Quantitative Health Risk Assessment of Cryptosporidium in Rivers of

ARTICLE pubs.acs.org/est

Quantitative Health Risk Assessment of Cryptosporidium in Rivers of Southern China Based on Continuous Monitoring Wei An,† Dongqing Zhang,† Shumin Xiao,†,‡ Jianwei Yu,† and Min Yang*,† †

State Key Laboratory of Environmental Aquatic Chemistry, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, P.O. Box 2871, Beijing 100085, China ‡ College of Emergency Management, Henan Polytechnic University, Jiaozuo 454003, China

bS Supporting Information ABSTRACT: The concentrations of Cryptosporidium in the source water of several cities of Zhejiang Province, China were determined to be in the range of 0
17 oocysts/10 L in the rainy season in 2008, with a mean value of 7 oocysts/10 L. Based on the investigation data, comprehensive risk assessment of Cryptosporidium infection was performed by considering different water intake routes as well as water consumption. Intakes of unboiled tapwater (including drinking and tooth-brushing and food and dish washing) and source water (through swimming in rivers) were estimated to be 2.59
25.9 and 0.32
0.74 L/ year
person, respectively. The mortality due to Cryptosporidium infection for people in this region, excluding HIV-infected patients, was calculated as 0
0.0146 per 105 persons using a conditional probability formula. Disability-adjusted life years (DALYs) were used to quantify the risk of Cryptosporidium infection, for which uncertainty was analyzed. For people who consumed conventionally treated water, the DALYs due to Cryptosporidium infection were 6.51 per 105 (95% CI: 2.16  10
5
22.35  10
5) persons, which were higher than a risk judged acceptable by some (1.97  10
5 DALYs per year), and the risk for those consuming ozone-treated water became 0.0689  10
5 DALYs per year. The major risk of infection resulted from swimming in the river. This study provides a method to establish the risk of Cryptosporidium infection and optimize the scheme for reducing the risk effectively, which is useful for the modification of water quality standards based on cost utility analysis given use of DALYs.

’ INTRODUCTION Cryptosporidium is considered to be one of the major health risks for gastroenteritis in developed and developing countries.1 As of year 2000, there were 15 documented outbreaks of waterborne Cryptosporidium in the America alone.2 These have infected hundreds to thousands of tap water drinkers in different countries including the United Kingdom,2 Australia and Ireland,3 with a mortality rate as high as 70% in HIV-infected patients.4 In addition, swimming was also regarded as an important transmission route due to getting water in mouth, and swallowing water, which can enhance the rate of person
person transmission.5,6 In some countries, cryptosporidiosis is listed as a notifiable disease.7 Thus, risk assessment of Cryptosporidium has been used as an important tool in many countries to help make environmental decisions to control its health effects.7
11 Several studies have highlighted the health risk assessment of Cryptosporidium using the annual individual probability of infection7,9,12 or disability-adjusted life year (DALY) as an assessment measure.4 DALYs have the advantage of integrating r 2011 American Chemical Society

the different adverse health effects such as mortality and nonfatal health effects, using time as a uniform unit of measurement. In 2000, Havelaar et al. (2000) estimated DALYs accumulated renal cell cancer due to bromate and infection with Cryptosporidium to compare the risks and benefits of drinking water disinfection.4 DALYs were calculated as the sum of the years of life lost due to premature mortality in the population and the equivalent healthy years lost due to disability for incident cases of the disease.13 The major symptom of Cryptosporidium infection is diarrhea, which can directly cause death. In 2007, there are maximum fatality rates about 8.3  10
6 occurred in the children age groups of 1
5 years with gastroenteritis symptom in China.14 However, previous estimates of cryptosporidiosis-associated premature mortality based on death certificate reporting Received: November 30, 2010 Accepted: April 18, 2011 Revised: April 1, 2011 Published: May 10, 2011 4951

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Table 1. Summary of Equations and Description of the Parametersa equation 1

exponential distribution model

equation 2

Beta-Poisson distribution model

equation 3

activity proportion of Cryp.

Pin ¼ 1
e
r

D

ð1Þ

D: is the daily ingested dose of Cryp.


