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 N at ional 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.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

### 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

N A WQA 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

### 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 the se disease cases.

Ar senic 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

### 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 o f the skin cancer (basal and squamous cell) and this type of cancer is usually nonfatal.

### 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 CancerCasesDamages (millions of $)



### Health Endpoints Fatal Cases

Non-Fatal Cases
Damages (millions of $)

### 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.

### 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.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, w e used “V alue per Statistical L ife ” estimates for fatal cases and cost-of-illness estimates for non-fatal cases of these four health effects. To the extent possible, w e 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.

### 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.

### 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.

### 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 re ports 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

### 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 Wel ls



### 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. T he se data allowed us to model the entire distribution of arsenic concentrations in private wells.

For the contiguous United States, 2 

### the 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 pu blicly 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.

### 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.

### Approach

Abt Associates Inc. September 22, 2014 ▌ pg. 2-4 Table 2-1: Summary Statistics on NA WQA 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) 95 th

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

### 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 concentrationswas 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 OpenBUGS 5 

### 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 N ational 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.

### 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 ) . T he 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 Effect s

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 dozennon-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 ).

### 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 dose

response 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 (DWUR
annu al 

### ) 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 (DWUR annual 

### ) using se x -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:

### 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

DWUR annual 

### in Table 3-2 ; life expectancy is 81.1 for females and 76.3 years for males ( Hoyert 2012 )

= Do mestic 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

.

### 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 Can cer 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

“ co mbined ” (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 tha t 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 epiderm is (top layer of skin), while “invasive” means that the cancer cells have invaded the dermis (middle layer of skin).

### 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 = e β Δ X



### , where β is the risk coefficient . Thus, the risk coefficient can be calculated as ln(HR)/ Δ

X. 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 se lf

-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

### 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 term s “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 th e 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 approach es 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



### Mo rtality risk reductions are valued using EPA’s VSL estimate ( U.S. Environmental Protection Agency 1999 ), which is defined as “how m uch 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.

### 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 adjust ed 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 ad dition, 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.

### Approach

Abt Associates Inc. September 22, 2014 ▌ pg. 2- 13 Figure 2-3: Cost- of -Illness Approach



### 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

### 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

tre atment 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.

### 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.

### 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

### 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 region s. 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 s olid line represents the central estimate of the distribution, whil e the grey lines correspond to 1, 000 distributions reflecting estimation uncertainty. The vertical dashed line at 1 μ g/L represents the minimum reporting level.

GWA Region B GWA Region C

### 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 s olid line represents the central estimate of the distribution, whil e the grey lines correspond to 1, 000 distributions reflecting estimation uncertainty. The vertical dashed line at 1 μ g/L represents the minimum reporting level.

GWA Region D GWA Region E

GWA Region F GWA Region G

### 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 s olid line represents the central estimate of the distribution, whil e the grey lines correspond to 1, 000 distributions reflecting estimation uncertainty. The vertical dashed line at 1 μ g/L represents the minimum reporting level.

GWA Region H GWA Region I

GWA Region J GWA Region K

### 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 s olid line represents the central estimate of the distribution, whil e the grey lines correspond to 1, 000 distributions reflecting estimation uncertainty. The vertical dashed line at 1 μ g/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.

### 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 ) ( % )CentralEstimate(b)90%CredibleIntervalCentral(b)Estimate90%CredibleIntervalCentral(b)Estimate90%CredibleInterval

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 t wo 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.

### 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 DWUR annual (per

μg/L) 2.92E

- 06 a

3.41E

- 06 a 17.1% fatal; 74.9% non

- fatal/non

invasive; 8.1% non

- fatal/invasive

Lung

cancer incidence Females

Males DWUR annual (per

μg/L) 4.69E

- 06 a

1.98E

- 06 a 81.1% fatal; 16.8% non

fatal/Stage I; 2.1% non

fatal/Stage II

Skin

cancer incidence Females

Males DWUR annual (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% non

fatal/squamous cell (invasive)

Heart
disease mortality All β (per μg/L) 2.21E

- 3 c Baseline h eart disease mortality

rate (per 100,000) is 27.7 for ages 0

- 59 and 832 for ages 60+ d

DWUR annual : 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 ED 01 estimates in Table 5

- 3 of EPA (2010) as 0.01/ED 01. The lifetime DWUR is calculated from the central OSF using an ingestion rate of 2 L/day and a bodyweight of 70 kg. The DWUR annual is the life time 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 death s 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 = e βΔX

. (d) T he heart disease mortality rates vary by county . T he estimates presented in the table are national

- l evel summaries obtained from CDC WONDER

.



### 3.3 Economic Inputs

As described in Section 2.3 , fatal illnesses we re 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 ).

### 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.

### 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

.

### 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 well s (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 ,0 00 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) U pdated 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 non fatal cancer cases ($/year) Cancer type (lung, bladder, skin) and sub

- type (e.g., invasive, non

- invasive) N S

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 insuffici ent for a full characterization in our modeling effort.

### 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, l ung 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 5 th 

### percentile and 95 th 

### 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 CancerCasesDamages (millions of $)



### Health Endpoints Fatal Cases

Non-Fatal Cases
Damages (millions of $)

### 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.)

### 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)

S kin 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

- 95 th 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.

### 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 (s imilar 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

1 40) and economic damages (over $800 million).

### 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

### 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

### 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.

### 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

cas es 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.

### 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 million 22 

### dollars of benefits ( Abt Associates Inc. 2000 ). When we conducted an additional analysis to estimate the correspondin g 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.

### 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 well s 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

### 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 diseasedeaths 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.

### Discussion

Abt Associates Inc September 22, 2014 ▌ pg. 5-6 

### 5.3 Limitati ons/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 characte rized.

Assumed demographic and
socioeconomic information on private well drinkers. Additional analyses could b etter 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 a nd 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

cons umption 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
consist ently Our estimates had different levels of resolution

– from the county level to the national level. We could try to obtain better resolved estimates.

### 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 c oncern. 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

met hemoglobinemia) ( Ward, DeKok et al. 2005 )

.



### 5.4 C onclusions 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 tocharacterize 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 extentpossible 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.

### References

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

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