An Epidemiologic Study of Arsenic-related Skin Disorders...
Transcript of An Epidemiologic Study of Arsenic-related Skin Disorders...
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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
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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
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Running Head: Skin Cancer and Arsenic Dermatosis in Inner Mongolia 17
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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 hypo- pigmentation), 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
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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.
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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
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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
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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
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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
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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
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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
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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
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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).
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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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-
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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.
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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.
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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.
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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 %
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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
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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
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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
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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
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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
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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
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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
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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