Perspectives of Quantitative Risk Assessment Studies ...

Water Air Soil Pollut (2017) 228:185 DOI 10.1007/s11270-017-3333-5

Perspectives of Quantitative Risk Assessment Studies for Giardia and Cryptosporidium in Water Samples Ana Paola Balderrama-Carmona & Pablo Gortáres-Moroyoqui Ruth Gabriela Ulloa-Mercado & Luis Alonso Leyva-Soto & Lourdes Mariana Díaz-Tenorio

&

Luis H. Álvarez &

Received: 10 November 2016 / Accepted: 8 March 2017 # Springer International Publishing Switzerland 2017

Abstract A quantitative microbial risk assessment method can be used to evaluate infections probabilities for microorganisms in a specific place. The methodology provides suitable information to generate strategies focusing on health problems. Giardia cysts (GC) and Cryptosporidium oocysts (CO) are considered emerging pathogens that can infect human and animals by ingesting contaminated food or water, where food and water are transport vehicles for these parasites. Studies for GC and CO have reported occurrences for these parasites in water up to 100%, and some of these studies documented a number of cases, about 403,000 people, infected worldwide. This review is focused on compiling the most relevant works assessing the risk for GC and CO and their presence in different water samples that are susceptible for direct and indirect human consumption. The annual risk infection probability for these A. P. Balderrama-Carmona Departamento de Ciencias Químico-Biológicas, Universidad de Sonora (UNISON), Unidad Regional Sur Lázaro Cárdenas 100, colonia Francisco Villa, C.P. 85890 Navojoa, Sonora, Mexico A. P. Balderrama-Carmona : P. Gortáres-Moroyoqui (*) : R. G. Ulloa-Mercado : L. A. Leyva-Soto : L. M. Díaz-Tenorio Departamento de Biotecnología y Ciencias Alimentarias, Instituto Tecnológico de Sonora (ITSON), 5 de febrero 818 Sur, colonia Centro, C.P. 85000 Cd. Obregón, Sonora, Mexico e-mail: [email protected] L. H. Álvarez Universidad Autónoma de Nuevo León (UANL), Facultad de Ciencias Químicas, Av. Universidad S/N, Cd. Universitaria, 66451 San Nicolás de los Garza, Nuevo León, Mexico

parasites has been reported from different water sources, with a range between 1 × 10−6 and 1, while the world standard regulation is 1 × 10−4. The infection probability depends not only on water quality but also on water treatment implementations. Keywords Risk probability . Giardia . Cryptosporidium . Drinking water . Groundwater . Surface water

1 Introduction Health risk increases due to pathogen spread in drinking water contaminated with human or animal feces. These pathogens are unregulated in several countries of the world even if they are responsible for diarrheal diseases (Ashbolt 2015). Approximately 2.5 billion people do not have access to a basic sanitation. Plus, 1.1 billion people worldwide do not have access to safe drinking water sources (WHO 2003; CDC 2012). Quantitative microbial risk assessment (QMRA) is a methodology to predict infection risks of pathogen microorganisms in a determinate environmental source and in a specific region. Also, it is a useful tool to evaluate the performance of water treatment systems. The information obtained from this method is interpreted and used to justify further analysis of specific hazardous events and establish remediation strategies. Furthermore, it is a common methodology to assess the risk of Cryptosporidium and Giardia (Ryu and Abbaszadegan 2008; Hunter et al. 2011; Razzolini et al. 2011). Giardia and Cryptosporidium are considered as

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emerging pathogens which can infect through the ingestion of their cysts and oocysts, respectively, and can be found in contaminated water bodies further in soil and airborne dust (Caccio et al. 2005). QMRA is based on microbiological data and exposure calculation to a microorganism, making a precise and predictive method that has become a standard for assessing the public risk of giardiasis and cryptosporidiosis in developing countries (Hunter et al. 2011). However, in the third world countries, there are no guidelines related to these pathogens to determine water quality (Olivas-Enríquez et al. 2011), which raises the incidence of Cryptosporidium and Giardia (Betancourt et al. 2014; Burnet et al. 2014). Direct or indirect ingestion of parasites from polluted water, or products irrigated or exposed to this water, can produce giardiasis or cryptosporidiosis (Spanakos et al. 2015). The water source is one of the main factors of risk to get the infection and depends on the parasite concentration. Drinking water, groundwater, and surface water are different options of water consumption for people; therefore, it can be contaminated with Giardia cysts (GC) and Cryptosporidium oocysts (CO). Also, the treated wastewater could be considered as another water source (Maikai et al. 2013). This review paper focuses on compiling the most relevant studies that assessed the risk for GC and CO in different environmental samples intended for direct and indirect human consumption.

