Pre-Publication Version 
Abt Associates Inc. September 22, 2014 ▌pg. i

Preamble

This document represents the Abt Associates’ final report for the project “Activities to Protect 

Populations and Communities Served by Unregulated Drinking Water Systems from Non-Infectious 

Contaminants: Disease and Economic Burden of Exposure to Arsenic in Drinking Water” (Contract 

No. 200-2013-57290) for CDC’s National Center for Environmental Health (NCEH)’s Health Studies 

Branch. To facilitate publication in the peer-reviewed literature, the final report is structured as a 

manuscript (e.g., abstract, introduction, approach, data inputs, results, discussion). To this end, we 

have summarized our work to present our final approach and results. Additional documentation 

describing our efforts on this project is available from our monthly progress reports, provided 

separately as Appendix A. Additionally, a presentation made at the International Society for 

Environmental Epidemiology (ISEE) meeting is included in Appendix B. While there is additional 

work required before submitting the manuscript to a journal, appropriate target journals include Risk 

Analysis and the Journal for Exposure Science and Environmental Epidemiology. We look forward to 

working with CDC on the manuscript to ensure this work reaches a broader audience. 

In order to aid the reader’s review, we have included two additional items in the manuscript that will 

be removed when it is submitted to a journal. First, we have written a targeted executive summary 

that CDC might find useful in describing the main purpose and results of the study. Second, we have 

provided some additional guidance text in the manuscript to help the reader navigate the format.

Pre-Publication Version 
Abt Associates Inc. September 22, 2014 ▌pg. ii

Table of Contents

Contents 

Abbreviations and Acronyms ............................................................................................................. iii

Executive Summary ............................................................................................................................ iv

Abstract ................................................................................................................................................ ix

1. Introduction .......................................................................................................................... 1-1

2. Approach ............................................................................................................................... 2-1

2.1 Private Wells................................................................................................................. 2-2

2.1.1 Arsenic Levels in Private Wells ...................................................................... 2-2

2.1.2 Number Served by Private Wells by County ................................................... 2-5

2.2 Health Effects ............................................................................................................... 2-6

2.2.1 Cancer Endpoints ............................................................................................. 2-7

2.2.2 Non-Cancer Endpoint ...................................................................................... 2-9

2.3 Economic Valuation ................................................................................................... 2-11

2.3.1 Valuation of Fatal Illnesses ........................................................................... 2-11

2.3.2 Valuation of Non-Fatal Cancer Cases ........................................................... 2-12

2.4 SafeWater CBX Model ............................................................................................... 2-14

3. Data Inputs ............................................................................................................................ 3-1

3.1 Exposure Inputs ............................................................................................................ 3-1

3.2 Dose-Response Inputs .................................................................................................. 3-6

3.3 Economic Inputs ........................................................................................................... 3-6

3.3.1 VSL.................................................................................................................. 3-6

3.3.2 COI .................................................................................................................. 3-7

3.4 Variability and Uncertainty .......................................................................................... 3-8

4. Results ................................................................................................................................... 4-1

5. Discussion .............................................................................................................................. 5-1

5.1 Contextualizing Our Results ......................................................................................... 5-1

5.2 Main Assumptions Underlying Our Results ................................................................. 5-3

5.3 Limitations/Areas for Refinement ................................................................................ 5-6

5.4 Conclusions for Stakeholders ....................................................................................... 5-7

6. References ............................................................................................................................ R-1

Pre-Publication Version 
Acronyms 

Abt Associates Inc. September 22, 2014 ▌pg. iii

Abbreviations and Acronyms

CDC Centers for Disease Control and Prevention

CI Confidence Interval

COI Cost-of-illness

DWUR Drinking water unit risk

EPA Environmental Protection Agency

GWA Ground Water Atlas

HEALS Health Effects of Arsenic Longitudinal Study

HR Hazard Ratio

IRIS Integrated Risk Information System

ISEE International Society for Environmental Epidemiology

MAP Maximum a posteriori probability

MCL Maximum contaminant level

MCMC Markov Chain Monte Carlo

MRL Minimum reporting level

NAS National Academy of Sciences

NAWQA National Water-Quality Assessment

NCEH National Center for Environmental Health

NRC National Research Council

NWIS National Water Information System

OSF Oral slope factor

SEER Surveillance, Epidemiology, and End Results

USGS United States Geological Survey

VSL Value per Statistical Life

WTP Willingness-to-pay

Pre-Publication Version 
Executive Summary 

Abt Associates Inc. September 22, 2014 ▌pg. iv

Executive Summary

Background 

Arsenic is a naturally occuring metal in the earth’s crust with known adverse effects in humans. It can 

be present in groundwater, which approximately 15% of Americans (40 million people) use as a 

drinking water source from private wells. These private wells are not protected by U.S. government 

regulations for contaminants in drinking water, and approximately 7% of arsenic samples from 

private wells exceeded the U.S. Environmental Protection Agency’s (EPA) primary drinking water 

standard of 10 μg/L. The magnitude of the problem of arsenic exposure from private wells in the 

United States is unknown. It is up to well owners to test and potentially treat their drinking water or 

pursue other options (such as bottled water or switching to community water supplies, which are 

required to meet the EPA standard). Costs of reverse osmosis treatment systems, which are effective 

at removing arsenic, range from $300 to treat water at the tap to $9,000 to treat all water entering the 

household. 

Objective 

We conducted an analysis to illustrate the potential magnitude of the public health burden associated 

with arsenic in unregulated private drinking water wells in the United States. We estimate the burden 

in terms of disease cases (cancer and non-cancer, fatal and non-fatal) as well as the potential 

economic damages associated with these disease cases. 

Arsenic Exposure 

To capture geographic variability, we developed separate arsenic well concentration distributions for 

each of 12 groundwater regions in the continental United States based on 1992-2004 United States 

Geological Survey (USGS) data. We used a statistical (Bayesian) model to estimate the distributions. 

The model has the advantage of using information from surrounding regions when within-region 

information is sparse, and it can efficiently handle data with concentrations falling below the 

minimum reporting level (MRL), which was the case for over half of the USGS arsenic data. 

Health Effects 

Based on a review and evaluation of the epidemiologic literature, weight of evidence, and potential 

economic impacts, we included the following health endpoints in our analysis: skin cancer, bladder 

cancer, lung cancer, and heart disease mortality. We relied on estimates in the literature linking 

arsenic levels in wells with these adverse health effects in humans (i.e., dose-response estimates) and 

assumed that exposure to even very low levels of arsenic results in adverse health effects (i.e., arsenic 

exhibits “no threshold”).

Economic Inputs 

To enable direct comparison of cancer and non-cancer cases, as well as to compare the disease burden 

of arsenic in wells to potential costs associated with arsenic removal, we computed the economic 

value of eliminating the disease cases attributable to arsenic exposure. We developed cost-of-illness 

(COI) estimates for non-fatal cases of (1) non-invasive and invasive bladder cancer, (2) Stage I and 

Stage II lung cancer, and (3) basal cell and squamous cell (non-invasive and invasive) skin cancer. 

For each cancer sub-type, we developed typical medical treatment profiles and then calculated cost 

estimates comprised of direct medical costs and opportunity costs. To estimate the economic value of 

Pre-Publication Version 
Executive Summary 

Abt Associates Inc. September 22, 2014 ▌pg. v

eliminating the excess risk of fatal cancer and heart disease mortality, we applied EPA’s Value per 

Statistical Life (VSL). 

Approach 

We developed the three input streams (exposure from private wells, health effects, and economic 

valuation) and then conducted our analysis using the SafeWater CBX model. SafeWater CBX is a 

state-of-the-art economic analysis model that Abt Associates developed for the EPA Office of Ground 

Water and Drinking Water to estimate the costs and benefits of new or revised drinking water 

regulations. The model can account for variability and uncertainty in the exposure, health effect, and 

economic valuation inputs. We selected a model year of 2010 (the year to which economic estimates 

were scaled and the year from which all inputs were drawn or to which they are assumed to 

correspond).

Results 

Across the United States, we estimated 500 deaths from heart disease and 1,074 cases of cancer (511 

lung, 480 bladder, and 83 skin cancer cases) attributable to arsenic in private wells (see Figure ES-1). 

We estimated the total annual damages of arsenic in wells to be $9.4 billion dollars, approximately 

half attributed to cancer and half to non-cancer effects (i.e., heart disease mortality). Within the 

cancer cases, while the number of lung cancer cases was nearly the same as the number of bladder 

cancer cases, the higher fatality rate of lung cancer combined with the much larger economic value 

associated with the fatal cases relative to the non-fatal cases caused lung cancer to have larger 

economic damages. The skin cancer damage estimates were the smallest because there were only a 

few excess cases of the skin cancer (basal and squamous cell) and this type of cancer is usually nonfatal. 

Pre-Publication Version 
Executive Summary 

Abt Associates Inc. September 22, 2014 ▌pg. vi

Figure ES-1: Mean National Estimates of Arsenic Burden in Private Wells in the United States 

in Terms of Cases and Economic Damages 

There are regional differences in the distribution of cases across the United States, which are a 

function of the exposed population and county-level variation in heart disease mortality rates, but are 

mainly associated with the exposure distribution. The highest number of cases occurs in regions with 

larger exposure distributions (e.g., USGS Ground Water Atlas Regions B, J, K, L, and M). Figures 

ES-2 and ES-3 outline the cases and economic damages, respectively, for each region. 

$4,729

$3,918

$784

$0.47

0

100

200

300

400

500

600

 $-

 $500

 $1,000

 $1,500

 $2,000

 $2,500

 $3,000

 $3,500

 $4,000

 $4,500

 $5,000

Heart

Disease

Lung

Cancer

Bladder

Cancer

Skin Cancer

Cases

Damages (millions of $)

Health Endpoints

Fatal Cases

Non-Fatal Cases

Damages (millions of $)

Pre-Publication Version 
Executive Summary 

Abt Associates Inc. September 22, 2014 ▌pg. vii

Figure ES-2: Mean Regional Estimates of Arsenic Burden (Total Cases of Lung, Bladder, and 

Skin Cancer and Heart Disease Mortality) from Private Wells 

The 12 regions (B through M) in the map denote USGS Ground Water Atlas regions in the contiguous United States. They 

are used as the geographic unit of the concentration distributions and the reported regional estimates. 

Pre-Publication Version 
Executive Summary 

Abt Associates Inc. September 22, 2014 ▌pg. viii

Figure ES-3: Mean Regional Estimates of Arsenic Burden (Total Economic Damages and 

Proportional Case Type) from Private Wells 

The 12 regions (B through M) in the map denote USGS Ground Water Atlas regions in the contiguous United States. They 

are used as the geographic unit of the concentration distributions and the reported regional estimates. 

Conclusion 

In this illustration of the potential burden of disease attributable to arsenic contamination of private 

wells in the United States, we found 500 excess cases of heart disease mortality and 1,074 excess 

cases of three cancers (lung, bladder, and skin) with economic damages of over $9 billion. While 

there are many assumptions embedded in these results, they suggest that the issue of arsenic 

contamination of private wells in the United States merits additional attention. Efforts to target areas 

of high contamination must recognize that arsenic well levels vary across and within regions. 

Pre-Publication Version 
Abt Associates Inc. September 22, 2014 ▌pg. ix

Abstract

Roughly 15% of Americans obtain drinking water from private wells, which are not required to meet 

U.S. government regulations for contaminants. Arsenic levels exceeding the U.S. primary drinking 

water standard of 10 μg/L have been documented in such wells. We conducted an analysis to 

illustrate the magnitude of the disease burden associated with arsenic in unregulated drinking water 

wells in the United States. We estimated potential arsenic exposure by conducting a Bayesian analysis 

of concentrations in unregulated wells using available United States Geological Survey data. For the 

dose-response assessment, we focused on skin, bladder, and lung cancers, and heart disease mortality 

associated with arsenic exposure. These health effects were selected on the basis of weight of 

evidence, available information, and potential economic impacts. To estimate the magnitude of public 

health damages in monetary terms, we used “Value per Statistical Life” estimates for fatal cases and 

cost-of-illness estimates for non-fatal cases of these four health effects. To the extent possible, we 

characterized variability and uncertainty in all inputs to our analysis quantitatively. The variability 

and uncertainty were analysed using two-dimensional Monte Carlo simulation. We estimated that in 

2010, arsenic exposure in private wells could have led to 500 premature deaths from heart disease and 

511 lung, 480 bladder, and 83 skin cancer cases, resulting in total economic damages of over $9 

billion dollars. While there are many assumptions embedded in these results, they suggest that the 

issue of arsenic contamination of private wells in the United States merits additional attention. 

