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These Articles in Press
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Journal of Environmental Management
Article in Press, Corrected Proof - Note to users
Copyright © 2005 Published by Elsevier Ltd.
Estimating the economic burden from illnesses
associated with recreational coastal water pollution—a
case study in Orange County, California
Ryan H. Dwighta, Linda M. Fernandezb,
and Betty H. Olsone
, , Dean B. Bakerc, Jan C. Semenzad
aEnvironmental Health Science and Policy Program, University of California, Irvine, CA, USA
bDepartments of Environmental Sciences and Economics, University of California, Riverside, CA 92521-0424, USA
cDepartment of Medicine & Center for Occupational and Environmental Health, University of California, Irvine, CA,
dSchool of Community Health, Portland State University, Portland, OR, USA
eEnvironmental Health Science and Policy Program, University of California, Irvine, CA, USA
Received 26 February 2004; revised 22 October 2004; accepted 22 November 2004. Available online 22 April 2005.
A cost-of-illness framework was applied to health and income data to quantify the health
burden from illnesses associated with exposure to polluted recreational marine waters.
Using data on illness severity due to exposure to polluted coastal water and estimates of
mean annual salaries and medical costs (adjusted to 2001 values) for residents of Orange
County, California, we estimated that the economic burden per gastrointestinal illness (GI)
amounts to $36.58, the burden per acute respiratory disease is $76.76, the burden per ear
ailment is $37.86, and the burden per eye ailment is $27.31. These costs can become a
substantial public health burden when millions of exposures per year to polluted coastal
waters result in hundreds of thousands of illnesses. For example, exposures to polluted
waters at Orange County's Newport and Huntington Beaches were estimated to generate an
average of 36,778 GI episodes per year. At this GI illness rate, one can also expect that
approximately 38,000 more illness episodes occurred per year of other types, including
respiratory, eye, and ear infections. The combination of excess illnesses associated with
coastal water pollution resulted in a cumulative public health burden of $3.3 million per
year for these two beaches. This paper introduces a public health cost variable that can be
applied in cost-benefit analyses when evaluating pollution abatement strategies.
Keywords: Valuing morbidity impacts; Coastal; Beach; Water quality; Economic burden of
illness; Recreational marine waters; Gastroenteritis; Acute respiratory disease; Cost of
2.2. Valuation method
2.3. Site-specific example
3.1. Cost per illness
3.2. Cumulative public health cost
A large body of epidemiological research has shown that exposure to recreational waters
contaminated with human and animal waste can result in several types of illnesses including
gastroenteritis (GI), acute respiratory disease (ARD), and eye, ear and skin infections
(Saliba and Helmer, 1990, Prüss, 1998, Haile et al., 1999 and Dwight et al., 2004). These
illnesses are caused by enteric bacteria, viruses and protozoa that are found primarily in
human wastewater, and are not endemic to recreational waters. The majority of these
illnesses are self-limiting and place little demand on the health care system, and yet they are
debilitating to various degrees, which can result in associated costs. Several researchers have
suggested that illnesses associated with coastal water pollution can have significant health
and economic impacts on society (Fleisher et al., 1998, Gerba et al., 1996, Henrickson et al.,
2001, Saliba and Helmer, 1990, Corbett et al., 1993 and Haile et al., 1999). A recent study
estimated that globally polluted coastal waters generate 120 million excess GI episodes and
50 million ARD episodes every year, resulting in $12 billion per year in public health costs
Domestic sewage discharged along coastlines has historically been the primary source of
marine coastal water pollution for recreational bathing beaches in the USA. However, with
few exceptions, most sanitation facilities across the United States have been upgraded to
comply with USA federal law. The attention of policy makers is now being focused on the
potentially larger problem of untreated urban runoff. Urban runoff contains a mixture of
non-point source pollutants that are suspended in water from irrigation runoff, households,
and storm events, as well as contributions of raw sewage from degrading infrastructure and
accidental spills. Research of high density urban and industrial landscapes show infectious
and toxic pollutants are routinely released into runoff waters (Bay and Greenstein, 1996,
Gold et al., 1991 and Field et al., 1993).