2 Giardia and Cryptosporidium The most common human parasitic protozoans are the genera Giardia and Cryptosporidium which are transmitted by water. Giardia is a flagellated protozoan that was first observed in 1681 by Leeuwenhoek as a microorganism found in diarrheal stools; on the other hand, Cryptosporidium was first recognized as a human intracellular pathogen in 1976. These parasites are unicellular eukaryotic protozoans which commonly infect epithelial cells, causing giardiasis when the infection is caused by GC or cryptosporidiosis when CO is responsible; these illnesses are considered zoonosis because the different species of the genus can infect animals and also humans (Tables 1 and 2) (Vázquez and Campos 2009; Caccio and Widmer 2014). Cysts and oocysts, the environmental stages of Giardia and Cryptosporidium, respectively, are

Table 1 Giardia species responsible of human and animal infections (Adam 2001; Pond et al. 2004) Speciesa

Host

G. intestinalis

Human and mammals

G. agilis

Amphibians

G. muris

Rodents

G. ardeae

Herons, birds

G. psittaci

Birds

G. microti

Voles and muskrats

a Cyst mean size: 6–8 μm wide by 12–15 μm long; infection site: intestine

ubiquitous and persist viably in water during long periods (Fig. 1) (Korich et al. 1990). Also, they survive in conventional water treatment systems due to their small size, flexibility, and structure (Gerwig et al. 2002). The transmission of cryptosporidiosis and giardiasis occurs when there are around ten (oo)cysts ingested by a susceptible host (Castro-Hermida et al. 2010). Some studies documented that dose-response extrapolation for the consumption of just one (oo)cyst in humans is up to 2.0% of probability of gastrointestinal diseases (Rendtorff 1954; Dupont et al. 1995); therefore, it is Table 2 Common species of Cryptosporidium that infect human and animals (data taken from Xiao and Cama 2006; Smith and Nichols 2006) Speciesa

Hosts

C. andersoni

Cows, camels

C. baileyi

Chicken, rooster, turkey, ducks, ostriches, quail

C. bovis

Cows, yaks

C. canis

Dogs, foxes, wolf, humans

C. felis

Cats, humans, and cows

C. galli

Chicken, finches

C. hominis

Humans, monkeys, lambs, manatee

C. meleagridis

Turkey, humans, parrots

C. molnari

Fish

C. muris

Rodents, camels

C. parvum

Cows, lambs, goats, deer, humans, rats, pigs, horses

C. saurophilum Lizards and snakes C. serpentis

Snakes and lizards

C. suis

Pigs

C. wrairi

Guinea pigs

Oocyst mean size: 4.2–7.4 μm; principal infection sites: intestine and stomach

a


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Fig. 1 Immunofluorescence images of Giardia cysts (A) and Cryptosporidium oocysts (B) (Photo Credit: H.D.A Lindquist, U.S. EPA)

considered that even at low (oo)cyst doses, the risk of infection is high (Medema and Schijven 2001). Therefore, (oo)cyst ingestion may occur through direct contact with contaminated food or water. These are occasionally seen as a cause of waterborne diarrhea, with a very high prevalence and incidence of infection in developing countries (Caccio et al. 2003; Luján and Svärd 2011; Caccio and Widmer 2014). The common outbreaks of cryptosporidiosis and giardiasis have been attributed to consumption, directly and indirectly, of contaminated drinking water. These outbreaks have been associated with the presence of (oo)cysts in several water types, in many investigations around the world (Table 4).

3 QMRA Method QMRA is a four-tiered approach that is interpreted with laboratory data about public health impact and involves hazard identification, exposure assessment, doseresponse modeling, and risk characterization. 3.1 Hazard Identification The hazard identification is characterized as a diseasecausing agent. The information collected may include a description of conditions related to the pathogen such as morbidity ratios and clinical aspects. Collection of data for hazard identification is used in exposure assessments, where the impact of the processing, distribution, preparation, and consumption of food or water is incorporated (Haas et al. 1999).