Pre-Publication Version 
Introduction 

Abt Associates Inc. September 22, 2014 ▌pg. 1-1

1. Introduction

Arsenic is a naturally occurring element that can be present in the environment in inorganic (more 

toxic) or organic (less toxic) forms. Exposure to arsenic through drinking water remains a primary 

concern around the world, even though exposure can also occur at certain workplaces or from 

consumption of food (Lim, Vos et al. 2013; Naujokas, Anderson et al. 2013; Oberoi, Barchowsky et 

al. 2014). Exposure to arsenic has been associated with a wide range of health effects in humans, 

including cancers of the skin, lung, liver, bladder, and kidney (EPA 1988; CalEPA 2004; NTP 2011; 

IARC 2012). Other (“non-cancer”) effects include skin lesions, inflammation, neurological 

impairment in children, hypertension, ischemic heart disease, and diabetes (CalEPA 2004; ATSDR 

2007; NTP 2011; WHO 2011; Naujokas, Anderson et al. 2013). 

Exposure to arsenic via groundwater is a concern in many countries around the world including 

Bangladesh, Taiwan, Argentina, Chile, and the United States (Naujokas, Anderson et al. 2013). In the 

United States, the Environmental Protection Agency (EPA) set a maximum contaminant level (MCL) 

standard for arsenic of 10 μg/L that applies to community water systems (i.e., a system serving 15 

locations or 25 residents year-round) as well as non-community water systems (e.g., schools, 

residential facilities, factories) (EPA 2012). However, the standard does not apply to private 

(domestic) wells. Almost 7% of sampled private wells in the United States had arsenic concentrations 

exceeding the MCL (DeSimone, Hamilton et al. 2009) and some individual wells contained arsenic 

concentrations exceeding 500 g/L (Nielsen, Lombard et al. 2010). 

Private well owners, who make up approximately 15% of the U.S. population (DeSimone, Hamilton 

et al. 2009), are responsible for testing and treating their own wells and drinking water. Treatment 

systems like reverse osmosis, activated alumina filtration, ion exchange, and iron treatment can be 

used to effectively remove arsenic from water either at the faucet (point of use) or at the entry to the 

home (point of entry). Costs of reverse osmosis systems can range from $300 for point of use to 

$9,000 for point of entry systems and are borne by the well owners (Schmidt 2014). Other options 

include using bottled water for cooking and drinking or connecting to a community water system. 

There are several challenges in estimating the public health burden of arsenic in domestic wells in the 

United States. One main challenge is the sparse data available on arsenic levels in private wells across 

the country. Levels of arsenic in wells can vary geographically to a great extent and should be 

captured in the burden estimation. Another challenge is that the literature relating arsenic exposure 

and adverse health effects in humans is extensive and evolving. While there are well-established and 

long studied dose-response relationships for some cancers, these relationships are nascent but 

growing for cardiovascular effects and diabetes, requiring thoughtful evaluation and selection of 

potential health endpoints to determine the burden. 

Our objective is to illustrate the magnitude of the public health burden associated with arsenic levels 

in wells using available data. We leveraged existing data to provide estimates of select adverse health 

effects associated with exposure to arsenic in terms of cases as well as dollars. Expressing the health 

effects in terms of dollars enables comparison across different effects, as well as against potential 

mitigation costs. In our analysis, we characterized variability and uncertainty in our inputs to the 

extent possible and conducted two-dimensional Monte Carlo simulations to generate illustrative 

estimates of the public health burden of arsenic in private wells in the United States. 

Pre-Publication Version 
Introduction 

Abt Associates Inc. September 22, 2014 ▌pg. 1-2

Additional details on our efforts on this project are available from our monthly progress reports, 

which are provided separately as Appendix A. Furthermore, a presentation summarizing our approach 

and draft results was made at the International Society for Environmental Epidemiology (ISEE) 

meeting held in Seattle in August 2014 and is included in Appendix B. 

Pre-Publication Version 
Approach 

Abt Associates Inc. September 22, 2014 ▌pg. 2-1

2. Approach

We used the Abt Associates SafeWater CBX model to estimate the disease burden and economic 

damages associated with exposure to arsenic from unregulated private wells. In doing so, we first 

developed three input streams related to (1) private wells, (2) health effects, and (3) economic 

valuation. The model incorporates uncertainty and variability, and reports distributional estimates of 

the fatal and non-fatal cases of adverse health effects as well as the corresponding economic damage 

estimates. Figure 2-1 provides a visual overview of our approach which is described in Section 2, 

while the final data inputs to the SafeWater CBX model are presented in Section 3. 

Figure 2-1: Visual Overview of Approach 

Health Effects · Dose-response relationships · Baseline incidence rates · U.S. demographic information See Sections 2.2 (approach) & 3.2 (inputs)

· Number of fatal and non-fatal cases attributed to arsenic levels in private wells · Damages ($) associated with the cases See Section 4 (results)

Private Wells · Arsenic well water concentrations · Domestic self-supplied population

See Sections 2.1 (approach) & 3.1 (inputs)

Economic Values · Value of fatal cases · Value of non-fatal cases

See Sections 2.3 (approach) and 3.3 (inputs)

SafeWater CBX Model

Disease Cases & Damages

Model integrates the inputs listed above across the U.S. to produce the outputs below

See Sections 2.4 (approach) and 3.4 (variability & uncertainty)

Pre-Publication Version 
Approach 

Abt Associates Inc. September 22, 2014 ▌pg. 2-2

2.1 Private Wells

To estimate the exposure of the U.S. population to arsenic from private wells, we combined 

information on the distribution of arsenic concentrations in private wells with information on the 

number of people who obtain their drinking water from a private well. In doing so, we made several 

assumptions. First, we assumed that people obtaining drinking water from private wells do not treat 

their drinking water. Second, we assumed that each private well serves approximately the same 

number of individuals, so that the distribution of arsenic concentrations over wells is same as the 

distribution of arsenic concentration exposures over individuals in a given area. Third, we assumed 

that each consumer of private well water is continuously exposed to the same level of arsenic that we 

assign from our exposure model. 

2.1.1 Arsenic Levels in Private Wells

We estimated the distribution of arsenic concentration levels in private wells using private/domestic 

well arsenic sampling data for 1992-2004 from the National Water-Quality Assessment (NAWQA) 

Program administered by the USGS (2014). This was the only data source we located with nationwide coverage employing consistent sampling protocols, analytical methods, and quality assurance 

procedures.1 The NAWQA data were collected using random stratified sampling of approximately 30 

wells in each aquifer. These data allowed us to model the entire distribution of arsenic concentrations 

in private wells. 

For the contiguous United States,2the NAWQA data contained information on arsenic concentrations 

for 1,868 samples, of which 54% were non-detects. A non-detect is an observation that indicates the 

arsenic concentration in the water sample is too low to be measured by the analytical method. The 

minimum reporting level (MRL) is the concentration level below which a given analytical method is 

unable to generate a measurement. In the NAWQA dataset, the most common MRL was 1 μg/L. 

Arsenic concentrations vary geographically across the United States, and it is important to capture 

that variability in our exposure model. Since there were insufficient data to support the development 

of county- or state-specific exposure models, we chose to analyze the NAWQA sampling data 

separately for each of 12 USGS Ground Water Atlas (GWA) regions located in the contiguous United 

States. The GWA regions were defined by USGS to keep regional aquifers within a region and to 

keep each region at a reasonable mapping size (Miller 1999). The 12 regions, which are denoted with 

the letters B through M, are shown in Figure 2-2. 

 

1

 The other information sources we evaluated included: the USGS National Water Information System (NWIS) 

data for 1976-2003 (not specific to private well types) and CDC’s Private Well Initiative (of the two suitable 

state-level databases, one was not yet publicly available and the other reflected “hotspot” sampling).

2

 We constrained our exposure model to the contiguous United States because information on individuals who 

derive their drinking water from private wells was available only for the contiguous United States. 

Pre-Publication Version 
Approach 

Abt Associates Inc. September 22, 2014 ▌pg. 2-3

Figure 2-2: USGS Groundwater Atlas Regions in the Contiguous United States 

Source: (Miller 1999)

In Table 2-1 we report basic summary statistics for the NAWQA data in each GWA region and for 

the contiguous United States overall. There was a large share of non-detects in the NAWQA data for 

some GWA regions: 8 of the 12 regions have over 50% non-detects, with a maximum of 90% in 

region G. When a large share of measurements in a data set bears only partial information (i.e., there 

are many non-detects), simple summary statistics (e.g., mean, variance, percentiles) that are typically 

used to characterize distributions of measured values cannot be reliably computed (Helsel 2010). 

Therefore, we employed more advanced statistical modeling to make inferences about the distribution 

of arsenic concentrations in private/domestic wells in the GWA regions. 

Pre-Publication Version 
Approach 

Abt Associates Inc. September 22, 2014 ▌pg. 2-4

Table 2-1: Summary Statistics on NAWQA Private Well Arsenic Concentration Data by GWA 

Region 

GWA 

Region States Number of Samples

Number of 

Samples with 

Detected As

Percent of 

Non-Detects 

(a) (%)

Median(b)

(μg/L)

95th

Percentile(b)

(μg/L)

B California, Nevada 150 122 19 2.6 16

C

Arizona, Colorado, 

New Mexico, Utah 220 146 34 3.0 21

D

Kansas, Missouri, 

Nebraska 246 182 26 3.6 10

E Oklahoma, Texas 203 102 50 <1 12

F

Arkansas, 

Louisiana, 

Mississippi

83 18 78 <1 3.0

G

Alabama, Florida, 

Georgia, South 

Carolina

137 14 90 <1 2.5

H

Idaho, Oregon, 

Washington 147 93 37 1.0 13

I

Montana, North 

Dakota, South 

Dakota, Wyoming

63 26 59 <1 5.1

J

Iowa, Michigan, 

Minnesota, 

Wisconsin

100 22 78 <1 7.7

K

Illinois, Indiana, 

Kentucky, Ohio, 

Tennessee

145 48 67 <1 33

L

Delaware, 

Maryland, New 

Jersey, North 

Carolina, 

Pennsylvania, 

Virginia, West 

Virginia

254 55 78 <1 6.0

M

Connecticut, 

Maine, 

Massachusetts, 

New Hampshire, 

New York, Rhode 

Island, Vermont

120 37 69 <1 17

TOTAL for contiguous U.S. 1868 865 54 <1 13

(a) The MRL was 1 μg/L; (b) Numbers in this column are rounded to 2 significant figures, “<1” indicates that the 

median could not be calculated because the share of non-detects was over 50%.

We followed the statistical modeling approach used by EPA to generate concentration distributions of 

volatile organic compounds to evaluate the impact of a drinking water rule (EPA Office of Water 

2012). Specifically, we assumed that arsenic concentrations follow a lognormal distribution, and then 

Pre-Publication Version 
Approach 

Abt Associates Inc. September 22, 2014 ▌pg. 2-5

estimated GWA region-specific parameters of this distribution using a hierarchical (or multi-level) 

Bayesian model.3 The parameters defining the distributions are a log-mean and a log-standard 

deviation. We accounted for the fact that information on a significant share of arsenic concentrations 

was only partial (i.e., they were known to be between 0 and the MRL of 1 μg/L) by using a leftcensored lognormal distribution.4 We used OpenBUGS5

software to solve our Bayesian model using 

Markov Chain Monte Carlo (MCMC) simulation. 

To characterize variability in arsenic exposure concentrations within each GWA region, we used the 

GWA region-specific estimates of the lognormal distribution parameters. The Bayesian model 

solution is represented by a series of parameter sets: a log-mean and a log-standard deviation for each 

of the 12 GWA regions. The extent to which the values of parameters in one set differ from the values 

of corresponding parameters in another set reflects the estimation uncertainty. We captured this 

uncertainty in our modeling by computing the arsenic burden estimates using 1,000 parameter sets. 

For our central estimate of the arsenic burden, we used the set of parameter values that yielded the 

maximum a posteriori probability (MAP) of observing the sampling data. See additional Bayesian 

modeling details in Appendix C. 

2.1.2 Number Served by Private Wells by County 

To characterize the U.S. population that obtains their drinking water from private wells, we relied on 

the USGS National Water Information System (NWIS). USGS’s National Water Use Information 

Program collects a comprehensive dataset on the nation’s water use on a five-year basis and makes 

much of it available to the public through NWIS. The most recent available data on private well water 

in NWIS was from 2005 (U.S. Geological Survey 2014).6

 We used information from the “Domestic 

Self-Supplied Population, in Thousands” data field, which is available at the county level for all states 

in the contiguous United States. Based on our calculations, this private well population constituted 

roughly 13% of the total contiguous U.S. population in 2010. See Table 2-2 for the well populations 

per GWA region. 

In modeling health endpoints, we also needed to reflect the sex and age composition of the private 

well population. Since this information was not available from our USGS domestic well dataset, we 

assumed that age and sex distribution of the county’s private well water drinkers matches that of the 

 

3

 Hierarchical modeling strengthens estimates in regions with sparse data (e.g., many samples below the MRL) 

by using information from all regions combined. We used a two-level model. The first level drew information 

from sampling data within regions, while the second level drew information from the collection of regional 

log-means. As a result, in regions with abundant data, the arsenic concentration distributions were estimated 

using predominantly the region-specific information. On the other hand, in regions with sparse data, 

estimation of the arsenic concentration distributions blended available region-specific information with 

information on variability in mean concentrations across regions. 