Urban runoff is of particular concern in the Southern California region of the USA where
the large population and vast expanse of impermeable surfaces generates large volumes of
polluted runoff water that can discharge onto popular coastal beaches. Untreated urban
runoff is considered the most significant source of water pollution impacting Southern
California's coastal waters, estuaries and bays (Southern California Coastal Water Research
Project, 1991, California Resource Agency, 1997, Schafer and Gossett, 1998 and Noble et
al., 2000; Dwight et al., 2002).
In one large-scale epidemiological study of illnesses associated with polluted recreational
marine waters, researchers measured the severity of the resulting illnesses by evaluating
length of illness, and whether or not the subjects had to see a doctor (Fleisher et al., 1998).
Values were collected for four illness types (GI, ARD, eye and ear infections), and the data
were used to ascertain the severity of the water-associated illnesses. The researchers
concluded that excess illnesses associated with recreational waters have a significant impact
on public health.
Understanding the severity of symptoms and estimating the economic impact from
recreational water-related illnesses is important because the information can aid the decision
making process related to pollution abatement strategies. To illustrate the utility of this
approach, we estimated the economic impact on public health from exposure to polluted
coastal water at two popular beaches.
In order to quantify the health costs per water associated illness, we applied the ‘cost-of-
illness’ framework to health data on illness-related lost activity days and medical care use
generated from an epidemiological study of recreational coastal water users (Fleisher et al.,
1998), and to annual income data and medical care costs for residents of Orange County,
California (USDC, 2004; Nichol, 2001). We then show in an example how these results can
be used to estimate the annual public health burden for recreational beaches by combining
our cost per illness results with published results of a model that estimated the number of
water associated GI illnesses for a specific site (Turbow et al., 2003).
To calculate the cost per water-associated illness, we used the cost-of-illness approach
which focuses on health damages, or costs following the onset of illness (Berger et al.,
1994). This is an attractive method for estimating economic values for morbidity effects
because it uses actual data of financial costs directly measured in a market setting, such as
lost earnings and medical services from illness (Kenkel, 1994).
The variables in the model include estimates of illness severity (proportion of persons with
lost normal-activity-days, and proportion requiring a medical visit per illness episode) for
each type of illness; the average annual salary of the population; the proportion of illnesses
that require professional medical attention; and the medical costs associated with an illness.
Data on illness severity came from the only published measures of illness severity for
illnesses associated with contaminated recreational water (Fleisher et al., 1998). The values
were collected during randomized intervention follow-up epidemiological studies conducted
at four beaches in the United Kingdom. For our cost-of-illness valuation, we only used
health data from subjects classified as bathers, and censored data from non-bathers.
Subjects were adult volunteers (>18 years) and the final cohort (n=548) had a mean age of
32 years; 54% were male. Although several types of illnesses have been associated with
exposure to waterborne pathogens, including some rare but severe illness (Saliba and
Helmer, 1990 and Prüss, 1998), the current study was restricted to GI, ARD, ear and eye
ailment illness outcomes as a conservative estimate of the total burden of illness.
Subjects in the study by Fleisher et al. (1998) were considered to have an illness only if they
reported a composite of certain symptoms known to be associated with a particular illness
lasting for over 24 h, or if there was clinical confirmation of symptoms. Subjects were
classified as having GI if they reported experiencing vomiting or diarrhea, or all cases of
nausea, indigestion, diarrhea or vomiting that were accompanied by a fever. Subjects were
classified as having ARD if they reported experiencing at least one symptom from each of
the following three categories: (1) fever; (2) headache and/or body-aches and/or unusual
fatigue and/or anorexia; and (3) sore throat and/or runny nose and/or dry or productive
cough. Subjects were classified as having an ear ailment if they showed clinical evidence of
ear infection or inflammation by medical examination, or if the subject reported ear pain
with or without discharge. Subjects were classified as having an eye ailment if they reported
incidence of sore, red, eyes with or without discharge.