 D
R Pin ¼ 1
1 þ β Pm ¼ e
d þ

Pin: the probability of infection; r: a scale factor for the dose;

ð2Þ

a þ bx
c

e 1 þ ea þ bx

ð3Þ

R and β: constant coefficients with a precondition of R, β and β.1; This represents heterogeneity in the host-pathogen interaction.19,53 Pm: the probability of any single organism successfully passes all immunity barriers; default value of coefficient (a,b,c,d): 3.56, -53.2, 1.64, and 2.12;54 x: preexisting anti-Cryp. IgG levels

equation 4

n

∑i di ei

ð4Þ

i: the age of subpopulation; di: the number of deaths

ð5Þ

N: the number of persons affected by a nonlethal disease; L: the duration of this disease;

ð6Þ

Pf_cryp: the mortality rate due to Cryp. infection;

pðf , gÞ Pðf jgÞ ¼ pðgÞ

ð7Þ

P(f,g): the probability of fatal gastroenteritis; P(g):

 PrðCcryp Þ ¼ 0:127e
0:127 Ccryp

ð8Þ

Pr: the probability of Cryp. occurrence in source water;

D ¼ Ccryp jPro Vd T

ð9Þ

j: removal efficiency during drinking water treatment (%);

lost life-years (LYL)

LYL ¼

due to mortality

due to a particular disease at age i; ei is the standard life expectancy at the same time

equation 5

lost life-years (YLD) by living with disability

equation 6

mortality rate due

equation 7

conditional

YLD ¼

n

∑i Ni Li Wi

W: the measure of its severity pf _cryp ¼ Pðf jgÞ  Pin

P(f|g): the mortality probability due to gastroenteritis

to Cryp. infection probability function equation 8

probability distribution of Cryp.

equation 9

intake dose formulas

the incidence of gastroenteritis in China Ccryp: Cryp. concentration (count/10 L) Pro: occurrence probability of different routes in total population; Vd: intake volume of water

equation 10

mortality rate due to Cryp. infection

equation 11

equation 12

a



 D =-0:49 Pfcryp ¼ Pðf jgÞ  1- 1 þ 11:9 Pfcryp ¼ Pðf jgÞ 

for each day (L/day/person), more details shown in Table 2; T: duration of Cryp. exposure (d) ð10Þ

!
 Pm  D
0:49 1
1þ ð11Þ 11:9


 ! D -0:49 Pfcryp ¼ 0:7  1- 1 þ 11:9

ð12Þ

used for immunocompetent subpopulation without preexisting anti-Cryp. IgG levels used for immunocompetent subpopulation with preexisting anti-Cryp. IgG levels used for immunocompromised subpopulation

Cryp.: Cryptosporidium.

alone have neglected death due to complications of gastroenteritis, which could led to underestimation of the mortality rate.15 A comprehensive uncertainty analysis that involves the parameter variability and predictive errors of models is necessary to provide a scientific basis for making policy decisions. Several studies have applied the Monte Carlo method to simulate the variability of daily intake dose, but direct drinking has been considered as the sole exposure route in previous risk assessments of Cryptosporidium infection.7,9,12 Intake from residues of tooth-brushing and food and dish washing as well as swimming in rivers cannot be neglected,16 especially in places where boiled water and cooked food are popular in daily life. In addition, predictive errors occur when some parameters are predicted using models, which inevitably increases the uncertainty of risk assessment.17 When assessing the risk of Cryptosporidiosis for consumption of drinking water, each Cryptosporidium strain has a specific dose
response curve due to its distinct virulence. When the dose
response curve was applied to extrapolate the infectivity at specific doses, the predictive error was often neglected.7,9,18 In 2004, Englehardt estimated the confidence intervals of the

dose
response among isolates with different virulence,19 which has provided a tool to calculate the predictive errors. In China, Cryptosporidium has been detected in many provinces, with concentrations of 0
22 oocysts/10 L of source water in waterworks,20
22 which is in accordance with those (0.67
13.4 oocysts/10 L) reported in other countries where outbreaks of cryptosporidiosis have occurred.1,7 In this study, the occurrence of Cryptosporidium in source water of several cities of Zhejiang Province was investigated in the rainy season of 2008. Compared to previous studies on Cryptosporidium infection risks, our study had the following characteristics: (i) the mortality rate in the immunocompetent population caused by Cryptosporidium infection was estimated based on the cause-specific mortality; (ii) potential risk factors were considered including multiple exposure routes, immunity conferred following an initial exposure/infection and different coexistent virulence among isolates; (iii) a comprehensive uncertainty analysis including the parameter variability and predictive errors was carried out using Monte Carlo and bootstrapping methods. Our study is believed to be the first attempt to assess the overall risk of Cryptosporidium via different exposure routes and will be helpful for the revision of related water standards in the future. 4952

