A container-based toilet (CBT) is a type of ecological toilet that allows users to compost their feces. During emptying, bucket washing, and composting operations, operators are exposed to microbial risks. This paper aims to evaluate these risks using the Quantitative Microbial Risk Assessment (QMRA) method. Nine pathogens prevalent in Haiti were targeted: Ascaris lumbricoides, Campylobacter spp., Cryptosporidium parvum, Escherichia coli O157:H7, Giardia intestinalis, poliovirus, Salmonella spp., Shigella spp., and Vibrio cholerae. Information regarding pathogens' concentration in feces came from scientific literature data. The exposure scenarios considered were those in which operators accidentally ingested a low dose of feces during the aforementioned operations. A Monte Carlo simulation was conducted to address uncertainties. The results showed that the probability of infection is highly elevated, while the probability of illness is generally moderate or minor, except for poliovirus and Ascaris. Preventive measures can be implemented to reduce these risks during various operations, such as wearing gloves, disposable protective masks, and appropriate clothing. It is up to the political authorities to develop guidelines in this regard and to organize awareness-raising activities with the help of local organizations mandated by the relevant authorities to ensure the safer use of technology by households.

  • Quantitative Microbial Risk Assessment related to container-based toilets (CBTs) in Haïti represents the first scientific QMRA study on composting toilets in the country. It considers nine pathogens, including some that have not been considered in previous studies. Results show the importance of precautions during emptying and composting operations.

Although access to sanitation is now considered a fundamental human right, about 2.3 billion people worldwide still lack access to basic sanitation facilities (Dickin et al. 2020). This problem is particularly common in low-income countries and promotes inadequate sanitation practices such as open defecation and/or dumping untreated feces into the environment (Jean et al. 2017; Ufomba et al. 2021). These practices pose a threat to human health (Saleem et al. 2019; Ufomba et al. 2021). Pathogens present in feces can contaminate the environment and, subsequently, cause infectious diseases in humans (Feachem et al. 1983; Mara 2004), most of which are contagious (Cloeckaert & Kuchler 2020; Zhang 2022). In Haiti, due to fecal contamination of the Artibonite River in 2010 (Guimier 2011), cholera was responsible for nearly 9,800 deaths and more than 820,000 suspected cases from 2010 to 2019 (Griffiths et al. 2021).

Technological solutions, such as container-based toilets (CBTs), have been developed to help reduce fecal pollution around the world (Esrey et al. 1998; Jean 2018). This type of ecological toilet offers households the possibility of recycling their feces usually through composting (Figure 1). With the CBT, feces are collected in a 20-L bucket, and a quantity of litter – consisting of shavings and/or sawdust – is poured over the feces after each defecation (Jean et al. 2017; Jean 2018). The litter helps absorb moisture and limits odors (Jean et al. 2017). When the bucket is full, it is emptied manually, and the fecal sludge is deposited into a composter for agricultural recovery. This material recovery is aligned with the principles of the circular economy, which advocates recycling matter to preserve natural resources and avoid potential contamination (Stahel 2016). However, handling feces during manual emptying and composting operations carries microbial risks, since the feces contain pathogens that can have a negative impact on human health (Feachem et al. 1983; Mara 2004). In the current context, where national and international institutions are promoting the use of this type of toilet, the assessment of microbial health risks associated with CBTs is necessary to prevent risks to human health.
Figure 1

Container-based toilet (Lécopot 2022).

Figure 1

Container-based toilet (Lécopot 2022).

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Quantitative Microbial Risk Assessment (QMRA) is an assessment method developed in the 1970s by the United States National Research Council, which is inspired by the chemical risk assessment method (De Giudici et al. 2011). It consists of four main steps: hazard identification, exposure assessment, hazard characterization (often reduced to dose-response assessment), and risk characterization (Haas et al. 1999; U.S. EPA 2012; WHO 2022). Studies relating to the QMRA have been carried out on composting toilets, but they have generally focused on the health risks associated with either the use of compost resulting from the recovery of feces (Nakagawa et al. 2006; Schonning et al. 2007; Darimani et al. 2015; Kumwenda et al. 2017) or spreading feces on gardens or agricultural fields (WHO 2006). The conclusions were divided in regard to the results of these studies. According to Nakagawa et al. (2006) and Schonning et al. (2007), the risk of infection is generally below the acceptable level of risk, whereas it is above the acceptable level of risk according to Kumwenda et al. (2017) and Darimani et al. (2015). The acceptable level of risk corresponds to 10−4 per person per year (Nakagawa et al. 2006; Schonning et al. 2007; Darimani et al. 2015). This disparity is mainly due to (i) the pathogens targeted, which differ between studies and/or regions, (ii) the different exposure scenarios developed by the authors, and (iii) the types of toilets considered. In addition, a semi-QMRA associated with the use of CBTs was carried out by Mackinnon et al. (2018) where Escherichia coli was considered as the target pathogen. The study revealed a high level of fecal contamination on toilet surfaces and a high risk of infection, through hand-to-mouth contact, in users and operators.