For each of the four illness types, Fleisher et al. (1998) provides estimates of the proportion
of illnesses that resulted in lost normal activity, from 1 to 3 days (Ai,j, where i=days of lost
normal activity [1,2,3], and j=illness type [GI, ARD, Ear, Eye]), as well as the proportion of
illnesses that required medical care (Cj) (Table 1). These estimates are relevant to the cost-
of-illness model because only a small proportion of exposed persons become ill and only a
relatively small proportion of those who become ill lose a day or more of normal activity or
seek medical care. For example, Table 1 indicates that only 9.3% of persons with a GI
illness episode lose one day of normal activity and only 12% seek any medical care.
Illness severity measures (A and C) for water-associated illnesses
GI ARD Ear Eye
Proportion of illnesses with 1 day lost activity (A1)0.0931,GI
Proportion of illnesses with 2 days lost activity (A2)0.042,GI
Proportion of illnesses with 3 days lost activity (A3)0.0143,GI
Proportion of ill persons that go to a doctor (C)
GI, gastroenteritis; ARD, acute respiratory disease; ear, ear ailments; eye, eye ailments; Dr,
medical attention sought. Data from Fleisher et al. (1998).
Data on income-per-day in 2001 were calculated from the average annual wage in Orange
County of $39,895 in that year (USDC, 2004). This figure was divided by 240 work-days
per year to estimate an income-per-workday of $166.23. Data were reported as days of lost-
normal-activity, but not every day of lost-normal-activity is equal to a day of lost wages.
Therefore, we multiplied ‘income-per-day’ by the proportion of work days in a year (240
work days/365 days), yielding $109.30 income-per-day in Orange County, CA. We did not
include costs from lost leisure time in this analysis due to lack of data. However these costs
should be included in a full valuation model because people invest a lot of time and money
in their vacations, so a disrupted vacation from a recreational-water-related illness can have
greater value loss than just the lost income value.
The medical expense for each illness episode that required a medical visit includes the costs
of physicians' fees, medications and patient co-payments. This value was estimated at
$102.00 per visit in 1998 dollars based on the average value from Nichol (2001). For the
model this was adjusted to $108.98 to account for inflation to the year 2001.
2.2. Valuation method
The cost-per-illness (F ($)) was calculated for each type of illness as follows—Eq. (1) is the
overall model used to calculate the cost-per-illness:
where: L=lost income per illness episode ($) and M=medical costs ($).
The lost income per illness episode term in Eq. (1) was calculated from:
where: Aij is the proportion of exposed persons experiencing i days of lost normal activity
for each illness (j), and D is the income per work day ($).
The proportion of lost days of normal activity for 1, 2, or 3 days (i=[1,2,3]) and illness type
(j=[GI, ARD, Ear, Eye]) for each illness episode are referenced from the epidemiology
study in Table 1. For example, using the data for GI in Table 1, the proportion of lost days
per illness episode is calculated as: (1 day×0.093)+(2 days×0.040)+(3 days×0.014)=0.215
day. This amount is multiplied by the income-per-workday to equal $23.50 in lost income
per GI illness episode. The calculation for each of the four illnesses is shown in Table 2.
Public health cost from coastal water pollution in california's newport and huntington beaches
Item Type of illness
GIARD Ear EyeEstimates
Proportion lost days/illness
1 day 0.0930.0740.0230.042
2 days 0.04 0.1480.0230.083
3 days0.014 0.0370.023 0.00
Total lost days/illnessa
Effective daily incomeb
Lost income/illness$23.50$52.57$15.08 $22.73
Proportion medical visit/illness0.120.222 0.2090.042
Total cost/illness$36.58 $76.76 $37.86$27.31
Illness relative ratesd
Annual GI illness ratee
36,778 per year
Estimated illness costsf
a Total lost days/illness=ΣiAi,j, where i=number of days, Ai,j=proportion of type j illness with lost
activity day (from Fleisher et al., 1998).
b Effective daily income=(Annual Salary/240 work days/year)×(240 work days/365 total days).