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Table 2. Summary of Exposure Parameters for Different Exposure Routes routes

volume per

occurrence probability of different

removal efficiency of

exposure duration

day/time (Vd)

routes in total population (Pro)

Cryptosporidium (j), %

per year (T)

intake of drinking unboiled tapwater (Vdr)

1.75

0.8%

log2
2.5 with conventional treatment

365

intake of tapwater
water residues for

0.007
0.071

100%

and log4
4.5 with advanced one

365

0.016
0.037

10%
22%

other purposes (Vre) intake of raw water during swimming (Vsw)

’ MATERIALS AND METHODS Water Sampling. As shown in Figure S1, samples were taken from the source water of Jiaxing, Yuyao, Tongxiang, and Xiaoshan during the rainy season from June to September 2008. In this period, the Cryptosporidium oocyst levels in river tend to be higher than other seasons due to washing stools by rain.23 For Tongxiang source water, daily samples were collected from 19 to 29 August. For the other cities, two samples were taken from 8 July to 12 July and August 29, respectively. Ten liters of raw water samples were collected at each time, and the samples were filtered within 4 h after sampling. Analysis of Cryptosporidium. Cryptosporidium oocyst concentrations were measured following the method of Hashimoto et al.,24 which was adopted as the standard method in Japan. Tenliter water samples were filtered on site using 3-μm pore-size cellulose ester membrane (ADVANTEC, TOYO, JAPAN). The filters with entrapped oocysts were folded and transferred to 50-mL conical polypropylene centrifuge tubes, which were sent back to the laboratory under cooling conditions, and preserved at 4 °C until analysis. Within 7 days, the filters in the centrifuge tubes were dissolved in 40 mL of acetone and centrifuged at 1050 g for 15 min to collect the solids. The solids were transferred to a 15-mL conical centrifuge tube, add 3ml percollsucrose solution from the bottom of the tube, and centrifuged at 1050 g for 10 min to collect the middle layer (Cryptosporidium containing) layer, following the addition of 5 mL of Percoll
sucrose solution was added to the tube using the same method (specific gravity = 1.10). The tube was purified once more using the same method. The purified samples were stained with Crypt-a-Glo and Giardi-a-Glo and 40 ,6-diamidino-2-phenylindole (DAPI) staining solution on the PTFE membrane and observed under an epifluorescent microscope (BX51; Olympus, Tokyo, Japan) in different modes. In this study, the method recovery rates of oocysts and cysts are 56% and 35%, respectively, which is higher than the requirement of EPA1623 method.25 The cumulative and density distribution of the observed data for Cryptosporidium was fitted using different probability density functions, i.e. exponential, uniform, log-normal, and normal (Crystall Ball 2000 Professional; Decisioneering, Denver, CO, USA), of which, the best goodness-of-fit was selected according to the χ2 test. Framework for Estimating the Health Risk of Cryptosporidium Infection. The framework for estimating the risk (DALYs) due to Cryptosporidium infection is shown in Figure S2. The multiple exposure routes were combined with dose
response relation of host-pathogen interaction to calculate the health risk of different susceptible subpopulation with different age and immune status. Table 1 outlines formulas and parameters used for the models. Exposure Assessment. It is well-known that the risk of Cryptosporidium infection is related to the contamination level of the pathogen in water from different sources as well as water