4

 This type of distribution permitted us to combine the information from concentrations above the MRL with 

information on the number of instances where the concentration was below the MRL. 

5

 http://www.openbugs.net/w/FrontPage 

6

 The 2010 NWIS data collection was delayed and the results have not yet been released to the public. 

Pre-Publication Version 
Approach 

Abt Associates Inc. September 22, 2014 ▌pg. 2-6

general population at the national level, based on data from the 2000 Census of Population and 

Housing (U.S. Census Bureau 2001). These estimates are described in Table 2-2. 

Table 2-2: Populations Supplied by Private Wells per Region and Assumed Demographics 

GWA Region

Domestic Self Supplied 

Population

(thousands)a,b

B 2,889

C 962

D 1,312

E 1,644

F 1,306

G 5,188

H 2,035

I 600

J 6,216

K 5,947

L 8,904

M 4,635

TOTAL 41,638

(a) Source for domestic self-supplied population: U.S. Geological Survey (2014) 

(b) We assumed that age and sex distribution of private well water drinkers matches that of the national-level 

U.S. population, based on Census 2000 (U.S. Census Bureau 2001). The U.S. population was 49.0% male in 

2000. The age distribution was: 28.0% under 20 years; 28.0% from 20-39 years; 26.9% from 40-59 years; 13.6% 

from 60-79 years; and 3.5% aged 80 years or over. 

2.2 Health Effects

While exposure to arsenic is associated with myriad cancer and non-cancer health effects, our 

objective was to illustrate the magnitude of this burden by critically reviewing available information. 

To this end, we developed a list of health effects to include in our analysis along with their 

corresponding dose-response relationships in several steps. First, we reviewed nine governmental 

reports that have synthesized the literature on arsenic and health and were published in the last decade 

to establish a preliminary list of health endpoints associated with arsenic exposure (CalEPA 2004; 

IARC 2004; ATSDR 2007; EPA 2010; NTP 2011; WHO 2011; EPA 2012; IARC 2012; NAS 2013). 

Based on this review, we developed a list of approximately one dozen cancer types and one dozen 

non-cancer health effects. Second, we evaluated the weight of evidence for each cancer and noncancer effect using information from the reports, as well as recent review articles (Maull, Ahsan et al. 

2012; Naujokas, Anderson et al. 2013) and meta-analyses (Abhyankar, Jones et al. 2012; Moon, 

Guallar et al. 2012). Third, for the health endpoints with the strongest evidence, we examined whether 

there was available dose-response information in the literature. For cancer endpoints, we relied upon 

U.S. EPA information for skin, lung, and bladder cancer (EPA 1988; EPA 2010). For non-cancer 

endpoints, we searched the literature for cardiovascular outcomes, and selected one study on heart 

disease mortality after applying epidemiological evaluation guidelines from Hertz-Piciotto (1995) and 

Vlaanderen et al. (2008). 

Pre-Publication Version 
Approach 

Abt Associates Inc. September 22, 2014 ▌pg. 2-7

It should be noted that EPA’s Integrated Risk Information System (IRIS), a premier source of doseresponse information in human health risk analysis, is in the process of re-evaluating the evidence 

associated with inorganic arsenic for both cancer and non-cancer endpoints, with a revised assessment 

expected in 2016 (EPA 2014). For our dose-response assessment, we made use of information from a 

guidance document to EPA from the National Research Council (NRC 2013), as well as the existing 

(EPA 1988) and draft (EPA 2010) IRIS assessments. 

2.2.1 Cancer Endpoints 

Exposure to arsenic has been associated with many cancer types, including lung, bladder, skin, 

kidney, liver, and prostate. We reviewed the weight of evidence assessments from several 

governmental reports (WHO 2001; CalEPA 2004; IARC 2004; ATSDR 2007; EPA 2010; NTP 2011; 

WHO 2011; EPA 2012; IARC 2012; NAS 2013) When we compared the weight of evidence across 

these reports, we found the strongest and most consistent evidence was for cancers of the skin, 

bladder, and lung. Furthermore, lung, skin, and bladder cancers were the three cancer types to 

comprise the top tier of NRC (2013) hierarchy of health concerns in its guidance to EPA as it revises 

its IRIS assessment. 

Skin Cancer 

The existing EPA IRIS estimate of carcinogenic risk from oral exposure (summarized as an oral slope 

factor [OSF] or drinking water unit risk [DWUR]) was established from a 1988 document and is 

based on skin cancer. The carcinogenic risk is based on a study conducted in Taiwan of 

approximately 40,000 persons exposed to arsenic in drinking water and 7,500 unexposed controls 

(Tseng, Chu et al. 1968; Tseng 1977; cited in EPA 1988). The central OSF estimates for skin cancer 

are 1 per mg/kg-day for females and 2 per mg/kg-day for males (EPA 1988). We converted this 

central OSF for skin to a DWUR using assumptions of 2 L/day consumption of drinking water and 70 

kg bodyweight. We then converted the lifetime DWUR estimates to an annual basis (DWURannual) 

using sex-specific life expectancy values from CDC 2009 life tables (Hoyert 2012). 

Lung and Bladder Cancer 

In 2010, EPA developed draft slope factor estimates for internal cancers (lung and bladder) based on 

a reanalysis of the same Taiwan dataset as in the skin cancer estimate (Morales, Ryan et al. 2000; 

EPA 2010). The upper bound lung OSF estimates for females and males, respectively, were 6.7 and 

16.6 per mg/kg-d, and the upper bound bladder OSF estimates were 10.5 and 11.2 per mg/kg-day. 

The slope factor is an “upper bound, approximating a 95% confidence limit, on the increased cancer 

risk from a lifetime exposure to an agent... usually expressed in units of proportion (of a population) 

affected per mg/kg-day” (EPA 2014). For our estimates, we derived central estimates of the OSF 

from EPA (2010) and converted them to DWUR using default assumptions of 2 L/day consumption 

of drinking water and 70 kg bodyweight. We then converted these lifetime DWUR estimates to an 

annual basis (DWURannual) using sex-specific life expectancy values from CDC 2009 life tables 

(Hoyert 2012). 

We estimated the type-specific cancer cases from the arsenic exposure estimates, the unit risks, and 

the affected population using the following equation: 

Pre-Publication Version 
Approach 

Abt Associates Inc. September 22, 2014 ▌pg. 2-8

Equation 2-1 

 

where 

 = ; = the well water arsenic concentration drawn 

from the GWA region-specific exposure distribution [μg/L]; = 0 

μg/L for burden analysis and 1 or 10 μg/L for sensitivity analyses 

 = [risk per μg/L]; see cancer- and sex-specific 

DWURannual in Table 3-2; life expectancy is 81.1 for females and 76.3 

years for males (Hoyert 2012) 

 = Domestic self-supplied population; see Table 2-2. 

Cancer Fatality Rates 

There were different fatality rates and disease sub-types associated with lung, bladder, and skin 

cancer. To determine the proportional fatal/non-fatal split for cases of lung and bladder cancer, we 

analyzed data on relative survival rates (by cancer stage) and the proportion of cases at each cancer 

stage (see Table 2-3). We assumed that all cases of cancer stages with a low 5-year survival rate 

(<15%) were fatal in the long term (i.e., Stage IV for bladder cancer, and Stages III and IV for lung 

cancer). We assumed there was no mortality lag (i.e., no time period between elimination of arsenic 

exposure from wells and realization of health benefits). For each other cancer stage, we determined 

the number of non-fatal cases by multiplying the proportion of cases in that stage by the 5-year 

survival rate. The final resulting survival rates are presented in Table 2-3. 

Pre-Publication Version 
Approach 

Abt Associates Inc. September 22, 2014 ▌pg. 2-9

Table 2-3: Relative Survival Rates for Bladder and Non-Small Cell Lung Cancer, by Stage 

Cancer Type

Categories of 

Stages for 

Valuation

Cancer Stage at 

Diagnosis

Proportion of 

Cases a

5-Year Relative 

Survival Rate b

Bladder Cancer

Fatal and non-fatal

(Non-invasive)

0 60.4% 98.4%

I 17.6% 87.7%

Fatal and non-fatal

(Invasive)

II 9.2% 62.6%

III 5.1% 45.5%

Fatal IV 7.7% 14.8%

Non-Small Cell 

Lung Cancer

Fatal and non-fatal I 29.6% 56.9%

Fatal and non-fatal II 6.1% 33.7%

Fatal III 29.7% 9.4%

Fatal IV 34.6% 1.8%

Source: National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) Survival Monograph.

(a) Excluding “unknown” stage. Source: Wisnivesky, et al. (2005), based on the National Cancer Institute’s SEER

Database for years of diagnosis from 1988-2001.

(b) For all types of lung cancer. Non-small cell lung cancer makes up 85% of all lung cancers (Ries and Eisner 

2007).

Non-melanoma skin cancers associated with arsenic (Yeh 1973) include basal cell carcinoma (15% of 

cases), invasive squamous cell carcinoma (19%), non-invasive squamous cell carcinoma (58%), and 

“combined” (8%), which did not denote any specific cancer type.7 We assumed that all cases of nonmelanoma skin cancer were non-fatal since these cases are usually cured before they spread 

(American Cancer Society 2013).8

2.2.2 Non-Cancer Endpoint 

Ischemic heart disease and skin lesions were the only two non-cancer endpoints listed in the hierarchy 

of health endpoints of concern by NRC (2013) in its guidance to EPA as it revises its IRIS assessment 

of arsenic. Since we did not expect skin lesions to figure prominently in the economic damages 

associated with arsenic, we focused on heart disease. NRC (2013) further suggested that 

cardiovascular dose-response analysis is possible at low to moderate levels of exposure and that 

analyses should focus on cardiovascular disease mortality, overall cardiovascular disease, ischemic 

heart disease, and/or myocardial infarction. We searched the peer-reviewed literature for 

epidemiological studies conducted since 2004 for the four endpoints above and evaluated about one 

dozen studies using evaluation guidelines from Hertz-Piciotto (1995) and Vlaanderen et al. (2008). 

 

7 We distributed the “combined” cases into basal cell and squamous cell carcinoma types by normalizing these 

estimates by 0.92. 

8

 For non-melanoma skin cancer, “non-invasive” means that the cancer cells are only found in the epidermis 

(top layer of skin), while “invasive” means that the cancer cells have invaded the dermis (middle layer of 

skin). 

Pre-Publication Version 
Approach 

Abt Associates Inc. September 22, 2014 ▌pg. 2-10

We selected a study by Chen et al. (2011) relating arsenic in drinking water with cardiovascular 

outcomes because of the strength of the study design (e.g., prospective cohort, wide range of 

exposures, validated outcome ascertainment, specificity of outcome) as the basis for our non-cancer 

dose-response assessment. Chen et al. (2011) collected water samples from nearly 6,000 wells in 

Araihazar, Bangladesh as part of the Health Effects of Arsenic Longitudinal Study (HEALS), a large 

prospective cohort study that began in 2000. They also collected cardiovascular mortality information 

for the cohort through a validated verbal autopsy procedure, with 95% specificity and 85% 

sensitivity. They examined the association between baseline well water arsenic levels and deaths from 

all cardiovascular diseases, ischemic heart disease combined with other forms of heart disease, 

ischemic heart disease only, and cerebrovascular disease only. The association between arsenic 

exposure and ischemic heart disease combined with other forms of heart disease, as well as with 

ischemic heart disease alone, was significant in models controlling for sex, age, body mass index, 

smoking status, educational attainment, and changes in arsenic concentration adjusted for urinary 

creatinine. The summary measure for ischemic and other forms of heart disease mortality is a hazard 

ratio (HR) of 1.29 (95% CI: 1.10-1.52) per standard deviation of 115 μg/L. 

To estimate the heart disease mortality associated with arsenic exposure, we used the concept of 

attributable risk, or (HR-1)/HR, applied to the baseline heart disease mortality. The HR is based on a 

standard log-linear model, HR = eβΔX

, where β is the risk coefficient. Thus, the risk coefficient can be 

calculated as ln(HR)/ΔX. In our case, β is 2.21E-3 per μg/L. 

The estimated heart disease deaths reflect the attributable risk (simplified to ), the baseline 

heart disease mortality rate, and the affected population. 

Equation 2-2 

 ( ) 

where 

β = risk coefficient relating ischemic and other forms of heart disease 

mortality and arsenic concentrations in wells; based on HR of 1.29 per 

115 μg/L from Chen et al. 2011 

 = ; = the well water arsenic concentration drawn 

from the GWA region-specific exposure distribution [μg/L]; = 0 

μg/L for burden analysis and 1 or 10 μg/L for sensitivity analyses 

 = Heart disease mortality rate from ICD-10 

codes: I20-I25 (ischaemic heart diseases) and I30-I51 (other forms of 

heart disease); (Centers for Disease Control and Prevention 2014) 

 = Domestic self-supplied population; see Table 2-2. 