Median annual salary in Orange County, CA=$39,755 (USDC, 2004).
c Medical cost/illness, M=CjE, where Cj=proportion of type j illness resulting in medical visit
(from Fleisher et al., 1998) and E=cost of visit. Average cost of medical visit = $108.98 (Nichol,
d Illness relative rates compared to GI illness rates, Rj=Dj/DGI, where Dj=rate of illness type j and
DGI=the rate of GI illness (from Fleisher et al., 1998).
e Annual GI illness incidence estimated for Huntington and Newport Beaches in Orange County
(Turbow et al., 2003).
f Estimated illness costs=GI illness rate×(illness relative rate)j×(total cost/illness)j.
Medical costs (M ($)) for each illness episode are calculated from:
where C is the proportion of exposed persons requiring medical attention for each illness
type (j), and E ($) is the medical expense per visit. The proportion of bather illness episodes
that required a person to seek medical attention is shown in Table 1, derived from Fleisher
et al. (1998). Each of these proportions was multiplied by the average cost of a doctor visit
($108.98) based on Nichol (2001). For example, the average medical cost of a GI illness
episode is equal to (0.012×$108.98) =$13.08 (Table 2).
2.3. Site-specific example
To estimate the health cost to the public from exposure to polluted coastal water, the cost-
of-illness estimate for each type of illness occurrence was multiplied by their respective
estimates of illness episodes for an exposed population at a specific site during a specified
period of time. A recent publication presented a model to estimate number of GI illness
episodes that occurred from 1998 to 2000 at Huntington and Newport Beaches in Orange
County, California (Turbow et al., 2003). These two adjacent recreational beaches are
world-renowned vacation destinations that host an estimated 5.5 million coastal water
exposures annually (County Sanitation Districts of Orange County, 1996).
The sample beaches receive pollution from two primary sources: treated domestic sewage
and untreated urban runoff. The Orange County Sanitation District (OCSD) discharges
around 900 million liters-per-day of treated domestic sewage year-round into the ocean
from an outfall that is 8 km directly offshore from these beaches. Another primary source of
surf zone pollution is the discharge of untreated urban runoff from the Santa Ana River,
which drains over 2600 km2 of one of the world's most highly developed watersheds.
The time period of Turbow et al. (2003) study was a representative period in Southern
California with low average rainfall and relatively low concentrations of faecal indicator
organisms in coastal waters. The parameters used in the model to estimate the number of
illness occurrences included historical enterococcus density data collected by the OCSD
approximately three times per week at 305 m intervals along the beach for a 31-month study
period; population at risk of exposure; and illness risk estimates. The population at risk was
estimated using data on aggregate beach attendance provided through local lifeguard
agencies and fire departments, and reports on the proportion of beachgoers who bathe at
different times of the year at the beaches. The enterococcus density–illness relationship used
in the model was derived from the cohort studies conducted by Cabelli et al. (1983). These
incidence risk estimates were used because they were the basis for the current US EPA
guidelines for recreational water quality in United States. For more information on the water
quality data for these beaches over time and the model parameters, please refer to Turbow et
The illness occurrence model developed by Turbow et al. estimated that 95,010 GI illnesses
occurred from coastal water pollution at these two beaches during the study period (31
months). That is equivalent to 36,778 GI illnesses per year from recreational water use in
Newport and Huntington Beaches during a typical year. This suggests the coastal waters in
this area generate a persistent low level of unreported illnesses that results in tens of
thousands of illness events annually. With this estimate of the annual number of GI illness
events, the cumulative public health burden can be calculated by multiplying the cost-per-
illness by the estimated number of illness episodes per year (Table 2).
Data from Turbow et al. (2003) related only to GI illnesses. In order to calculate the public
health costs associated with the other illness types, study data from Fleisher et al. (1998)
was used to calculate the relative rates (Rj) for each of the other illness.
3.1. Cost per illness
Table 2 shows the estimated cost per illness calculated using the cost-of-illness model. The
estimated health costs are $36.58 per GI episode, $76.76 per ARD episode, $37.86 per ear
ailment, and $27.31 per eye ailment.