0

20

consumption habits.1 The daily consumption of water in other countries is between 0.25 and 1.1 L,11,26
30 but it is about 1.75 L in China.31 However, the Chinese are known for their habits of drinking boiled water and eating heated food, which can inactivate oocyts above 71.5 °C in 15 s.1 In Shanghai, which is close to Zhejiang Province, approximately 0.8% of people drink tap water directly,31 which is much less than in France (69%) and the United States (45
55%).7 Such water consumption habits mean that people in China are less susceptible to infection with waterborne pathogens like Cryptosporidium. Quantification of water intake is an important step in exposure assessment. In this study, three main exposure routes were considered: direct drinking, residues from tooth-brushing and food and dish washing, and swimming in rivers (see Table 2). As mentioned, in Shanghai, approximately 0.8% people drink tap water directly,31 and this was adopted for risk assessment in our study. A certain amount of residual water remains in the mouth after tooth-brushing and food and dish washing. The ingestion volume of such water has been estimated to be 0.007
0.071 L/ person
day (more details in Section I of the Supporting Information). A person swimming in a river is estimated to ingest accidentally 0.016
0.037 L of surface water.16 It has been shown that 10.5
21.6% of the people in this Zhejiang province swim in rivers.32 It is assumed that these people swam once weekly over a 5-month period from May to September (i.e., 20 times/year). Removal of oocysts by the coagulation-filtration step in the waterworks was inferred to be about 2
2.5 log10 units.1 Advanced treatment using ∼3 mg/L ozone could further remove 4
4.5 log10 units oocysts,12which is the combination of conventional and ozone treatments. More details of exposure parameters were shown in Table 2. Dose
Response of Cryptosporidium Infection. Two models, the exponential and Beta-Poisson models, were respectively used to fit the dose
response data of Cryptosporidium infection as eq 1 and eq 2 in Table 1. Model selection criteria (MSC) were used to compare their relative goodness-of-fit between the two models,33 to select the more suitable model (more details in the Supporting Information Section II ). It is well-documented that the susceptibility to infection in the subjects was affected by the levels of preexisting anti-Cryptosporidium IgG levels.34,35 A conferred immunity parameter (Pm) (eq 3 in Table 1) was applied to express effects of the preexisting anti-Cryptosporidium IgG levels on the dose
response relation, which means the probability of any single organism successfully passes immunity barriers. Uncertainty and Sensitivity Analysis. The uncertainty analysis in this study included the predictive errors and parameters fluctuation. The predictive errors of dose
infection rate responses were estimated using a bootstrapping method (Matlab Ver. 6.5) (2000 trials), and the fluctuations of parameters such as water consumption volumes, and removal efficiency of treatment, were acquired using the Monte Carlo method of Crystall Ball 2000 4953

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Table 3. Lost Disability-Adjusted Life Years (DALYs Median, 95% Confidence Intervals) Per Year of Subpopulations with Different Immunity Status under Three Main Exposure Routes and Different Treatments treatment type

subpopulation

conventional treatment

immunocompetent with antibody immunocompetent without antibody

advanced treatment

direct consumption of

ingestion of tap

water intake

drinking tap water

water residues

during swimming

0.0881 (0.064
0.409) 0.391 (0.0318
1.32)

0.428 (0.0965
1.83) 4.11 (1.14
15.5)

0.206 0.887

sum 0.786 (0.376
2.2) 5.52 (2.42
17.0)

immuno- compromised (HIV)

0.215 (0.0175
0.725)

0.992 (0.192
4.31)

0.781

2.07 (1.15
5.38)

sum

0.696 (0.0558
2.45)

5.55 (2.43
16.8)

1.87

8.40 (4.03
24.4)

immunocompetent with antibody

0.0009 (0.0001
0.0044)

0.0043 (0.0010
0.0184)

0.206

0.212 (0.207
0.233)

immunocompetent without antibody

0.0044 (0.0003
0.0214)

0.0412 (0.00114
0.158)

0.887

0.935 (0.903
1.05)

immuno-compromised

0.0024 (0.0002
0.0118)

0.0099 (0.0019
0.0328)

0.781

0.795 (0.785
0.829)

sum

0.0078 (0.0005
0.0376)

0.0558 (0.0149
0.219)

1.87

1.94 (1.90
2.14)