Since heart disease mortality rises sharply after age 60, we stratified our analysis, making separate 

estimates in two age groups: 0-59 years and 60+ years. We obtained heart disease mortality rates for 

Pre-Publication Version 
Approach 

Abt Associates Inc. September 22, 2014 ▌pg. 2-11

each of those age groups at the county level across the United States from CDC WONDER (Centers 

for Disease Control and Prevention 2014) and applied them to our private well population.9

Furthermore, we assumed the same fraction of the national U.S. population that was 0-59 years old 

also applied to the domestic self-supplied population and used this parameter to estimate the 

population in each age group (U.S. Census Bureau 2001). 

2.3 Economic Valuation

In our economic valuation approach, we use the terms “damages” or “economic damages” to refer to 

the economic values estimated in this analysis. However, our estimates are not based on any observed 

economic losses from arsenic in private wells. Instead, the measure of interest is the economic value 

that the affected population would assign to reducing arsenic concentrations in private wells to zero. 

Since this economic value is not known, we use proxies, as described in this section. Additional detail 

on the estimates we generated in our approach is available in Appendix D. 

There are different approaches for estimating the economic damages of the health effects, depending 

on whether the arsenic-related illness results in a fatal or non-fatal endpoint. Fatal illnesses include 

fatal cancer cases and heart disease mortality and are valued using an estimate of the Value per 

Statistical Life (VSL). For non-fatal cancer cases, we developed cost-of-illness (COI) estimates for 

lung, bladder, and skin cancer for the following sub-types: 

· Non-small cell lung cancer: Stages I and II; 

· Bladder cancer: invasive and non-invasive; 

· Basal cell skin cancer: invasive and non-invasive; and 

· Squamous cell skin cancer: invasive and non-invasive. 

2.3.1 Valuation of Fatal Illnesses 

Mortality risk reductions are valued using EPA’s VSL estimate (U.S. Environmental Protection 

Agency 1999), which is defined as “how much people are willing to pay for small reductions in their 

risks of dying from adverse health conditions that may be caused by environmental pollution.” 10,11

EPA (1999) estimated a distribution of the value of mortality risk reductions using information from 

26 studies on avoided premature mortality (21 labor market and 5 contingent valuation studies). 

Specifically, EPA fit a Weibull distribution to the 26 estimates, with a central estimate of $4.8 million 

 

9

 While CDC WONDER provides mortality rates for more refined age groups (e.g., 5-year age groups), broad 

age groups were required to avoid data suppression at the county level. We compared mortality rates across 

10-year age groups and observed the most significant change at approximately age 60. 

10 For more details on EPA’s VSL estimate, see: 

http://yosemite.epa.gov/ee/epa/eed.nsf/pages/MortalityRiskValuation.html. 

11 Note that it is likely that people would undergo medical treatment before dying of a disease. However, we do 

not add any treatment costs to the VSL estimate for fatal cases. Since treatment costs are quite small (even 

including terminal care costs) compared to the VSL, there would not be a large difference in the resulting 

values. Therefore, our estimates for fatal cases may slightly underestimate the true economic damages. 

Pre-Publication Version 
Approach 

Abt Associates Inc. September 22, 2014 ▌pg. 2-12

(in 1990 dollars) with a standard deviation of $3.2 million. SafeWater CBX inflates EPA’s VSL 

estimate from 1990 dollars to a corresponding value for 2010 by multiplying by an inflation factor of 

1.67 (based on the U.S. Bureau of Labor Statistics’ Consumer Price Index).

In addition, since we know that willingness-to-pay (WTP) will generally increase if real incomes 

increase (U.S. Environmental Protection Agency 1999), we also adjusted the VSL for income growth. 

The first component of this adjustment is to determine the income growth rate. We calculated a 

compound annual growth rate (a) of approximately 0.017 in real disposable per capita income 

between 1990 and 2010, using data from the Bureau of Economic Analysis’ National Income and 

Product Accounts Tables. In addition, we use a distribution of income elasticity (ε) to characterize 

how WTP varies with income over time. We use a triangular distribution of income elasticity from 

EPA (1999): low = 0.08, high = 1, and most likely = 0.4. Using the following equation, SafeWater 

CBX conducts Monte Carlo analyses of the VSL and income elasticity distributions to obtain a VSL 

distribution for this analysis: 

 

  ( )

 ( )

 

2.3.2 Valuation of Non-Fatal Cancer Cases 

We developed COI estimates for non-fatal cases of: 

· Bladder cancer: invasive and non-invasive; 

· Non-small cell lung cancer: Stages I and II; 

· Basal cell skin cancer: invasive and non-invasive; and 

· Squamous cell skin cancer: invasive and non-invasive. 

WTP is the preferred valuation approach because it captures the full damages associated with disease, 

including pain and suffering, whereas COI estimates are comprised of medical costs and “opportunity 

costs” of time lost to treating the illness. For each non-fatal health effect associated with exposure to 

arsenic, we first conducted a literature search to determine whether there are existing WTP or COI

estimates. While estimates for some of the three cancers exist in the literature (e.g., Avritscher, 

Cooksley et al. 2006; Adamowicz, Dupont et al. 2007; Seidler, Bayoumi et al. 2012), they did not 

meet the following criteria which are desirable, consistent with economic theory, and/or necessary for 

our approach: (1) disease-specific; (2) based on recent, nationally representative U.S. data; (3) 

individual-level; (4) based on patient expenditures, not hospital/provider charges; and (5) applicable 

to cancer survivors only. Therefore, we developed typical treatment profiles for each disease, using 

information from the literature and major medical organizations (e.g., American Cancer Society). We 

then calculated disease-specific COI estimates based on the treatment profiles and recent data. Figure 

2-3 provides an overview of our approach for calculating COI estimates. 

Pre-Publication Version 
Approach 

Abt Associates Inc. September 22, 2014 ▌pg. 2-13

Figure 2-3: Cost-of-Illness Approach 

∑ Frequency × Cost per procedure Medical Costs in each Year

Treatment Information · Medical procedures · Frequency of procedures · Hours required per procedure

∑ Frequency × Hours required × Value per Hour or Days of employment disability × Value per Hour × 16 hours

Opportunity Costs in each Year

Economic Damages In SafeWater CBX, unit costs are multiplied by the number of avoided cases and expressed in present terms

Total Unit Costs in each Year Medical Costs + Opportunity Costs

For the cancer stages with non-fatal cases, we conducted a literature search to determine the treatment 

profile of procedures that are typically used (and their frequency) for treatment of the cancer in the 

United States. The treatment profile was then used to calculate medical and opportunity costs in each 

year. We calculated the per-procedure medical expenditures (using Medical Expenditure Panel 

Survey data for 1996-2010) and multiplied by frequency of the procedures to obtain the medical costs 

in each year. 

The other component of the cost of an illness is the opportunity cost – i.e., the value of time lost 

during the illness. The amount of time lost depends on how severely the illness impacts a person’s 

ability to participate in normal activities. We divided non-fatal cancer cases into two groups, based on 

Yabroff et al.’s (2004) estimates of the percentages of cancer patients that are unable to work due to 

Pre-Publication Version 
Approach 

Abt Associates Inc. September 22, 2014 ▌pg. 2-14

health problems.12 In the first group, people are able to continue normal activities throughout their 

treatment and follow-up doctor visits. In the second group of non-fatal cancer cases, people are 

unable to participate in normal activities during the initial phase of treatment (i.e., diagnosis and 

initial surgeries, chemotherapy, and other treatments). 

We estimated opportunity costs for the two groups as follows: 

· For those who are able to continue normal activities, we estimated opportunity costs using a 

“partial productivity” approach. Under the partial productivity approach, we obtained nationallevel data on the amount of time and average wage rates for time spent working, at leisure, and 

housekeeping. We then calculated the average value of a waking hour. Next, we obtained data 

(from the literature and the websites of organizations such as the American Cancer Society) on 

the amount of time spent receiving medical treatment and the amount of caretaker time required. 

We multiplied the amount of time by the frequency of procedures in each year and the value of a 

waking hour. 

· For those who are unable to participate in normal activities during the initial phase of 

treatment, we valued lost time based on the value of full employment disability (i.e., 

inability to work). Based on the treatment profile for the illness, we determined the total number 

of months that patients actively receive treatment during the initial treatment phase. We valued 

each day using the value of a waking hour multiplied by 16 hours (assuming 8 hours of sleep per 

day). We then used a partial productivity approach for the maintenance phase of treatment (i.e., 

follow-up doctor visits, and treatments to ensure that the cancer stays in remission). 

Total unit costs are the sum of medical and opportunity costs. For each cancer type and stage, we 

calculated two per-person unit costs: in the year of diagnosis and in each subsequent year. The final 

economic damages are then calculated as the present discounted value of the stream of economic 

impacts – i.e., the value of all (current and future) impacts are expressed in present terms, to reflect 

people’s preference for current consumption over future consumption. In particular, for non-fatal 

cancer cases, costs are incurred in every year after diagnosis until the person’s death, based on age at 

diagnosis and general life expectancy. These costs are projected to future years and then discounted to 

present terms. Based on U.S. EPA guidance (2007) for economic analysis, we use a discount rate of 

3%, which reflects the consumption rate of interest (i.e., the post-tax rate of returns on savings). A 

summary of the COI estimates is presented in Section 3.3. 

2.4 SafeWater CBX Model

We customized the SafeWater CBX model to estimate the burden of arsenic in private wells. 

SafeWater CBX is a state-of-the-art economic analysis model that Abt Associates developed for the 

EPA Office of Ground Water and Drinking Water to estimate the costs and benefits of new or revised 

drinking water regulations (Abt Associates Inc. 2010). 

SafeWater CBX conducts two-dimensional Monte-Carlo simulations (Cullen and Frey 1999) to 

estimate the number of disease cases due to drinking water contamination as well as economic value 

 

12 These percentages are for the overall population of cancer patients, not just those of working age. 

Pre-Publication Version 
Approach 

Abt Associates Inc. September 22, 2014 ▌pg. 2-15

placed by the affected population on avoiding these disease cases. Specifically, SafeWater CBX relies 

on explicit distributional representations of variability (e.g., by age, sex, or location) and uncertainty 

(e.g., statistical error) in relevant inputs. The model efficiently aggregates information from 

variability and uncertainty distributions for a large number of inputs through sampling (Latin 

Hypercube or Monte Carlo) to obtain distributional estimates for the outputs of interest (i.e., disease 

cases and economic value of avoiding them). 

We selected a model year of 2010 to use in SafeWater CBX. The year is important for the economic 

damages, since estimates developed in different years must be adjusted to one common year to 

account for inflation and/or differences in income levels.13 The data inputs were from several 

different years (e.g., arsenic occurrence in 1992-2004, private well population in 2005). Since more 

recent data were not available, we assume that these data were representative of 2010. In addition, the 

economic damages were projected to future years and then discounted to present terms for 2010. 

In Figure 2-4, we provide conceptual representation of how the analytical inputs were combined in 

SafeWater CBX, with the specific equations and/or details being presented in sections 2.1 through 

2.3. The main steps involve estimating the (1) excess risk of adverse health effects from arsenic in 

well water, (2) cases of adverse health effects, (3) fatal/non-fatal split of cases, and (4) economic 

value of avoiding fatal and non-fatal cases. 

 

13 COI estimates are adjusted for inflation only. Since willingness-to-pay estimates (such as the VSL) most 

likely change over time as income changes, the VSL was adjusted for both inflation and income growth. 

Pre-Publication Version 
Approach 

Abt Associates Inc. September 22, 2014 ▌pg. 2-16

Figure 2-4: Estimation of Annual Disease Cases and Economic Values in SafeWater CBX 

VSL: value per statistical life; COI: cost-of-illness

Pre-Publication Version 
Data Inputs 

Abt Associates Inc. September 22, 2014 ▌pg. 3-1

3. Data Inputs

3.1 Exposure Inputs

Figure 3-1 illustrates the private well arsenic concentration distributions developed using our 

Bayesian concentration model for all 12 GWA regions. These distributions are applied to the private 

well drinking population in each region. To illustrate, two panels are discussed: GWA Region B, 

which includes California and Nevada, and had abundant data (150 samples, with only 19% nondetects), and GWA Region G, which includes Alabama, Florida, Georgia and South Carolina, and had 

relatively sparse data (137 samples, with 90% non-detects). The GWA Region B private-well arsenic 

concentration distribution covers a range in concentrations from approximately 0.05 to 500 μg/L and 

most of it lies above the MRL of 1 μg/L. The GWA Region G occurrence distribution covers a range 

in concentration from approximately 10-6 to 100 μg/L, but shows considerably more estimation 

uncertainty at the lower percentiles that fall below the MRL. 