3.2. Cumulative public health cost
Health costs associated with individual illness episodes are a substantial public health
burden when extrapolated to popular recreational beaches that attract millions of recreators
per year. Table 2 indicates a $1.3 million public health cost from GI illnesses associated
with polluted recreational waters at Newport and Huntington Beaches in a typical year. The
public health cost from ARD episodes is $951,378 per year, from ear ailments is $767,221
per year, and from eye ailments is $304,335 per year. The annual cumulative public health
burden for these two beaches is equal to $3.3 million.
The US EPA's current water quality standard for recreational marine waters, which was
recently adopted by California, has a threshold illness rate for GI of 1.9% (Cabelli, 1989).
This water quality criterion was adopted by the US EPA based on a series of
epidemiological studies conducted over six years at beaches in Boston Harbor, New York
City, and a lake in Louisiana (Cabelli et al., 1983). Therefore, we also calculated the health
costs that would occur if the recreational coastal waters of Huntington and Newport Beaches
were to exactly meet the accepted US EPA and California State standard for indicator
bacteria for a 1-year period, thereby calculating the cost of the current standard at a popular
recreational beach. Under these assumed conditions, one would expect a GI illnesses rate of
1.9% (Cabelli, 1989), or an estimated 78,515 excess GI episodes per year (Turbow et al.,
2003). Using this GI illness rate, the cumulative public health cost for GI illnesses would be
$2.87 million per year. The annual public health burden would be $2.03 million for ARD,
$1.63 million for ear ailments, and more than $649,000 for eye ailments. The total public
health cost for Newport and Huntington Beaches would be greater than $7 million annually,
if coastal water quality complied exactly with US EPA standards for an entire year.
The United States spends considerable resources to ensure that food and drinking water
meet stringent health and safety standards for pathogens and toxins to the point that they
pose a relatively low health risk. However, water quality standards for recreational waters,
especially marine waters, are much less stringent with an acceptable risk level of 1.9 GI
illnesses/100 exposures (Cabelli, 1989). Although people do not drink ocean water on
purpose, accidental ingestion does occur frequently when undertaking recreational activities
in the ocean.
GI illnesses have been the primary illness investigated in epidemiology studies of drinking-
water associated illnesses because that is the most prominent illness that results from
drinking polluted water. However, when people are immersed in polluted recreational water,
they can contract a range of illnesses because all portals of entry into the body are exposed
when swimming. Therefore, the US EPA's acceptable rate for GI illness of 1.9% in
recreational marine waters should actually be considered conservatively as an illness rate of
1.9% GI+0.64% ARD+1.05% Ear+0.58% Eye, for an overall illness rate of 4.17% when
water quality is at the accepted standard.
For the purpose of data validation, we conducted a search of the following databases
MEDLINE, BIOSIS, and ABI/INFORM Global for peer-reviewed publications on GI illness
severity and cost-of-illness. The most relevant citations are summarized in Table 3. Only a
few water-related studies measured some of the variables used in our cost-of-illness model.
Therefore, we also investigated studies of GI illnesses attributed to causes other than
contaminated water. This was done because the source of an infectious agent should not
influence the severity of the resulting illness. This assumption is supported by an
epidemiological study where subjects reported no statistical difference between water-
exposed and unexposed subjects in the duration of illnesses, illness percentages seeking
medical attention, and time lost from normal daily activity (Fleisher et al., 1998).
Comparative measures of illness severity for gastroenteritis
Fleisher et al., 1998a
4.1 (mean) 0.201At least 1 day 0.12
Lost normal activity
Cabelli et al., 1979a
nr0.50 of cases stayed home or saw a doctor
Garthright et al., 1988nr0.521 day 0.083
Gerba et al., 1996a
Hardy et al., 1994b
5.4 (mean)nrnr 0.10
Payment et al., 1991a
1.9 (mean)nr nrnr
Wit et al., 2000c
nr0.15 2 days (median)0.22
Wit et al., 2001c
6 (median) 0.603.1 days (mean)0.20
a Studies of water-associated illnesses.
b Studies conducted on children.
c Studies of non-water-associated illnesses.