Professional (Decisioneering, Denver, CO, USA) which was applied to analyze the sensitivity of the parameters in these models. Calculation of DALYs. Disease reduces length and quality of life, which can be quantified using the DALY, as proposed by Murray.36 DALY is calculated by combining lost life-years (LYL) due to mortality and those lost by living with disability (YLD), weighed with a factor between 0 and 1 for the severity of the disability. LYL and YLD are calculated by eq 4 and eq 5 in Table 1. Thus, population was divided into tree subpopulations: immunocompromised, such as HIV-infected patients, immunocompetent without and with preexisting anti-Cryptosporidium IgG. The same dose-infection response relation was applied for immunocompromised and immunocompetent without the preexisting anti-Cryptosporidium IgG subpopulations. For that with the preexisting antiCryptosporidium IgG, the dose parameter (D) in eq 1 or 2 was multiplied with a conferred immunity parameter (Pm, eq3 in Table 1) to express the decrease of Cryptosporidium infectivity. There have been a few studies on the death (Pf_ cryp) caused by Cryptosporidium infection (Pg_cryp). For the immunocompetent population, the mortality rate due to Cryptosporidium infection (Pf_cryp) was estimated by multiplying the mortality probability due to gastroenteritis (P(f|g)) with incidence of gastroenteritis caused by Cryptosporidium infection (Pin in eq 1 or 2) according to the statistical formula of multiplication37 shown as eq 6 in Table 1. According to the conditional probability function,38 the mortality rate from gastroenteritis (P(f|g)) can be calculated by the quotient between P(f,g) and P(g) (eq 7 in Table 1), where the probability of fatal gastroenteritis (P(f,g)) is reported in Yearbook of Health in the People’s Republic of China,14 and the incidence of gastroenteritis (P(g)) in China was surveyed to be 56.7 per 105 persons.20 However, for the immunocompromised subpopulation, all cases infected by Cryptosporidium will have gastroenteritis, where incidence of gastroenteritis (P(g)) was regarded as 100%,39 However, that of only 30% can recover and then 70% will suffer from gastroenteritis until death in short time.15 Thus, the P(f,g) for immunocompromised subpopulation was defaulted to be 70%. More details about the models structure of cryptosporidiosis risk were shown in Figure S2.

’ RESULTS AND DISCUSSION Exposure Assessment of Cryptosporidium. The cumulative and density distribution of Cryptosporidium in source water of the investigated region is shown in Figure 1. The highest concentration of 17 oocysts/10 L was observed twice, and no detection was observed on seven occasions. The average concentration of Cryptosporidium was 7 oocysts/10 L for the 23 samples. In rivers

Figure 1. Frequency and cumulative distribution for number of Cryptosporidium oocysts per 10 L in river surface water of Zhejiang Province, which is used as a drinking water resource.

of Chengdu in Southwestern China, the concentration of Cryptosporidium varied between 2 and 22 oocytes/10 L of source water,21 which is close to our result. Similar results (5 oocysts/10 L) were also found in other rivers of Southern China during the dry season.40,41 In the surface water of The Netherlands, median Cryptosporidium distribution was 13.4 oocysts/10 L, which is higher than that in China. In Japan, the geometric mean concentration of Cryptosporidium in 13 resource water samples was 4 oocysts/10 L.24 In the widespread outbreak of watery diarrhea due to a Cryptosporidium infection in Milwaukee, Wisconsin, USA, 0.67
1.32 oocysts/10 L were detected in raw water samples in March 1993.42 In the first outbreak of cryptosporidiosis in Ireland in April 2002, the Cryptosporidium level in the resource water was 1.4
2.4 oocysts/10 L.7 Therefore, the oocyst level in the present study was in accordance with those reported in other areas in and beyond China. Different models were used to fit the results in Figure 1, and the exponential distribution function showed the lowest χ2 test value (1.57) (p = 0.667). Equation 8 in Table 1 was acquired to describe the probability of Cryptosporidium in source water of the investigated region. Considering the consumption volume (Vd) per day, the annual dose (D) can be calculated by eq 9 (shown in Table 2). The Chinese average daily ingestion of unboiled tapwater is about 21 mL, which is much less than the 960 mL 4954

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Figure 2. Fitting dose
response curves between infection probability of healthy adult volunteers and intake of Cryptosporidium oocysts, using the bootstrap method with a best-fit model of Beta-Poisson function. The red cycle (o) indicates the observed data set from healthy adult volunteers. The blue line represents all the predicted curves using the bootstrap resampling method.