Figure 3-1: Private-Well Arsenic Concentration Distributions for GWA Regions 

Note: Each panel represents the cumulative distribution frequency of modeled arsenic concentrations (μg/L) in private 

wells for the USGS GWA region specified. The solid line represents the central estimate of the distribution, while the 

grey lines correspond to 1,000 distributions reflecting estimation uncertainty. The vertical dashed line at 1 μg/L 

represents the minimum reporting level.

GWA Region B GWA Region C

Pre-Publication Version 
Data Inputs 

Abt Associates Inc. September 22, 2014 ▌pg. 3-2

Note: Each panel represents the cumulative distribution frequency of modeled arsenic concentrations (μg/L) in private 

wells for the USGS GWA region specified. The solid line represents the central estimate of the distribution, while the 

grey lines correspond to 1,000 distributions reflecting estimation uncertainty. The vertical dashed line at 1 μg/L 

represents the minimum reporting level.

GWA Region D GWA Region E

GWA Region F GWA Region G

Pre-Publication Version 
Data Inputs 

Abt Associates Inc. September 22, 2014 ▌pg. 3-3

Note: Each panel represents the cumulative distribution frequency of modeled arsenic concentrations (μg/L) in private 

wells for the USGS GWA region specified. The solid line represents the central estimate of the distribution, while the 

grey lines correspond to 1,000 distributions reflecting estimation uncertainty. The vertical dashed line at 1 μg/L 

represents the minimum reporting level.

GWA Region H GWA Region I

GWA Region J GWA Region K

Pre-Publication Version 
Data Inputs 

Abt Associates Inc. September 22, 2014 ▌pg. 3-4

Note: Each panel represents the cumulative distribution frequency of modeled arsenic concentrations (μg/L) in private 

wells for the USGS GWA region specified. The solid line represents the central estimate of the distribution, while the 

grey lines correspond to 1,000 distributions reflecting estimation uncertainty. The vertical dashed line at 1 μg/L 

represents the minimum reporting level.

GWA Region L GWA Region M

Table 3-1 reports several GWA region-specific estimates from the Bayesian arsenic concentration 

model. Across the United States, the median of arsenic concentration is 0.62 ug/L, the share of 

observations above the MRL of 1 μg/L is 35%, and the share of observations above the EPA’s MCL 

of 10 μg/L is 6.3%. However, there is variation across the regions in these estimates. In addition to 

central estimates, we report 90% credible intervals reflecting estimation uncertainty. 

Pre-Publication Version 
Data Inputs 

Abt Associates Inc. September 22, 2014 ▌pg. 3-5

Table 3-1: Estimated Arsenic Concentration Levels in Private Wells by GWA Region Based on 

NAWQA Data 

Region States

Median(a) (μg/L) Percent Above 1 

μg/L (a) (c)(%)

Percent Above 10 

μg/L (a) (d)(%)

Central Estimate 

(b)

90% Credible IntervalCentral 

(b)

Estimate90% Credible IntervalCentral 

(b)

Estimate90% Credible Interval

B California, Nevada 2.9 2.3 - 3.1 83 76-86 13 8.7-16

C

Arizona, Colorado, 

New Mexico, Utah

1.9 1.7 - 2.4 66 63-73 14 12-18

D

Kansas, Missouri, 

Nebraska

2.6 2.3 - 2.9 79 75-83 12 10-15

E Oklahoma, Texas 0.94 0.78 - 1.26 48 44-56 6.5 5.1-10

F

Arkansas, 

Louisiana, 

Mississippi

0.32 0.13 - 0.44 25 15-29 1.9 0.46-4.0

G

Alabama, Florida, 

Georgia, South 

Carolina

0.18 0.04 - 0.23 15 7.5-16 0.8 0.16-1.9

H

Idaho, Oregon, 

Washington

1.2 1.1 - 1.6 57 52-65 4.2 3.0-7.8

I

Montana, North 

Dakota, South 

Dakota, Wyoming

0.48 0.40 - 1.05 34 32-51 4.7 2.3-10

J

Iowa, Michigan, 

Minnesota, 

Wisconsin

0.25 0.08 - 0.37 26 17-30 4.1 1.8-7.4

K

Illinois, Indiana, 

Kentucky, Ohio, 

Tennessee

0.37 0.17 - 0.54 36 28-40 12 7.1-14

L

Delaware, Maryland, 

New Jersey, North 

Carolina, 

Pennsylvania, 

Virginia, West 

Virginia

0.19 0.11 - 0.30 22 18-27 3.4 2.2-5.3

M

Connecticut, Maine, 

Massachusetts, New 

Hampshire, New 

York, Rhode Island, 

Vermont

0.23 0.16 - 0.54 30 26-39 8.7 5.2-12

Overall for contiguous U.S.

(e) 0.62 0.56 - 0.67 35 32-36 6.3 5.3-7.1

(a) The point and range estimates are rounded to two significant figures.

(b) The central estimate is a maximum a posteriori probability (MAP) estimate.

(c) 1 μg/L represents the MRL in the NAWQA dataset.

(d) 10 μg/L represents EPA’s current MCL for arsenic in public water systems.

(e) The overall estimates were computed using GWA region-specific populations of private well water drinkers as 

weights.

Pre-Publication Version 
Data Inputs 

Abt Associates Inc. September 22, 2014 ▌pg. 3-6

3.2 Dose-Response Inputs

Based on our review of select government documents and peer-reviewed studies (described in section 

2.2), we selected four endpoints to illustrate the health burden of arsenic in private wells in the United 

States. The endpoints, the corresponding dose-response relationships, and additional information are 

presented in Table 3-2. 

Table 3-2: Dose-Response Information for Health Effects 

Endpoint Population Risk Estimate 

Type (Units)

Central 

Estimate Additional Details

Bladder 

cancer 

incidence

Females 

Males

DWURannual (per 

μg/L)

2.92E-06 a

3.41E-06 a

17.1% fatal; 74.9% non-fatal/noninvasive; 8.1% non-fatal/invasive

Lung 

cancer 

incidence

Females 

Males

DWURannual (per 

μg/L)

4.69E-06 a

1.98E-06 a

81.1% fatal; 16.8% nonfatal/Stage I; 2.1% nonfatal/Stage II

Skin 

cancer 

incidence

Females

Males

DWURannual (per 

μg/L)

3.52E-07 b

7.49E-07 b

0% fatal; 16.3% non-fatal/basal 

cell; 20.7% non-fatal/squamous 

cell (non-invasive); 63.0% nonfatal/squamous cell (invasive)

Heart 

disease 

mortality

All β (per μg/L) 2.21E-3

c

Baseline heart disease mortality 

rate (per 100,000) is 27.7 for ages 

0-59 and 832 for ages 60+d

DWURannual: annual drinking water unit risk; β is a risk coefficient derived from a hazard ratio (HR)

(a) The lifetime DWUR is based on central oral slope factors (OSF) which are calculated from ED01 estimates in 

Table 5-3 of EPA (2010) as 0.01/ED01. The lifetime DWUR is calculated from the central OSF using an ingestion 

rate of 2 L/day and a bodyweight of 70 kg. The DWURannual is the lifetime DWUR divided by life expectancy 

(81.1 years for females; 76.3 years for males).

(b) A central OSF of 1 per mg/kg-day for females and of 2 per mg/kg-day for males is detailed in EPA (1988). 

An OSF of 1.5 per mg/kg-day and lifetime DWUR of 5E-5 per μg/L are reported in the IRIS database, available 

at: http://www.epa.gov/iris/subst/0278.htm

(c) The β is derived from a HR of 1.29 per 115 μg/L summarized in Chen et al. (2011), reflecting increased 

deaths of “ischemic heart disease and other forms of heart disease” (ICD-10 codes I20-25 and I30-51) from an 

increase in well water arsenic. The HR = eβΔX.

(d) The heart disease mortality rates vary by county. The estimates presented in the table are national-level 

summaries obtained from CDC WONDER.

3.3 Economic Inputs

As described in Section 2.3, fatal illnesses were valued using the VSL and non-fatal cancer cases 

were valued using COI estimates. 

3.3.1 VSL

Using the approach described in Section 2.3.1, we updated EPA’s VSL estimate to account for 

inflation and income growth since 1990. In 2010 dollars and at 2010 income levels, the mean VSL 

estimate is $9,470,085, with a 95% confidence interval of $914,924 to $25,085,605 (see Figure 3-2).

Pre-Publication Version 
Data Inputs 

Abt Associates Inc. September 22, 2014 ▌pg. 3-7

This Weibull distribution of the VSL is used in an uncertainty analysis in the SafeWater CBX model 

for all fatal cases (including heart disease mortality and select cases of lung and bladder cancer). 

Figure 3-2: Distribution of VSL Estimate in 2010 dollars and at 2010 income levels 

3.3.2 COI 

Total unit costs for each type of non-fatal cancer are presented in Table 3-3. These costs are perperson annual estimates comprised of both medical and opportunity costs. Since the non-melanoma 

skin cancers associated with arsenic are readily treatable, their costs are lowest. Lung cancer requires 

more intensive treatment and often leads to the inability to work. Therefore, lung cancer has the 

highest unit costs. As described in Section 2.4, these values are incorporated into the SafeWater CBX 

model, which calculates the present discounted value of the stream of economic damages for the 

model year of 2010. For example, for non-fatal cancer cases, costs are incurred in every year after 

diagnosis until the person’s death. These costs are projected to each future year and are then 

discounted to present terms for 2010. 

Pre-Publication Version 
Data Inputs 

Abt Associates Inc. September 22, 2014 ▌pg. 3-8

Table 3-3: Cost of Illness Estimates (2010$) 

Cancer Type Cancer Category Cost in 

First Year

Cost in 

Subsequent 

Years

Bladder

Non-invasive $13,705 $941

Invasive $37,463 $2,532

Lung

Stage I $39,255 $934

Stage II $72,284 $934

Skin

Basal cell $1,274 $317

Squamous cell, non-invasive $1,591 $274

Squamous cell, invasive $3,180 $401

3.4 Variability and Uncertainty

To the extent possible, we characterized variability and uncertainty in our inputs. The details on 

input-specific uncertainty and variability that we modeled are summarized in Table 3-4. 

Pre-Publication Version 
Data Inputs 

Abt Associates Inc. September 22, 2014 ▌pg. 3-9

Table 3-4: Characterization of Input Variability and Uncertainty 

Input Name Variable By Uncertainty Characterization

Arsenic level in 

private wells

(μg/L)

12 GWA regions, each with a

separate lognormal variability 

distribution estimated using a 

hierarchical Bayesian model

Estimation uncertainty was characterized for 

each regional variability distribution, using 

1,000 draws of lognormal distribution 

parameter sets that were consistent with the 

arsenic sampling data

Population served 

by wells 

3,109 U.S. counties; 

aggregated to 12 GWA 

regions

NA

Drinking water 

unit risks (per 

μg/L)

Cancer type (lung, bladder, 

skin) and sex

NS

Baseline heart 

disease mortality 

rates

3,109 counties and 2 age 

groups (less than 60 years; 60 

years and older)

NA

Fatal/non-fatal 

split for cancers

Cancer type (lung, bladder, 

skin) and sub-type (e.g., 

invasive, non-invasive)

NA

Value for fatal 

cancer and heart 

disease mortality 

cases ($/year)

Country (same value applied 

to entire relevant U.S. 

population)

Updated the EPA’s Weibull distribution of the 

VSL uncertainty using the income growth 

adjustment factor. This factor was also 

uncertain, due to the uncertainty in the VSL 

income elasticity

Values for nonfatal cancer cases 

($/year)

Cancer type (lung, bladder, 

skin) and sub-type (e.g., 

invasive, non-invasive)

NS

NA: No information was available to characterize the uncertainty in the indicated input parameter. 

NS: Some information was available to characterize uncertainty, but it was insufficient for a full characterization 

in our modeling effort.

Pre-Publication Version 
Results 

Abt Associates Inc. September 22, 2014 ▌pg. 4-1

4. Results

Figure 4-1 summarizes our mean estimates of the burden of arsenic from private water wells in the 

United States from the two-dimensional Monte Carlo analysis for the model year 2010. Across the 

United States, we estimated 500 deaths from heart disease and 1,074 cases of cancer (511 lung, 480 

bladder, and 83 skin cancer cases) attributable to arsenic in private wells. While non-cancer cases 

represented approximately a third of all cases, they represented approximately half of the total annual 

damages of $9.7 billion dollars. This is driven by the fact that the non-cancer endpoint of heart 

disease mortality was 100% fatal, while the fatality rate for the three cancer types ranged from 0% 

(skin) to 81% (lung) and the economic value associated with the fatal cases was much larger than the 

economic value assigned to non-fatal cases. Similarly, within the cancer cases, while the number of 

lung cancer cases was nearly same as the number of bladder cancer cases, lung cancer’s higher 

fatality rate (81% versus 17% for bladder cancer) caused lung cancer to have larger economic 

damages. The skin cancer damage estimate of $0.466 million was the smallest because there were 

fewer excess cases of non-melanoma skin cancer and this type of cancer is usually non-fatal. 