No previous cost-per-illness studies have been conducted on illnesses associated with
polluted recreational waters, so in order to review the evidence, a literature search of cost-
per-illness investigations of illnesses from other sources such as food-borne infections was
undertaken. Table 4 lists the citations that provided a calculated cost-per-illness; however,
there are important methodological differences between citations in the types of subjects
they studied and the complexity of the models they used. The estimated costs-per-illness
values generated in this study are supported by a comparison to cost-per-illness values
generated in the other studies, and even suggest that our results may be low by a factor of
ten. This is due to the restrictive cost values used in our model, as well as from the
restrictive model itself, which has only two health cost variables: Lost income (L) and
Medical costs (M).
Comparative cost-per-illness citations
Our results with data from Fleisher et al, 1998a
Frühwirth et al., 2001b
Garthright et al., 1988d
Hardy et al., 1994b
Liddle et al., 1997c
Scott et al., 2000
Acute respiratory disease
Our results with data from Fleisher et al., 19981
Carabin et al., 1999lk and lk
Ray et al., 1999
$34–1,978e (medical costs only)
a Investigated water-associated illnesses.
b Investigated children.
c Published results in foreign currency were converted to US dollars at the year of publication, and
then adjusted to 2001 dollar values due to inflation.
d Investigated all vectors including waterborne pathogens.
e Published results were adjusted to 2001 US dollar values due to inflation.
f Investigated Gastroenteritis and Acute Respiratory Disease illnesses combined.
Our cost-per-illness model was limited in comparison to Scott et al. (2000), who
investigated many types of costs associated with food-borne infections. Their model
included direct medical costs, direct non-medical costs, indirect costs of lost productivity,
and intangible costs of impaired quality of life. The researchers found the largest cost
component was lost income (87.4%), which was the primary component in our model.
Not included in our cost-per-illness model, due to absence of data, are the personal out-of-
pocket expenses associated with having a prescription filled after a doctor visit, or the costs
of self-medication. The majority of the illnesses being investigated (88% for GI) do not
necessitate a visit to the doctor or hospital, so symptom relief is primarily left to the
individual. When ill, many people purchase some form of medication, whether a
pharmaceutical product or chicken soup. The US Centers for Disease Control and
Prevention reports that a large unmeasured portion of the personal costs associated with the
common cold are for over-the-counter medications (Centers for Disease Control and
Also, we did not include the costs associated with rare and severe illnesses such as hepatitis,
which can have very high associated health costs, including, perhaps, mortality costs in
addition to morbidity costs. The rates and severity of the rare and severe illnesses associated
with polluted recreational waters are not well understood, and should be considered in
In the literature there is some discrepancy as to the value of the Ai,j term, which can have a
pronounced effect on the cost-per-illness estimates produced. Shown in Table 3; Wit et al.
(2000) reported that a low 15% of GI illnesses resulted in lost work-days, while both Wit et
al. (2001) and Garthright et al. (1988) reported greater than 50% of subjects lost at least one
day of work from being ill with GI. Cabelli et al. (1979) reported ‘about 50% of cases either
stayed home or had to visit a doctor’, however, we were not able to use this result in our
model because stay-at-home days was not segregated from doctor visits. There is also a
range in the reporting of [Cj] with a low of 8.3% of patients seeing a doctor (Garthright et
al., 1988), and a high of 22% of patients seeing a doctor from a GI illness (Wit et al., 2000)
During the construction of our cost-per-illness model, we considered a range of potential
influences. For example, the Santa Monica Bay epidemiological study reported 48% of
summer beach-swimmers are children 12 years or younger (Haile et al., 1996). With half of
the exposed population being children, one might think the estimated lost wages would
decrease because children are typically not employed. However, studies at day-care centers
have found that a sick child requires a caretaker to remain at home from work, and thus
results in loss of productivity, as if the caretaker were ill themself (Hardy et al., 1994,
Liddle et al., 1997 and Carabin et al., 1999). This scenario does not hold true when one of
the parents is a stay-at-home caretaker.