in the United States.43 This might be the main reason why largescale watery diarrhea outbreaks occur more frequently in countries such as the United States and United Kingdom, rather than China, although the concentrations of Cryptosporidium in source water are not very different. Gastroenteritis Incidence and Mortality due to Cryptosporidium. To date, there have been many studies on the infectivity of Cryptosporidium,34,35,44
46 which has provided useful information to establish the dose
response relationship between human and different isolates of Cryptosporidium (TAMU, Iowa, UCP, and Moredun) (Supporting Information Table S2). Several different models, particularly the exponential model, have been employed to describe the Cryptosporidium dose
infection responses in previous studies.1 In the present study, the two most often used models, namely, the exponential (eq 1) and Beta-Poisson (eq 2) models were compared for their MSC values, based on these infectivity data (Table S2). The MSC values of the two models were
0.668 and
0.565, respectively, which showed that the Beta-Poisson model could fit the dose
response data better than the exponential model could. Therefore, the coefficients (R,β)dose
response of gastroenteritis incidence (Pin) could regress as Figure S2. It is clear that eqs. 10
12 satisfied the precondition of the Beta-Poisson model (R,β and β.1). The infective ability of Cryptosporidium depends on its genotype as well as some environmental factors such as duration from the previous host and water temperature. The human susceptibility to Cryptosporidium might also differ due to predisposed genetic immunity, previously acquired resistance, and specific circumstances of exposure.1 The combinations of the above factors can be simulated using the bootstrapping resampling method, to which 2000 trials (curves) were subjected in the uncertainty analysis shown in Figure 2. The bootstrap resampling curves of the dose
responses converge with the decrease of Cryptosporidium dose, which agrees well with the quantile contours of the predicted dose
response relations of the four isolates TAMU, Iowa, UCP, and Moredun.1 Prevalence of pre-existing anti-Cryptosporidium IgG by age in Anhui Province was reported by Guerrant,47 which was regarded

ARTICLE

as the same with that (Pimm) of Zhejiang Province (shown in Table S3). For the subpopulation with some preexisting antiCryptosporidium IgG level, the dose (D) in eq 2 was multiplied with activity proportion of Cryptosporidium (Pm) as effective dose (D*Pm) to calculate infection rate (Pin). The probability distribution (x) of the anti-Cryptosporidium IgG level in eq 3 was obtained based on a survey in Anhui Province population as normal distribution (Log10 scale: mean (0.64) and standard derivation (0.28)).48 The mortality for these three subpopulations will be calculated using eqs 10-12 in Table 1. For the immunocompetent population without preexisting anti-Cryptosporidium IgG, the mortality (Pf_ cryp) induced by gastroenteritis due to Cryptosporidium infection is shown as eq 11 in Table 1, where fatal gastroenteritis (P(f,g)) in different age groups are shown in Table S3. High fatality rates occurred in the age groups of 1
5 years (1.5
8.3  10
6) and >70 years (2.3
9.3  10
6). The age group of 5
70 years had a much lower fatality rate (0
0.7  10
6). The incidence of gastroenteritis has often been used as the assessment end point of risk.18,26,49 It should be noted that different age groups not only have different susceptibility but also different standard life expectancy. Thus, the use of the average incidence of gastroenteritis can lead to over- or underestimation of health risk. This problem could be solved by using DALYs, which use life lost to calculate different illness symptoms4 as the end point to calculate the health risks of people exposed to Cryptosporidium. According to the result of sensitivity analysis (shown in Figure S3), the dose
response formula contributed mostly to the variance of total risk, of which two coefficients (R and β) are listed in the top three sensitive parameters. The removal coefficient was the second most sensitive parameter, followed by daily intake volume of water, concentration of Cryptosporidium, etc. DALY Calculation for Different Cases. The DALYs for each case were calculated using eq 3 and 4 shown in Table 1. In the studied region, some waterworks only adopted conventional treatment, while others adopted advanced treatment using ozone, which led to different Cryptosporidium removal efficiencies. For people who consumed drinking water treated with the conventional process, the total DALYs were 8.4 per 105 persons (95% CI: 4.03
24.4 per 105), which is significantly lower than the model estimated DALYs for diarrheal diseases in China in 2004 (324 per 105 persons).50 This means that Cryptosporidium infection contributes to about 2.59% (8.4/324) of diarrheal diseases, which is slightly higher than that (1.3%) in Anhui Province22 adjacent to Zhejiang Province (Figure S1). Many agencies, including the United States Environment Protection Agency, have identified that the range of cancer risk level acceptable by some for a lifetime is from 10
4 to 10
6,51 which can be used to calculate a tolerable annual loss of DALYs. Considering the average DALYs per cancer death is 13.8 years,52 the risk level tolerable by some DALYs over a year will be from 1.97  10
7 to 1.97  10
5 based on a 70-year lifespan. However, the total risk of DALYs for people who consume conventionally treated water is approximately 6.51  10
5 (95% CI: 2.16  10
5
22.35  10
5), which is a little higher than the upper bound (1.97  10
5) of risk level tolerable by some. Therefore, the health risk level due to Cryptosporidium infection was still considerably high for people in this region consuming conventionally treated drinking water. In Figure 3, the diameter of the circles represents the relative values of DALYs per year for immunocompetent and immunocompromised groups who consumed tap water treated with 4955

