 (0.0) 50- 5.7 (5.4) 3.7 (3.5) 3.0 (2.7) 0.0 (0.0)

150- 11 (9.4) 8.2 (7.0) 5.8 (4.8) 1.2 (1.0) 500+ 69 (71.9) 48 (53.7) 43 (48) 7.1 (5.9)

Table 7.2 Crude and (age-adjusted) Dermatological Prevalence Rates by CAE

CAE Keratoses Dyspigmentation Kerato/Dyspig Skin Cancer


Table 8.1. Observed and expected cumulative skin cancer case count by highest arsenic concentration (frequency weighted model)

HAC Group (ppb) Mean HAC (ppb) Cum. Subj (N) Cum. Obs. (N) Cum. Exp. (N) < 10 5 287 0 0.04 10- 15 692 0 0.22 30- 33 1104 0 0.62 50- 55 1620 0 1.46 60- 70 2185 0 2.63 100- 122 2721 0 4.55 150- 175 3137 5 6.7 500+ 1048 3179 8 8

Table 8.2. Frequency and prevalence of skin cancer by weighted mean arsenic concentration intervals (ppb) in the Taiwan study (Tseng et al., 1968) As Concentration (ppb) Skin Cancer Population Prevalence Range Weighted Mean (N) (N) (%) < 300 171 21 9,526 0.2 300-600 473 60 5,413 1.1 > 600 785 185 8,251 2.2 Total 460 266 23,190 1.1

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Table 8.3

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