Figure 4-1: Mean National Estimates of Arsenic Burden in Private Wells in the United States in 

Terms of Cases and Economic Damages 

Table 4-1 presents the mean and range estimates for the cases and damages ($), providing detailed 

information for lung, bladder, and skin cancer in terms of fatal cases and non-fatal sub-type cases. 

The range reflects the 5th percentile and 95th percentile estimates of our results that capture variability 

(in exposure, population served by wells, drinking water unit risks, and baseline heart disease 

$4,729

$3,918

$784

$0.47

0

100

200

300

400

500

600

 $-

 $500

 $1,000

 $1,500

 $2,000

 $2,500

 $3,000

 $3,500

 $4,000

 $4,500

 $5,000

Heart

Disease

Lung

Cancer

Bladder

Cancer

Skin Cancer

Cases

Damages (millions of $)

Health Endpoints

Fatal Cases

Non-Fatal Cases

Damages (millions of $)

Pre-Publication Version 
Results 

Abt Associates Inc. September 22, 2014 ▌pg. 4-2

mortality) and uncertainty (in exposure and VSL estimates).14 The range in economic damages is 

much larger than the range in cases, and this is driven by the uncertainty in the VSL estimate applied 

to fatal cases. Illustrating this, the range in economic damages is similar to the range in cases for the 

skin cancer estimates, which were all non-fatal and so where the VSL was not applied.

 

14 We do not present a range for the ‘all cancer total’ estimate since the estimates for lung, bladder, and skin 

cancer are drawn from three separate distributions.) 

Pre-Publication Version 
Results 

Abt Associates Inc. September 22, 2014 ▌pg. 4-3

Table 4-1: Summary of Disease Burden Results 

Health Endpoint Categories Number of Cases a Damages (2010$, Millions) b

Mean Range (P5 – P95) c Mean Range (P5 – P95) c

Heart Disease Mortality 500 (395 – 576) $4,729 ($663 – $10,200)

Lung Cancer

Fatal 414 (323 – 486) $3,913 ($544 – $9,030)

Non-fatal: Stage 1 86 (67 – 101) $4.06 ($3.17 – $4.76)

Non-fatal: Stage 2 11 (8 – 12) $0.844 ($0.658 – $0.990)

Total 511 (398 – 599) $3,918 ($548 – $9,040)

Bladder Cancer

Fatal 82 (64 – 96) $774 ($108 – $1,790)

Non-fatal: Invasive 39 (30 – 45) $2.29 ($1.79 – $2.69)

Non-fatal: Non-Invasive 359 (280 – 422) $7.81 ($6.09 – $9.16)

Total 480 (374 – 563) $784 ($115 – $1,800)

Skin Cancer

Non-fatal: Basal Cell 14 (11 – 16) $0.0541 ($0.0422 – $0.0635)

Non-fatal: Squamous Cell Invasive 52 (41 – 61) $0.345 ($0.269 – $0.405)

 Non-fatal: Squamous Cell Non-Invasive 17 (13 – 20) $0.0675 ($0.0526 – $0.0792)

 Total 83 (65 – 97) $0.467 ($0.364 – $0.548)

All Cancer Total 1,074 N/A $4,702 N/A

All Endpoint Total 1,574 N/A $9,714 d N/A

(a) Case values were rounded to the nearest whole number. 

(b) Valuations were rounded to three significant figures.

(c) 5

th

- 95th percentile range reflects uncertainty in exposure for non-fatal health endpoints and uncertainty in exposure and the VSL for fatal health endpoints.

(d) The ‘all endpoint’ damages total differs from the sum of the cancer and non-cancer totals because it is the mean of the overall damages distribution.

Pre-Publication Version 
Results 

Abt Associates Inc September 22, 2014 ▌pg. 4-4

Table 4-2 presents the mean estimates for the total health endpoint cases and total economic damages 

for each GWA region. 

Table 4-2: Regional Results Summary 

GWA 

Region

Mean Cases All Endpoint 

Total Damages 

($, Millions) a

Heart Disease 

Mortality b Lung Cancer c Bladder Cancer

d Skin Cancer e

B 49 51 48 8 $959

C 14 19 18 4 $328

D 25 22 20 4 $452

E 19 17 16 3 $346

F 7 5 5 1 $122

G 13 12 12 1 $241

H 15 17 16 3 $311

I 4 5 4 0 $89.5

J 46 46 43 8 $883

K 168 168 159 27 $3,240

L 56 57 53 9 $1,090

M 83 89 84 14 $1,660

(a) Valuations were rounded to three significant figures.

Percentage of cases that were fatal vs. non-fatal: (b) 100% / 0% (c) 81% / 19% (d) 17% / 83% (e) 0% / 

100%.

As expected, there was geographic variability in the case and damage estimates across the 12 GWA 

regions, paralleling regional differences in exposure and population. We provide detailed tables 

(similar to Table 4-1) of regional results in Appendix E. The mean regional results are summarized in 

maps in Figure 4-2 and Figure 4-3. Regions B, J, K, L, and M had the highest mean total cases (over 

140) and economic damages (over $800 million). 

Pre-Publication Version 
Results 

Abt Associates Inc September 22, 2014 ▌pg. 4-5

Figure 4-2: Mean Regional Estimates of Arsenic Burden (Total Cases of Lung, Bladder, and 

Skin Cancer and Heart Disease Mortality) from Private Wells 

Pre-Publication Version 
Results 

Abt Associates Inc September 22, 2014 ▌pg. 4-6

Figure 4-3: Mean Regional Estimates of Arsenic Burden (Total Economic Damages) from 

Private Wells by GWA Region 

Pre-Publication Version 
Discussion 

Abt Associates Inc September 22, 2014 ▌pg. 5-1

5. Discussion

For the private well population in the United States, we estimated over 1,000 cases of lung, bladder, and 

skin cancer attributed to consumption of well water. Nearly half of the cancer cases were fatal. 

Additionally, we also estimated nearly 500 cases of premature heart disease mortality attributed to arsenic 

in private wells. Our estimate of $9.7 billion dollars for the economic damages is largely a function of the 

number of fatal cases (since the economic value of reducing mortality risk was much higher than the 

value of reducing risk of morbidity associated with the three cancers), and was split almost equally 

between cancer and non-cancer endpoints owing to the similar number of fatal cases for each. The equal 

split is a notable finding because, compared to cancer, heart disease is much less commonly associated 

with arsenic exposure. However, it reflects new and growing research linking arsenic with non-cancer 

outcomes, such as heart disease and diabetes. 

5.1 Contextualizing Our Results

Our results are hypothetical model-based estimates rather than estimates based on direct observation of 

increases in cancer/heart disease incidence. We try to contextualize our results by comparing them to 

expected annual adverse health effects in the private well population in the United States of 

approximately 42 million.15 Our estimates of 83 skin, 480 bladder, and 511 lung cancer cases attributed to 

arsenic in private wells represent roughly 0.03%, 2.2%, and 5.8% of total expected cancer cases in 2010 

in the private well population in the United States.16 When compared to the total expected cases of heart 

disease mortality in the private well population, our estimate of 500 deaths attributed to arsenic exposure 

in private wells represented 0.7% of expected cases.17 It is important to note that our estimates of 

additional cases represent just one route of exposure (ingestion), one medium (well water), and one 

pollutant; however, the exposure could be addressed. 

We compared our health burden results to other published estimates for the United States. Kumar et al. 

(2010) performed a comparative risk assessment of arsenic exposure in U.S. public and domestic drinking 

water supplies. They modeled domestic well concentrations in seven regions across the United States 

using NAWQA data collected from 1976 to 1996 and reported an excess lifetime risk of lung and bladder 

cancer mortality combined of 66 per million. Applied to the private well population and annualized, this 

corresponds to approximately 40 excess lung and bladder cancer deaths combined, which is more than 10 

times lower than our estimate of 496 lung and bladder cancer deaths combined. The discrepancy likely 

 

15 We have assumed that the rates of lung, bladder, and skin cancer and heart disease mortality in the private well 

population follow U.S. national rates. 

16 We applied U.S. level incidence rates of 719, 57, and 20 per 100,000 for skin, lung, and bladder cancer, 

respectively to the private well population. 

17 We applied the U.S. baseline heart disease mortality rate of 27.7 per 100,000 for ages 0-59 and 832 per 100,000 

for ages 60+ to the private well population, where we assumed 81% were below 60 years old. 

Pre-Publication Version 
Discussion 

Abt Associates Inc September 22, 2014 ▌pg. 5-2

results from the 10 times lower OSF employed by Kumar et al. (2010),18 but also from the differences in 

data and statistical methods used to estimate arsenic concentrations.19

 

Our estimates of the public health burden from arsenic from private wells can also be compared to arsenic 

exposure from food, where arsenic may be of the organic (less toxic) or inorganic (more toxic) form. 

(Drinking water arsenic is generally all in the inorganic form.) According to NRC (2013), when drinking 

water exposures are less than 50 μg/L, most of total exposure comes from food. In our analysis, only 

0.8% of the well population was exposed to arsenic levels above 50 μg/L, indicating the importance of 

exposure from food for most private well consumers. 

Oberoi et al. (2014) estimated the global burden of disease from arsenic in food, with a focus on lung, 

bladder, and skin cancers. They combined WHO estimates of food consumption in 13 country clusters 

with a range of estimates of inorganic arsenic in foods. We applied their global results to the U.S. 

population and found a low range estimate of approximately 400 annual cases of each of lung, bladder, 

and skin cancer attributed to arsenic in food. The high range estimate was approximately 5,000 annual 

cases of each cancer.20 Our mean lung and bladder cancer estimates from private well water of 

approximately 500 each agree with their low bound estimate from food, but our mean skin cancer 

estimate of 83 is much lower. Our mean estimates for all three cancers fall well below the upper range. 

The discrepancy is likely a consequence of two factors. First, exposure to arsenic from food may be larger 

than exposure to arsenic from private well water. Second, the OSF we employed differed from Oberoi’s, 

both across cancers and across sexes, despite the fact that we based our dose-response estimates on the 

same studies. Namely, we used 5.3 (male) and 13.3 (female) for lung; 9.1 (male) and 8.3 (female) for 

bladder; and 2 (male) and 1 (female) for skin [all units of risk per mg/kg-d]. Instead, Oberoi et al. (2014) 

used 1 (male) and 1.4 (female) for lung; 0.9 (male) and 1.4 (female) for bladder; and 1.1 (male) and 1.1 

(female) for skin [all units of risk per mg/kg-d].21

In terms of the monetary damages from arsenic contamination of private wells, we were unable to locate 

other estimates related to this issue. However, one recent study on another environmental issue -- air 

pollution and the role of trees in removing a small fraction of air pollution -- estimated the potential 

 

18 Kumar et al. (2010) used a “potency factor” for lung and bladder combined of 2.2x10-2 per mg/d based on Gurian 

et al. (2004). Using a bodyweight of 70 kg, this converts to an oral slope factor (OSF) of 1.5 per mg/kg-d. When 

we averaged our lung and bladder oral slope factors across males and females and then added them together, we 

obtained a combined slope factor of 16 per mg/kg-d.

19 Kumar et al. (2010) used 1976-1996 NAWQA data and estimated arsenic concentrations in seven US regions 

using a maximum likelihood model, while we used 1992-2004 NAWQA data and estimated arsenic concentrations 

in 12 GWA regions using a hierarchical Bayesian model. 

20 Globally, Oberoi et al. reported a range of 9,000 to 119,000 bladder cancer cases, 12,000 to 121,000 for lung 

cancer cases, and 11,000 to 110,000 for skin cancer cases. (The low and high bounds are driven by the inorganic 

fraction range in the food.) We assumed a world population of 7.016 billion and a U.S. population of 300 million 

to derive the U.S. estimates. The U.S. was part of GEMS cluster M in their analysis. 

21 Table 1 of Oberoi et al. reports slope factors in units of μg/d which we converted to mg/kg-d by assuming a 70 kg 

bodyweight. 

Pre-Publication Version 
Discussion 

Abt Associates Inc September 22, 2014 ▌pg. 5-3

benefits of the trees to be approximately $7,000 million (McGill 2014). This is in line with our arsenic in 

private wells assessment at approximately $9,700 million. 

One potentially more relevant economic comparison involves EPA’s 2000 benefit-cost analysis for the 

arsenic drinking water regulation applied to community water systems. Focusing on lung and bladder 

cancer, EPA estimated that reducing the MCL from 50 μg/L to 10 μg/L in community water systems 

could result in $217 to $306 million22 dollars of benefits (Abt Associates Inc. 2000). When we conducted 

an additional analysis to estimate the corresponding benefits of reducing arsenic in private wells from 50 

μg/L to 10 μg/L, we estimated the mean combined lung and bladder cancer benefits to be $1,277 million. 