For microbiological agents, the hazard identification recognizes and examines microorganisms or microbial toxins associated with the disease with the presence of pathogens in water. The hazard identification data can be obtained from scientific literature, databases, and through experts’ calculations. 3.2 Exposure Assessment Exposure occurs when a person is in contact with a hazard, as a pathogen. It is for that reason that in the exposure assessment, first it is necessary to determine the route or pathway in which the microorganism accedes to the host. In this step, a characterization of physicochemical properties of the study area and the characteristics of the host population are considered. Evaluation is based on samples and analysis of surface water or other media (Sunger and Haas 2015). Exposure assessments are the estimation of probabilities of an individual or a population to be exposed using the identified hazard, which also includes the probabilities of ingestion (amount ingested). In addition, it describes routes through which a pathogen population is introduced and threatened in the production, distribution, and consumption of food or water. Depending on the scope of risk assessment, exposure assessment can begin with the prevalence of pathogens in raw materials or by the description of pathogen populations and subsequent steps. To complete the route to the host, it is necessary to bring this data to a dose-response model (Razzolini et al. 2011). Within the exposure assessment, there are estimated consumption patterns (including portion sizes and frequency of consumption) that are influenced, for

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example, by the socio-economic, cultural, and ethnic type, seasonality, age of consumers, the region, preferences of consumers, and health education of food handler. The evaluation also considers the degree of exposure to environmental microorganisms and thermal shock (i.e., exposure to the pathogen can increase a million times when water is not heated, and levels may be reduced to zero when boiled). The probability of infection depends on the viability of the (oo)cysts; hence, the reduction of viability is an important factor to prevent the disease. For cryptosporidiosis and giardiasis, it is important to know the health status of the consumer. The infection risk is higher in immunosuppressed people, which could be a fatal infection. Risk factors, in the evaluation of exposure, depend on the quality of the raw material, processing, environment, product composition, packaging, and storage conditions (USEPA 2013; Schroeder et al. 2006). Exposure assessment can also be obtained, indirectly, from existing data. For instance, Hunter et al. (2011) obtained a quantitative microbial risk assessment of cryptosporidiosis and giardiasis using existing data of Escherichia coli in private water supplies. The most important factors to estimate the average exposure (N) are the amount of (oo)cysts in the exposure, the recovery efficiency of the detection method, the amount of viable (oo)cysts or those capable of infection, removal efficiency of (oo)cysts during treatment in water, and the daily consumption average (Haas et al. 1999; Schroeder et al. 2006). 3.3 Dose-Response Modeling The beginning of the hazard characterization requires a systematic planning stage to identify the context, purpose, scope, and focus of the study to be carried out. It should examine the aspects of the pathogen, host, and matrix (i.e., water, food, soil) (Haas et al. 1999). The dose-response is the quantitative measurement or norm for the risk estimation; this step is defined as the determination of the relationship between the magnitude of exposure by chemical, physical, or biological agent and the severity and frequency of its association with adverse health. Risk characterization provides a qualitative and quantitative description of the severity and duration of adverse effects, resulting from the ingestion of microorganisms and their toxins (Haas et al. 1999; Schroeder et al. 2006; An et al. 2012). The simulation dose-response provides a quantitative relationship


between the likelihood of adverse effects and the level of microbial exposure that permits a correlation that describes the probability of a specific response from the exposure to a particular pathogen in a given population according to the dose. This function is based on empirical data and is usually expressed as a mathematical relationship. For calculating an infection risk, there are several models which are adjusted to certain microorganisms and are based on one, two, three, or four of the following parameters or assumptions: the dose for initiating the infection necessary for one or more microorganisms to begin the illness; shape of the microorganisms, the form of microorganisms that allows it to have a better pathway distribution and also causes greater survival in the environment; curve rate value, the parameter affecting the spread of the microorganism; and the predicted dose at a specified value of the probability of the infection. For Cryptosporidium and Giardia, the best fitting model is exponential which has a Poisson distribution of the protozoan among replicated doses, where one (oo)cyst is capable of producing an infection if it is ingested by a host. All the (oo)cysts have an independent and identical probability of surviving to reach and infect (exponential model equations are provided in Table 3) (Haas et al. 1999; Moon et al. 2005; Ahmed 2014). The dose is the number of (oo)cysts able to initiate infection (k), and the optimized parameters are as follows: k = 5.72 × 10−2 for Cryptosporidium and k = 1.99 × 10−2 for Giardia (Rose et al. 1991; Haas et al. 1999; Balderrama-Carmona et al. 2014). The lethal dose which causes the death of 50% of the sample population (LD50) is a standard in toxicology and is a constant to measure the time limit of potential poisoning for a hazard. LD50 for CO is 1.21 × 10+1 and 3.48 × 10+1 for GC. Dose-response data of infection with Cryptosporidium have also been reported in humans by DuPont et al. (1995), indicating that low-dose infectivity of Cryptosporidium appears to be about five times lower than that of Giardia. Compared to newborns, in elderly persons and other risk groups, the risk of infection calculated with data from adult persons may be underestimated (Haas et al. 1999; Lammerding and Paoli 1997; Rose et al. 1991). When the dose is known, the probability of infection by GC and CO may be calculated with the doseresponse relation of the exponential model (Haas et al. 1999). According to this model, the probability of a person being infected is in a function of the ingested pathogens that survive to initiate infections. A