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Figure 3. Contribution of different exposure routes to total DALYs per year due to Cryptosporidium infection of inmmunocompromised and immunocompetent groups, after conventional and advanced water treatment, respectively. The diameter of the circles represents the relative number of DALYs between the different groups.

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estimated that the DALYs for Cryptosporidium exposure after conventional and advanced treatment were 0.93 and 0.17 DALYs per 106 person
years, respectively, when its concentration was 0.044 oocysts/L in raw water and daily unboiled tap water about 0.160 L.4 The DALYs were lower than those in our study, which could be attributed to the difference in the concentration of oocysts in raw water, dose
response curves, and the volume of consumption of unboiled tap water. Figure 4 shows that the adoption of advanced treatment can reduce the DALYs due to Cryptosporidium infection from 8.4 (95% CI: 4.03
24.4) per 105 persons to 1.94 (95% CI: 1.90
2.14) per 105 persons. However, advanced treatment could only eliminate 76% of the risk, although it could cut further 99% of Cryptosporidium from drinking water. The reason was that the use of ozone could not cut the risk of swimming in rivers. According to the above analysis, we can reach the following conclusions. The health risk level of Cryptosporidium infection in Zhejiang Province with advanced water treatment is considered acceptable by some. The practice of swimming in rivers, post-tap treatment, and drinking water rates have a major influence on the risk of Cryptosporidium infection. Ingestion of residues from tooth-brushing and food and dish washing is also an important exposure route. Swimming in rivers is the major route of ingestion for those who consume ozone-treated tap water. It should be noted that Cryptosporidium was measured as the total oocysts. So the calculated risk may represent the conservative level. This study provides a method to establish the risk of Cryptosporidium infection and provide information for the potential strategies for risk reduction, which is useful for the modification of water quality standards based on cost utility analysis given use of DALYs.

’ ASSOCIATED CONTENT

bS

Supporting Information. Detailed descriptions of estimation of tapwater residues by washing and intake volume during swimming, brushing and optimizing dose
response relationships, sampling sites map, and Chinese population structure. This material is available free of charge via the Internet at http://pubs.acs.org.

’ AUTHOR INFORMATION Figure 4. DALY frequency distribution of conventional and advanced treatment per 106 people living near a river in Zhejiang Province based on the Monte Carlo method. Advanced water treatment had high efficacy to reduce the risk of Cryptosporidium infection.

different processes. The percentage of DALYs of the immunocompromised group in the overall population in China was estimated to be 24.6% and 41% for those drinking water treated with conventional and advanced processes, respectively. If the conventionally treated water was consumed, the highest contribution came from the residues intake from tap water, which was 74.3% and 49.9%, for the immunocompetent and immunocompromised groups, respectively. The contribution of swimming in the rivers to the DALYs was 22.3%, which was 96.4% when the advanced treatment drinking water was consumed in these regions. When advanced treatment was used, the risk from dinking water was only 0.0689  10
5 DALYs per year, which was much lower than the tolerable risk. Havelaar et al. have

Corresponding Author

*Phone/Fax: 86-010-62923541. E-mail: [email protected].

’ ACKNOWLEDGMENT The financial support by Risk assessment of contaminants in Drinking Water based on National Survey in China (2009ZX07419-001) and the National Natural Science Foundation of China [50778171;50809066; 20807013] is gratefully acknowledged. ’ REFERENCES (1) WHO. Risk Assessment of Cryptosporidium in Drinking Water; World Health Organization: 2009; p 143. (2) Smith, H. V.; Patterson, W. J.; Hardie, R.; Greene, L. A.; Benton, C.; Tulloch, W.; Gilmour, R. A.; Girdwood, R. W.; Sharp, J. C.; Forbes, G. I. An outbreak of waterborne cryptosporidiosis caused by posttreatment contamination. Epidemiol. Infect. 1989, 103 (3), 703–15. 4956

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