Given the larger population served by community water systems population (approximately 253 million 

in EPA’s benefit-cost analysis versus 42 million in private wells), it is surprising that our corresponding 

private well estimate is approximately five times larger than EPA’s estimate for community water 

systems. However, since less than 1% of community water systems served by surface waters had arsenic 

levels above 10 μg/L, we assume most of EPA’s benefits came from community water systems supplied 

by groundwater (serving a population of 84 million). In addition, our benefits may be larger due to 

differences in the distribution of arsenic levels between 10 μg/L and 50 μg/L, the application by EPA of 

relative exposure factors that scaled risks between 60% and 80% compared to an individual who 

consumed 2 L of water per day and weighed 70 kg, and the larger (by factors ranging from 5 to 10) 

lifetime drinking water unit risks that we employed for lung and bladder cancer.23 The unit risks 

employed in EPA’s benefit-cost analysis were adopted from one of the models from Morales, Ryan et al. 

2000 while our drinking water unit risks are based on an EPA (2010) document which reanalyzed the data 

from Morales, Ryan et al. 2000, fitting a Poisson model and using U.S. bladder and lung cancer incidence 

data. 

5.2 Main Assumptions Underlying Our Results

Many assumptions underlie our approach and results. We summarize the main ones below. 

In terms of the private well population, we assume: 

 

22 EPA’s estimated lung and bladder cancer benefits due to the rule ranged from $139 to $198 million (in 1999 

dollars). For comparison with our estimates, we adjusted EPA’s estimates for inflation and real income growth 

between 1999 and 2010. We used our estimate of inflation- and income growth-adjusted 2010 VSL of $9.47 

million to value EPA’s 1999 estimates of the number of fatal lung and bladder cancer cases. We also adjusted 

EPA’s estimates of willingness to pay (WTP) to avoid a non-fatal cancer case by adjusting them for inflation 

(1.31; see https://usa.ipums.org/usa/cpi99.shtml) and income growth (1.19, derived from the ratio of our 2010 

VSL of $9.47 million and EPA’s 1999 VSL in 2010 dollars of $7.98 million). We then used the updated WTP 

estimates to value EPA’s 1999 estimates of the number of non-fatal lung and bladder cancer cases. 

23 The mean lifetime drinking water unit risks employed by EPA (as detailed in Exhibit B-2, Abt Associates (2000)) 

were: 2.53E-5 (male) and 3.97E-5 (female) per μg/L for bladder cancer and 2.75E-5 (male) and 3.88E-5 

(female) per μg/L for lung cancer. The corresponding lifetime drinking water unit risks used in this analysis 

were: 2.60E-4 (male) and 2.37E-4 (female) per μg/L for bladder cancer and 1.51E-4 (male) 3.80E-4 (female) 

per μg/L for lung cancer. 

Pre-Publication Version 
Discussion 

Abt Associates Inc September 22, 2014 ▌pg. 5-4

· The number of private well drinkers did not change between the years 2005 to 2010. We 

assumed that there were no changes in number of private well users between the year 2005, the 

latest year for which data were available, and the year 2010 (our model year). 

· The demographic and income characteristics of the private well users are same as those of 

the U.S. population. While we had data on the number of persons using private wells for each 

county in the United States, there was no readily available information on the demographic (e.g., 

age, sex) and income characteristics of this U.S. population subgroup. Thus, we assumed that the 

age and sex distribution of the private well population followed that of the national U.S. 

population, as summarized in the 2000 U.S. Census. The sex distribution was used in estimating 

the male and female populations for the cancer cases (since the dose-response information was 

sex-specific) and the age distribution was used in estimating the population above and below 60 

years old for the heart disease deaths (since we used separate baseline heart disease mortality 

rates for above and below 60). Furthermore, we assumed that the annual income per capita of the 

private well population was similar to that of the United States as a whole. 

In terms of the exposure modeling, we assume: 

· The Bayesian concentration model adequately reflects concentrations of arsenic in private 

wells in the United States. Our concentration model has many advantages, such as modeling 

concentrations below the MRL (rather than substitution) and leveraging data from other regions 

when within-region data are sparse. However, the model is based on available USGS NAWQA 

private well concentrations, and we assume that these concentrations are representative of the 12 

GWA regions we modeled. In particular, the NAWQA sample is unlikely to have captured 

“hotspots” (extreme high arsenic concentrations) given that it was a general sample and not 

targeted to high level areas. If an analysis of hotspots is desired, one option would be to 

investigate hotspot samples in particular, like the data from the state of Florida detailed in CDC’s 

Private Well Initiative. 

· Well owners consume a fixed amount of untreated drinking water from their home tap. In 

our analysis, we assumed that all water consumed was at the modeled concentration. In other 

words, well owners do not: (i) treat their well water, (ii) consume water at different 

locations/concentrations (such as at the workplace), or (iii) consume bottled water. Shaw et al. 

(2005) reported that in a community with known arsenic issues (66% of household wells had 

arsenic levels above the MCL of 10 μg/L), only 18% used a system that was effective at 

removing arsenic and a similar percentage (16%) consumed only bottled water. Furthermore, in 

converting our OSF dose-response estimates to DWUR, we assumed a fixed consumption of 2 

L/d as is the default in many human health risk assessments. 

In terms of the dose-response analysis, we assume: 

· It is valid to apply results from studies conducted outside of the United States to a U.S. 

private well population. In dose-response modeling, we often face the issue of animal-to-human 

extrapolation (applying results from toxicological studies to humans) and high-to-low dose 

extrapolation (applying results from highly exposed populations, such as in occupational or 

unique situations, to persons exposed at much lower environmentally relevant concentrations). 

The studies underlying the cancer unit risks and the heart disease mortality HR are based on 

cohorts in Taiwan and Bangladesh, respectively. Where possible, adjustments are made that are 

specific to U.S. populations (e.g., in terms of bodyweight, ingestion rates, and well 

Pre-Publication Version 
Discussion 

Abt Associates Inc September 22, 2014 ▌pg. 5-5

concentrations). Research elucidating the effects of arsenic on human health at levels below 100 

μg/L is growing. Such studies should be examined for use in a future analysis. However, we 

follow human health risk assessment practices in applying this research to a U.S. population (e.g., 

EPA’s IRIS unit risk for skin cancer is based on the Taiwan cohort) to illustrating the magnitude 

of the burden of arsenic in private wells in the United States. 

· Arsenic has no threshold. Some researchers argue that exposure to arsenic only results in 

adverse effects at high levels of exposure, and that exposures below a threshold do not result in 

any adverse effects. One theory is that exposure to arsenic leads to reactive metabolites that 

interact with groups on the surface of cells, leading to cytotoxicity and regenerative cellular 

proliferation (Cohen, Arnold et al. 2013). This is an area of research and many recent studies 

have linked low-doses of arsenic in humans with diabetes (Navas-Acien, Silbergeld et al. 2008), 

cardiovascular disease (Moon, Guallar et al. 2013), immunotoxicity (Dangleben, Skibola et al. 

2013), and cancer (García-Esquinas, Pollán et al. 2013; Gilbert-Diamond, Li et al. 2013; Mostafa 

and Cherry 2013; Schmidt 2014). In our analysis, we have assumed that arsenic may cause 

adverse effects at very low levels. We also conducted our analysis assuming a threshold of 1 μg/L 

(the MRL) and 10 μg/L (the MCL) and found that the economic damages were 91% and 61%, 

respectively, of the damages in our no threshold model. 

· The heart disease mortality rates we derived are applicable to the private well population.

We applied county-level heart disease mortality rates in determining the excess heart disease 

deaths due to arsenic exposure (above and below 60 years of age). We assumed that the countylevel rates apply to the private well population in each county. 

· The fatal/non-fatal cancer split we derived is applicable to the private well population. We 

derived the fraction of bladder and lung cancers that are fatal using national-level data and we 

assume that this estimate holds for the private well population. 

· The annual cancer risk is the same as the lifetime cancer risk divided by life expectancy. 

The DWUR dose-response estimates reflect lifetime risk of cancer. We annualized them by 

dividing by life expectancy. However, this is an approximation since cancers can have long 

latency periods. 

In terms of the economic analysis, we assume: 

· The economic estimates capture the relevant dimensions. For fatal cases, we applied EPA’s

VSL estimates, which are based on the studies of willingness to pay to reduce the risk of death. 

For non-fatal cases, our COI estimates are based on treatment information (including medical and 

opportunity costs). The non-fatal cases may involve other dimensions, such as pain and suffering, 

which we did not capture. Similarly, the fatal cases may involve treatment costs which we do not 

capture (though the treatment costs are generally much smaller than the VSL estimate). 

· The U.S. EPA’s VSL estimate is applicable to private well drinkers. We assume that the 

income of the private well population is similar to the income of U.S. residents as a whole. While 

we had good information on the number of private well drinkers, we did not know anything about 

them in terms of age, sex, income, etc. and had to make assumptions regarding these parameters.

Pre-Publication Version 
Discussion 

Abt Associates Inc September 22, 2014 ▌pg. 5-6

5.3 Limitations/Areas for Refinement

While we accomplished our goal of providing an illustration of the potential magnitude of the burden of 

arsenic in private wells using available data and information, there are many opportunities to refine our 

estimates. In Table 5-1, we call out the limitations of our analysis and suggest areas for refinement. 

Table 5-1: Limitations and Opportunities for Refinement Associated with this Analysis 

Limitation Potential Refinement

Only examined four health 

endpoints due to illustrative 

nature of this project.

The literature linking arsenic to adverse health effects is growing. 

EPA’s assessment of inorganic arsenic is expected in 2016 and could 

provide helpful information on ways to expand this analysis to include 

additional health endpoints for arsenic like diabetes and 

neurodevelopmental, immune system, and respiratory effects.

Did not conduct a “hot spot” 

analysis

Because we wanted to yield nationally representative estimates, we 

used USGS data that was collected in a systematic fashion and did 

not reflect “hot spots”. However, it may be of interest to examine for 

one affected area the potential magnitude of the burden of arsenic in 

wells (e.g., Florida data summarized in CDC’s Private Well Initiative). 

We can readily apply our approach to other specific geographic 

areas. 

Insufficient information for 

quantitative uncertainty analysis 

for dose-response and COI 

estimates.

Additional analyses are required in order to capture the uncertainty 

associated with our dose-response estimates and this activity could 

be undertaken in the future. Similarly, the COI estimates could be 

more fully characterized.

Assumed demographic and 

socioeconomic information on 

private well drinkers.

Additional analyses could better characterize the population in terms 

of age, sex, and income where we assumed that the private well 

population followed U.S. patterns as a whole (e.g., heart disease 

mortality rates). Furthermore, we could collect additional information 

on risk factors for adverse health outcomes (e.g., smoking). This type 

of information might be derived from the U.S. Census since we know 

the latitude and longitude of the sampled wells or from a tailored 

survey of these populations.

Only examined arsenic exposure 

from drinking water sources, but 

total arsenic load could be 

higher.

Private well drinkers could have higher arsenic loads as a result of 

consumption of food or participation in occupations where arsenic 

exposure is high. We could model the exposure to arsenic from 

food/occupation to understand which communities might have a 

larger total arsenic load.

Ability to characterize variability 

consistently

Our estimates had different levels of resolution – from the county level

to the national level. We could try to obtain better resolved estimates.

Pre-Publication Version 
Discussion 

Abt Associates Inc September 22, 2014 ▌pg. 5-7

Only examined arsenic There are other groundwater pollutants, like uranium and nitrates, 

that are of concern. We could apply our general framework to these 

pollutants. Nitrate, which results mainly from agricultural uses, has 

been linked with cancer, reproductive outcomes, and diabetes in 

adults (though the drinking water standard is protective against 

methemoglobinemia) (Ward, DeKok et al. 2005).

5.4 Conclusions for Stakeholders

We have conducted an illustration of the burden of arsenic in private wells in the United States in terms of 

select adverse health effects and their associated economic damages. We used existing data from USGS to 

characterize current levels in private wells, select four health endpoints and characterize the doseresponse relationship, and estimate economic values for the fatal and non-fatal endpoints (e.g., to develop 

the COI and VSL estimates). We characterized variability and uncertainty in our inputs to the extent 

possible before conducting a Monte Carlo simulation to generate our results. Our results indicate a nonnegligible burden of disease from arsenic in private wells in the United States – over 1,000 cases of 

cancer and 500 deaths from heart disease, estimated to cause over $9 billion dollars in damages across the 

United States. 

To our knowledge, this is the first effort that has attempted to put a dollar value on the impact of arsenic 

in well water across the United States. While the burden of arsenic in private wells varies across the 12 

U.S. groundwater area regions we modeled, it also varies within regions. Thus, efforts to reduce high 

exposures should not be limited to one GWA region. The information from this report can be used to 

garner or further support of initiatives aimed at reducing arsenic exposure. The adverse health effects 

from arsenic in private wells can be prevented with targeted public health educational campaigns and 

interventions. 