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Table 3 Some dose-response models for quantitative microbial risk assessment

Number of parameters

Models

Equations

1 Exponential

Pi = 1 − expkd

2 Beta-Poisson

Log-probit Log-logistic

3 WeibullGamma

Parameter values

Most investigated and match pathogen agents

Reference

Pi = infection probability d = dose k = model (infectivity) parameter −α α = model (infectivity) Pi ¼ 1− 1 þ βd parameter β = model (shape) pa rameter 1 ðlnd−αÞ=β Pi ¼ 2π ∫−∞ exp − 12 t 2 dt f = function of dose

G. duodenalis C. parvum N. fowleri Poliovirus P. aeruginosa Rotavirus R. rickettsi Salmonella C. jejuni

Haas et al. 1999

Pi ¼ 1þexp1−lnd−α ½ β

Y. enterolitica

γ −α Pi ¼ 1− 1 þ d β

Chen and Hoover 2003

L. monocytogenes

Farber et al. 1996

Gale 2001

Conlan et al. 2011

Exponential Gamma

Þ Pi ¼ 1− exp1þð−γd ð βd Þα

S. pullorum IV

Moon et al. 2004

Gompertz

Pi = 1 − exp { − exp ( α + βf (d))]

V. parahaemolyticus

Yang et al. 2009

mathematical model that describes a non-linear doseresponse relationship is the log-probit curve. According to the log-probit model, the risk of infection from a dose of one oocyst is 120-fold lower than 10-oocyst ingestion. Therefore, the log-probit and exponential models differ only by how those oocyst doses are distributed in an individual consumer within the population (Haas et al. 1999; Lammerding and Paoli 1997). 3.4 Risk Characterization Risk characterization is the final step in the risk assessment process, and it integrates the data collected in the previous steps to express the public health outcomes. Then, the probability of health risk can be determined depending on the values previously obtained (Andersen 2015). The degree of confidence at the final estimation of risk will depend on the variability, uncertainty, and assumptions previously considered. Moreover, an important component in the risk characterization is the sensitivity of the analysis for determining the most important parameters that contribute to the total uncertainty of the risk assessment. Thus, the final result of the risk characterization is to give an estimation of a disease prediction associated to a particular microorganism, given the uncertainty and variability (An et al. 2012).

In many cases, the risk evaluation assumes that the dose-response has a relationship that is approximately linear at low doses. Therefore, at very low doses, the determination of risk infection simply consists in multiplying the dose estimated with the slope of the doseresponse relation to low doses. Then, the frequency distribution of the resulting probability of infection (or disease, or death, if sufficient information is available) could be assessed, either via analytical methods, or sample collection procedures (Haas et al. 1999).

4 Application of QMRA Method in Different Water Types The development of models describing the complex nature of pathogen populations in the water supply is a profit of QMRA (Lammerding and Paoli 1997). This method has become a standard in many parts of the world. For instance, the UK has pronounced a mandate that establishes that the risk assessment on water supplies should be carried out by local governments (Hunter et al. 2011). QMRA is detailed to a place which has specific environments and pathogen occurrence; for this, the method cannot be converted to other conditions or regions (De Keuckelaere et al. 2015).

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Water source and country

Small water supplies in France (Hunter et al. 2010) Small water supplies in England (Hunter et al. 2010) Drinking water in Canada (Barbeau et al. 2000) Tap water in Ireland (Cummins et al. 2010) Drinking water in Brazil (Razzolini et al. 2011) Water irrigating fresh produce in Mexico (Mota et al. 2009) Recreational waters in France (Coupe et al. 2006) Tap water in USA (Rose et al. 1996) Recreational lakes in Amsterdam (Schets et al. 2008) Surface water in USA (Ryu et al. 2007) 1.00E-04

2.00E-01

4.00E-01

6.00E-01

8.00E-01

1.00E+00

Risk probabilities of cryptosporidiosis

Fig. 2 Risk probabilities of giardiasis and cryptosporidiosis

QMRA is widely used to evaluate water quality and infection risk of illness transmitted through pathogen ingestion. For Giardia in water, the US Environmental Protection Agency (EPA) established a permissible risk value of