Unlike the aforementioned previous studies, the present paper aims to quantitatively assess the microbial risks faced by operators. It is not interested in the risk associated with the use of compost or feces as a fertilizer, as that subject has already been extensively studied in previous research. Furthermore, this study takes into consideration pathogens that have not been considered by other studies, such as Campylobacter spp., poliovirus, Shigella spp., and Vibrio cholerae.

This section aims to present the methodological approach used to conduct the study. The main steps are schematically presented in Figure 2.
Figure 2

Diagram illustrating the steps of the study.

Figure 2

Diagram illustrating the steps of the study.

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Presentation of the study area

Grande Plaine was chosen as the study area because it is one of the two main areas in Haiti with a significant number of CBT users. This rural area is located in the municipality of Gros-Morne, Haiti, and has the following geographical coordinates: 18.52°N and 74.34°W (Google Earth 2022). The average annual temperature is 24.8 °C (Jean et al. 2017). Grande Plaine has nearly 2,000 inhabitants distributed across 192 households, 35 of which, i.e. 280 people, use the CBT (Association des Originaires de Grande Plaine 2022). The health centers and reference hospitals in the region mentioned by Jean et al. (2017) revealed that typhoid, gastroenteritis, and intestinal parasitosis are the most frequent diseases in the region (especially among children). In addition to these pathologies, cholera is a sporadic epidemic in the region.

Out of the 35 aforementioned households, 33 use a community composting platform to compost their feces and two use their own composter (Association des Originaires de Grande Plaine 2022). This composting platform consists of nine compost bins, including five community and four individual, which compost fecal sludge from CBT user households throughout the year (Association des Originaires de Grande Plaine 2022). The platform and the composting process are described by Jean et al. (2017) and Jean (2018).

Hazard identification

This study focuses on nine pathogens. These pathogens were selected based on the following criteria in accordance with Westrell (2004), Schonning et al. (2007), and WHO (2022): (i) prevalence in Haiti, (ii) presence in feces, (iii) pathogenicity, (iv) ability to survive in the environment after excretion, and (v) availability of data (especially those related to the dose-response model) to allow their integration into a QMRA study. The target pathogens are Ascaris lumbricoides, Campylobacter spp., Cryptosporidium parvum, E. coli O157:H7, Giardia intestinalis, poliovirus, Salmonella spp., Shigella spp., and V. cholerae. The health problems generated by this organism are ascariasis, campylobacteriosis, cryptosporidiosis, hemorrhagic diarrhea, giardiasis, poliomyelitis, salmonellosis, shigellosis, and cholera, respectively (Feachem et al. 1983; Mara 2004). For the purpose of this study, Ascaris eggs were considered (not the worms).

Exposure assessment

Concentration of pathogens in feces

The data on fecal pathogen content of feces are drawn from the scientific literature (Table 1). Most of these studies used the quantitative polymerase chain reaction (qPCR) method to quantify target pathogens in feces.

Table 1

Concentration of target pathogens in feces

PathogensCFU/g for bacteria, NE/g for Ascaris, NO/g for protozoa, and TCID50 /g for poliovirusReferences
A. lumbricoides 104 (Feachem et al. 1983; WHO 2006
Campylobacter spp. 103 (Misawa et al. 2001; LaGier et al. 2004
C. parvum 103 (Valdez et al. 1997
E. coli O157:H7 3.3 × 102 (Westrell 2004; Schonning et al. 2007
G. intestinalis 102–103 (Straub et al. 1993
Poliovirus 1.3 × 105 (Hovi et al. 2001; Lodder et al. 2012
Salmonella spp. 104 (Yin Ngan et al. 2010; Teh et al. 2021
Shigella spp. 104 (Yavzori et al. 1994; Mokhtari et al. 2012
V. cholera 102–105 (Feachem et al. 1983
PathogensCFU/g for bacteria, NE/g for Ascaris, NO/g for protozoa, and TCID50 /g for poliovirusReferences
A. lumbricoides 104 (Feachem et al. 1983; WHO 2006
Campylobacter spp. 103 (Misawa et al. 2001; LaGier et al. 2004
C. parvum 103 (Valdez et al. 1997
E. coli O157:H7 3.3 × 102 (Westrell 2004; Schonning et al. 2007
G. intestinalis 102–103 (Straub et al. 1993
Poliovirus 1.3 × 105 (Hovi et al. 2001; Lodder et al. 2012
Salmonella spp. 104 (Yin Ngan et al. 2010; Teh et al. 2021
Shigella spp. 104 (Yavzori et al. 1994; Mokhtari et al. 2012
V. cholera 102–105 (Feachem et al. 1983

CFU: colony-forming unit; NE: number of eggs; NO: number of oocysts/cysts; TCID50: 50% tissue culture infectious dose.