We tried to consider the fact that many people receive paid sick-leave when they stay at
home from work (Kenkel, 1994). With no survey data on the subjects who received paid
sick-leave to use in our calculations, we considered illnesses to result in either lost income
unadjusted for paid sick leave, or an equivalent lost opportunity cost because paid sick-leave
would be unavailable at a later time. Further, if a sick person is paid for time not worked,
then the economic burden is simply shifted to the employer who pays for a day of non-
We were also restrictive in several respects in our site-specific public health burden
example shown in Table 2. First, we used lower-bound cost-per-illness estimates generated
using data from Fleisher et al. (1998), as opposed to higher values produced in other studies
of non-water associated illnesses (Table 4). Second, we used conservative relative illness
rate ratios for the different illness types (Rj) (Fleisher et al., 1998), as opposed to using
higher illness rates for ARD found in other studies. Third, we incorporated results from
Turbow et al. (2003) which used the US EPA risk model to generate an annual GI illness
The ratio of illness rates (Rj) for ARD compared to GI (Fleisher et al., 1998) should be
considered conservative because of the restrictive criteria the investigators used to define the
illnesses. Several epidemiological studies with less restrictive definitions have found that
respiratory symptoms are more commonly reported from exposure to polluted waters than
GI symptoms (Prüss, 1998, Haile et al., 1999 and Dwight et al., 2004). Some studies
reported respiratory illnesses rates twice that of GI rates (Seyfried et al., 1985 and Haile et
al., 1999). Had we used these higher rates of respiratory illness in our public health burden
example, the resulting estimated costs for ARD would be six times greater.
The relative ratios of illness rates (Rj) for the four illness types were generated by subjects
exposed to a known source of domestic sewage (Fleisher et al., 1998). Each pollution source
has its own profile of pathogens; therefore the ratios of the different illnesses (Rj) should
change relative to exposure to different pollution sources (i.e. raw or treated domestic
sewage, urban or agricultural runoff, boat bilge). Studies of subjects exposed to untreated
urban runoff from Southern California produced an illness rate ratio (Rj) (Haile et al., 1999
and Dwight et al., 2004) that differs from the (Rj) reported by subjects exposed to treated
domestic sewage (Fleisher et al., 1998). Basically, Rj is different for each of the numerous
epidemiology studies. When comparing illness ratios (Rj) between exposure to treated
domestic sewage versus exposure to untreated urban runoff, urban runoff (Haile et al., 1996
and Dwight et al., 2004) can generate twice as many sinus/respiratory infections than GI
infections, as well as higher rates of ear and skin infections, as can result from exposure to
treated domestic sewage. However, some of these illness rates may be an artifact due to the
different criteria used for illness classification by the various studies. Much additional
research is needed in this area. For example, do illness rates and ratios for a given faecal
indicator concentration vary according to the provenance of contamination (e.g. treated
sewage cf. untreated urban runoff).
Further, our public health burden estimates are most likely conservative because the
estimate of annual GI occurrences used in our example was generated using a risk model
(Turbow et al., 2003) that includes GI illness rates based on Cabelli et al. (1983), which has
a less steep entercoccus density-GI illness dose-response than that derived from the studies
by Fleisher et al. (1996), which forms the basis of the risk model recently adopted by the
World Health Organization (WHO, 2003). The WHO risk model was developed using
results by Kay et al., 1994 and Fleisher et al., 1996, and is detailed in Kay et al. (2004). Had
Turbow et al. (2003) used the newly adopted risk model, the annual GI illness rate would
have been greater, and consequentially would have produced a greater annual public health
Several studies have also reported that children are more susceptible to water-borne
pathogens than adults (Prüss, 1998, Saliba and Helmer, 1990, Gerba et al., 1996 and Haile et
al., 1999). Similarly, tourists not exposed to local pathogens also report higher illness rates
than local populations (Prüss, 1998). Consideration of these factors in a risk model would
increase the public health cost estimates.
For an upper-bound comparison to our public health burden results, we used data from
Nichol (2001), who reported the cost-per-illness for influenza-type symptoms is equal to
$387.33 per episode (Table 4). This value is much greater than the estimated cost-per-
illness values we report. With this higher cost-per-illness value used in our example at
Newport and Huntington Beaches in Table 2, we would expect a cumulative public health
burden greater than $14.2 million per year for GI illnesses alone. This value is significantly
greater than the $1.3 million we report for GI's cumulative public health burden.