Reducing exposure to arsenic in private wells can be achieved in several ways. First, home owners may 

install appropriate and effective treatment technologies either at the faucet or at the entry to the home. 

Second, home owners may use bottled water for drinking and cooking. Third, home owners may consider 

switching to a community water supply, if possible, in order to receive water that meets the MCL of 10 

μg/L. Obviously, time and cost are barriers to implementing any of these solutions. 

It should be noted that treatment costs to remove arsenic from private wells can vary. A treatment system 

to reduce arsenic at the kitchen faucet (“point of use”) can be as low as $300, while one to reduce arsenic 

at the entry to the home (“point of entry”) can cost up to $9,000 (Schmidt 2014). These costs can be 

prohibitive to some well owners. Doing a rough “back-of-the-envelope” cost calculation, the cost to 

install point of use treatment in all private wells is approximately $4 billion (assuming three persons per 

well), which is less than the approximately $9 billion dollar damages we found of arsenic in private wells 

in the United States. The point of entry costs for all private wells would be approximately $125 billion for 

all wells, but $8 billion for wells with concentrations above 10 μg/L. Thus, the burden of arsenic falls in 

the range of feasible treatment costs. 

Pre-Publication Version 
References 

Abt Associates Inc. September 22, 2014 ▌pg. R-1

6. References

Abhyankar, L. N., M. R. Jones, et al. (2012). "Arsenic exposure and hypertension: a systematic 

review." Environ Health Perspect 120(4): 494-500. 

Abt Associates Inc. (2000). Arsenic in Drinking Water Rule Economic Analysis. Washington D.C., 

Office of Drinking Water and Ground Water, EPA. EPA 815-R-00-026. 

Abt Associates Inc. (2010). SafeWater CBX: The Benefit and Cost Model for Drinking Water 

Regulations, Model Documentation. Prepared for the U.S. EPA Office of Ground Water and 

Drinking Water. 

Adamowicz, W., D. Dupont, et al. (2007). Valuation of Cancer and Microbial Disease Risk 

Reductions in Municipal Drinking Water: An Analysis of Risk Context Using Multiple 

Valuation Methods. Washington DC, Resources for the Future. 

American Cancer Society. (2013, 1/17/2013). "Skin Cancer: Basal and Squamous Cell." Retrieved 

January 16, 2014, from http://www.cancer.org/cancer/skincancerbasalandsquamouscell/detailedguide/index. 

ATSDR (2007). Toxicological Profile for Arsenic. 

Avritscher, E. B., C. D. Cooksley, et al. (2006). "Clinical model of lifetime cost of treating bladder 

cancer and associated complications." Urology. 68(3): 549-553. Epub 2006 Sep 2018. 

CalEPA (2004). Public Health Goals from Chemicals in Drinking Water. 

Centers for Disease Control and Prevention, N. C. f. H. S. (2014). "Underlying Cause of Death 1999-

2011 on CDC WONDER Online Database." Retrieved September 9, 2014, from 

http://wonder.cdc.gov/ucd-icd10.html

Chen, Y., J. H. Graziano, et al. (2011). "Arsenic exposure from drinking water and mortality from 

cardiovascular disease in Bangladesh: prospective cohort study." BMJ 342: d2431. 

Cohen, S. M., L. L. Arnold, et al. (2013). "Evaluation of the carcinogenicity of inorganic arsenic." 

Critical reviews in toxicology 43(9): 711-752.

Cosmetic Ingredient Review Expert Panel (2007). "Final report on the safety assessment of 

AloeAndongensis Extract, Aloe Andongensis Leaf Juice,aloe Arborescens Leaf Extract, Aloe 

Arborescens Leaf Juice, Aloe Arborescens Leaf Protoplasts, Aloe Barbadensis Flower 

Extract, Aloe Barbadensis Leaf, Aloe Barbadensis Leaf Extract, Aloe Barbadensis Leaf 

Juice,aloe Barbadensis Leaf Polysaccharides, Aloe Barbadensis Leaf Water, Aloe Ferox Leaf 

Extract, Aloe Ferox Leaf Juice, and Aloe Ferox Leaf Juice Extract." Int J Toxicol 26 Suppl 2: 

1-50. 

Cullen, A. C. and H. C. Frey (1999). Probabilistic techniques in exposure assessment: a handbook for 

dealing with variability and uncertainty in models and inputs, Springer. 

Dangleben, N. L., C. F. Skibola, et al. (2013). "Arsenic immunotoxicity: a review." Environmental 

Health 12(1): 73. 

DeSimone, L. A., P. A. Hamilton, et al. (2009). Quality of water from domestic wells in principal 

aquifers of the United States, 1991–2004—Overview of major findings. USGS. U.S. 

Geological Survey Circular 1332: 48 p. 

EPA. (1988, 02/01/1993). "Arsenic, inorganic (CASRN 7440-38-2)." Integrated Risk Information 

System Retrieved August 5th, 2014, from http://www.epa.gov/iris/subst/0278.htm. 

EPA (1988). Special Report on Ingested Inorganic Arsenic: Skin Cancer; Nutritional Essentiality. 

EPA (2010). Toxicological Review of Inorganic Arsenic in Support of Summary Information on the 

Integrated Risk Information System (IRIS) (draft). 

EPA (2012). Arsenic, inorganic. Integrated Risk Information System (IRIS) Database. 

EPA (2014). IRIS Public Science Meeting (June 2014) Meeting Materials. Arlington, VA. 

EPA. (2014, 4/28/2014). "Risk Assessment Glossary." Retrieved September 8, 2014, from 

http://www.epa.gov/risk_assessment/glossary.htm.

Pre-Publication Version 
References 

Abt Associates Inc. September 22, 2014 ▌pg. R-2

EPA Office of Water (2012). Occurrence Technical Support Document for Regulated Volatile 

Organic Compound Carcinogens. 

García-Esquinas, E., M. Pollán, et al. (2013). "Arsenic exposure and cancer mortality in a US-based 

prospective cohort: the Strong Heart Study." Cancer Epidemiology Biomarkers & Prevention 

22(11): 1944-1953. 

Gilbert-Diamond, D., Z. Li, et al. (2013). "A population-based case-control study of urinary arsenic 

species and squamous cell carcinoma in New Hampshire, USA." Environmental Health 

Perspectives 121(10): 1154-1160. 

Helsel, D. (2010). "Much ado about next to nothing: incorporating nondetects in science." Annals of 

occupational hygiene 54(3): 257-262. 

Hertz-Picciotto, I. (1995). "Epidemiology and quantitative risk assessment: a bridge from science to 

policy." Am J Public Health 85(4): 484-491. 

Hoyert, D. L. (2012). Deaths: Preliminary Data for 2011. N. C. f. H. S. Centers for Disease Control 

and Prevention. National Vital Statistics Reports 61.

IARC (2004). Arsenic in Drinking-Water. IARC Monographs on the Evaluation of Carcinogenic 

Risks to Humans. 

IARC (2012). Arsenic and Arsenic Compounds. IARC Monographs on the Evaluation of 

Carcinogenic Risks to Humans. 

Kumar, A., P. Adak, et al. (2010). "Arsenic exposure in US public and domestic drinking water 

supplies: a comparative risk assessment." Journal of Exposure Science and Environmental 

Epidemiology 20(3): 245-254. 

Lim, S. S., T. Vos, et al. (2013). "A comparative risk assessment of burden of disease and injury 

attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic 

analysis for the Global Burden of Disease Study 2010." The lancet 380(9859): 2224-2260. 

Maull, E. A., H. Ahsan, et al. (2012). "Evaluation of the association between arsenic and diabetes: a 

National Toxicology Program workshop review." Environ Health Perspect 120(12): 1658-

1670. 

McGill, N. (2014). "US trees remove tons of pollution, save hundreds of lives, study says." The 

Nation's Health 44(7): E36. 

Miller, J. A. (1999). Groundwater Atlas of the United States, U.S. Geological Survey. HA 730-G.

Moon, K., E. Guallar, et al. (2012). "Arsenic exposure and cardiovascular disease: an updated 

systematic review." Curr Atheroscler Rep 14(6): 542-555. 

Moon, K. A., E. Guallar, et al. (2013). "Association between exposure to low to moderate arsenic 

levels and incident cardiovascular disease: a prospective cohort study." Annals of internal 

medicine 159(10): 649-659. 

Morales, K. H., L. Ryan, et al. (2000). "Risk of internal cancers from arsenic in drinking water." 

Environmental Health Perspectives 108(7): 655. 

Mostafa, M. and N. Cherry (2013). "Arsenic in drinking water and renal cancers in rural Bangladesh." 

Occupational and environmental medicine 70(11): 768-773. 

NAS (2013). Critical Aspects of EPA's IRIS Assessment of Inorganic Arsenic. 

Naujokas, M. F., B. Anderson, et al. (2013). "The broad scope of health effects from chronic arsenic 

exposure: update on a worldwide public health problem." Environ Health Perspect. 121(3): 

295-302. doi: 210.1289/ehp.1205875. Epub 1202012 Dec 1205821. 

Navas-Acien, A., E. K. Silbergeld, et al. (2008). "Arsenic exposure and prevalence of type 2 diabetes 

in US adults." Jama 300(7): 814-822.

Nielsen, M. G., P. J. Lombard, et al. (2010). Assessment of Arsenic Concentrations in Domestic Well 

Water, by Town, in Maine, 2005-09. 

NTP (2011). Arsenic and Inorganic Arsenic Compounds. Report on Carcinogens. Twelfth Edition. 

Pre-Publication Version 
References 

Abt Associates Inc. September 22, 2014 ▌pg. R-3

Oberoi, S., A. Barchowsky, et al. (2014). "The global burden of disease for skin, lung and bladder 

cancer caused by arsenic in food." Cancer Epidemiology Biomarkers & Prevention: cebp. 

1317.2013. 

Ries, L. A. G. and M. P. Eisner (2007). Chapter 9: Cancer of the Lung. SEER Survival 

Monograph:Cancer Survival Among Adults: U.S. SEER Program, 1988-2001, Patient and 

Tumor Characteristics. L. A. G. Ries, J. John L. Young, G. E. Keelet al. Bethesda, MD, 

National Cancer Institute, SEER Program. NIH Pub. No. 07-6215. 

Schmidt, C. W. (2014). "Low-dose aresenic: in search of a risk threshold." Environmental Health 

Perspectives 122(5): A130. 

Seidler, A. M., A. M. Bayoumi, et al. (2012). "Willingness to pay in dermatology: assessment of the 

burden of skin diseases." J Invest Dermatol. 132(7): 1785-1790. doi: 

1710.1038/jid.2012.1750. Epub 2012 Mar 1715. 

Tseng, W.-P. (1977). "Effects and dose-response relationships of skin cancer and blackfoot disease 

with arsenic." Environmental Health Perspectives 19: 109. 

Tseng, W., H. M. Chu, et al. (1968). "Prevalence of skin cancer in an endemic area of chronic 

arsenicism in Taiwan." Journal of the National Cancer Institute 40(3): 453-463. 

U.S. Census Bureau (2001). 2000 U.S. Census of Population and Housing. Washington, DC. Data at 

the state level by age and sex. 

U.S. Environmental Protection Agency (1999). The Benefits and Costs of the Clean Air Act: 1990 to 

2010, EPA Report to Congress. Washington DC, U.S. EPA. 

U.S. Geological Survey (2014). National Water Information System (NWISWeb) [Groundwater 

1992-2004]. U.S. Geological Survey Database. 

U.S. Geological Survey. (2014, April 1, 2014). "Water Use in the United States." Retrieved August 

5, 2014, from http://water.usgs.gov/watuse/. 

Vlaanderen, J., R. Vermeulen, et al. (2008). "Guidelines to evaluate human observational studies for 

quantitative risk assessment." Environ Health Perspect 116(12): 1700-1705. 

Ward, M. H., T. M. DeKok, et al. (2005). "Workgroup report: Drinking-water nitrate and healthrecent findings and research needs." Environmental Health Perspectives: 1607-1614. 

WHO. (2001). "Water-related disease: Arsenicosis." Retrieved 10/21/13, from 

http://www.who.int/water_sanitation_health/diseases/arsenicosis/en/. 

WHO (2011). Arsenic in Drinking Water: Background document for development of WHO 

Guidelines for Drinking-water Quality. 

Wisnivesky, J. P., D. Yankelevitz, et al. (2005). "Stage of lung cancer in relation to its size: part 2. 

Evidence." Chest 127(4): 1136-1139. 

Yabroff, K. R., W. F. Lawrence, et al. (2004). "Burden of Illness in Cancer Survivors: Findings From 

a Population-Based National Sample." Journal of the National Cancer Institute 96(17): 9. 

Yeh, S. (1973). "Skin cancer in chronic arsenicism." Hum Pathol. 4(4): 469-485. 

Pre-Publication Version