For ARD we found a comparative study that reported a cost-per-illness for ARD of $409
per episode (Carabin et al., 1999) (Table 4). This value is five times greater than that
estimated in Table 2. When this upper-bound cost-per-illness value is multiplied by the
number of illnesses used in our applied example in Table 2, a cumulative public health cost
for ARD illnesses would be greater than $5 million per year.
It is well established that exposure to polluted recreational waters can pose a significant
public health risk (Saliba and Helmer, 1990 and Prüss, 1998). These water-related illnesses
can have significant associated symptoms and can be of great public health consequence
(Fleisher et al., 1998). Our analysis quantified the health costs associated with water-
pollution-related illnesses and found these illnesses can result in a significant financial
burden to the individual who contracts an illness.
Furthermore, these costs accumulate into large public health burdens for recreational beaches
that attract millions of people per year. From the example in Table 2 using only two
Southern California beaches, with water quality well within the US EPA and California
State water quality standard, we estimated that millions of dollars per year in health costs
are incurred from a range of illnesses suffered by the ocean-swimming public.
The cumulative health burden example is based on only one small area (less than 14 km) of
California's vast coastline, which is extensively used by the recreating public. Similar public
health costs are most likely being incurred at other public beaches across the country where
hundreds of millions of people annually enjoy the nations' recreational waters. This analysis
shows that even when coastal water bacterial levels are well below the current standard, the
resulting health risk can produce a large health and economic burden. Therefore, from both
a public health and an economic perspective, we recommend that the current accepted
illness rate should be re-evaluated. In defense of the current standard, it was created as a
suggested minimum with the expectation that local health officials would enact more
restrictive standards to protect the public (Cabelli, 1989).
Values generated in this study may be most helpful for decision-makers conducting cost-
benefit analysis of pollution control options. Public officials are aware of the high costs
associated with pollution controls and law enforcement. They also know that closed beaches
due to pollution have substantial associated costs in lost recreational values and lost
business revenues. Until now, these were the only economic parameters decision makers
had for their consideration. The addition of a public health value helps identify the more
accurate overall costs that are associated with coastal water pollution, which may lead to a
wider array of pollution control options.
For example, in Southern California's Orange County, officials were concerned about the
cost of a $350,000 diversion project that pumps and siphons 2.5 million gallons per day of
urban runoff (representing 95% of the contaminated dry-flow runoff to Huntington state and
city beaches) into the sewage treatment facility for the summer months (Reyes, 2001).
Untreated urban runoff discharged onto these beaches has been shown to be a major source
of pollution in the recreational coastal waters throughout the year (Noble et al., 2000 and
Dwight et al., 2002). Results from this study show that if the overall illness rate is decreased
even slightly by the diversion, the annual financial benefit to the public could amount to
millions of dollars. Decision makers will be able to justify large project costs for pollution
control measures or source-reduction policies by the greater public benefit gained by taking
preventative action. Reduction of non-point source pollution is a valuable component of an
integrated strategy to protect public health (Gaffield et al., 2003). However, these health
burden values in a risk-benefit analysis are not large enough in and of themselves to justify
funding hundreds of millions of dollars for pollution prevention programs.
Research is needed to obtain data on actual lost income per illness, and other medical and
non-medical costs associated with the illnesses, by eliciting direct values from surveyed
individuals. When possible, epidemiology investigations of water-associated illnesses
should include follow-up cost-of-illness and contingent valuation questions needed to
determine the site-specific health costs. Research is also needed to better define Rj
adjustments (ratio for the different illness types) that are generated by exposure to different
pollution sources and different concentrations. We present this cost-per-illness model not as
a definitive cost projector, but as a template to be augmented by future investigations.
We are grateful to Dr Harvey Molotch, and the U.C. Toxic Substances Research and
Teaching Program for their early financial support. We are also grateful to Dr David
Turbow, Dr Jay Fleisher, Dr Judith Kildow, Dr Nancy Lee, Dr JoAnne Prause and Dr Dele
Ogunseitan for all their contributions.
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