Exposure scenarios

CBT users collect feces in approximately 20-L buckets, which are usually emptied once a week. In each household, sludge is manually emptied by an adult from the household (referred to as ‘emptier’ in this study) who carries the bucket of sludge to a community composting platform located approximately 200 m from the house.

The population exposed to microbial risks mainly includes emptiers and master composters. Farmers, who spread compost on their fields, as well as the potential consumers of the products grown, were excluded from the scope of this study. Thermophilic (co)composting is supposed to sanitize fecal sludge because of the increase in temperature during the second phase (Berendes et al. 2015; Jean 2018), which implies that the health risk can be considered negligible. The main known exposure routes are accidental ingestion, inhalation of bioaerosols, and skin contact. However, due to the absence of a dose-response model for the latter two exposure routes, only ingestion was considered.

The exposure scenarios considered the most plausible were those where the operators' hands were contaminated and accidentally brought to the mouth either directly or indirectly through eating, drinking, hand-to-mouth contact, or nail-biting. Two scenarios were developed: (i) contamination of emptiers during transport, unloading, and washing of the feces bucket and (ii) contamination of master composters while handling sludge during the composting process.

Measurement of the ingestion dose

The ingestion dose (D) corresponds to the amount of pathogens ingested per exposure. Equation (1) was used to determine D from the concentration (C) of pathogens in the sludge and the accidental ingestion (I) of feces.
formula
(1)

Two hypotheses were formulated based on previous studies to estimate accidental ingestion of feces by emptiers and master composters. The following values were considered, in cases where the operators did not sufficiently use personal protective equipment (PPE) in the course of their work:

Dose-response assessment

To model the behavior of pathogens within the host organism, two dose-response models were used: the exponential model represented by Equation (2) and the β-Poisson model represented by Equation (3).
formula
(2)
formula
(3)
where Pinf is the probability of host infection following the ingestion of a given pathogen; r is the constant corresponding to the survival capacity of the pathogen in the host organism; D is the ingested dose (in CFU for bacteria, number of roundworm eggs for A. lumbricoides, number of oocysts (or cysts) for protozoa (C. parvum and G. Intestinalis) and TCID50 for poliovirus); α and β are parameters of the β-Poisson model (α < β). They describe the pathogen's ability to survive and cause host infection (Health Canada 2019).

The parameters α, β, and r are specific to each of the pathogens considered. The values chosen for each of these organisms are presented in Table 2.

Table 2

Dose-response model applied to target pathogens

PathogensModelParametersReferences
A. lumbricoides β-Poisson α = 0.104
β = 1.1 
(Navarro et al. 2009; O'Connor et al. 2017
Campylobacter spp. β-Poisson α = 0.145
β = 7.59 
(Haas et al. 1999; Mara 2004; Health Canada 2019
C. parvum Exponential r = 0.0042 (Haas et al. 1999; Mara 2004; U.S. EPA 2012
E. coli O157:H7 β-Poisson α = 0.248
β = 48.8 
(Teunis et al. 2008; U.S. EPA 2012
G. intestinalis Exponential r = 0.0199 (Haas et al. 1999; U.S. EPA 2012; Health Canada 2019
Poliovirus Exponential r = 0.0091 (Haas et al. 1999; U.S. EPA 2012
Salmonella spp. β-Poisson α = 0.3126
β = 2,884 
(Haas et al. 1999; Westrell 2004; U.S. EPA 2012
Shigella spp. β-Poisson α = 0.21
β = 42.86 
(Haas et al. 1999; Mara 2004; U.S. EPA 2012
V. cholerae β-Poisson α = 0.25
β = 16.2 
(Haas et al. 1999; Mara 2004; U.S. EPA 2012
PathogensModelParametersReferences
A. lumbricoides β-Poisson α = 0.104
β = 1.1 
(Navarro et al. 2009; O'Connor et al. 2017
Campylobacter spp. β-Poisson α = 0.145
β = 7.59 
(Haas et al. 1999; Mara 2004; Health Canada 2019
C. parvum Exponential r = 0.0042 (Haas et al. 1999; Mara 2004; U.S. EPA 2012
E. coli O157:H7 β-Poisson α = 0.248
β = 48.8 
(Teunis et al. 2008; U.S. EPA 2012
G. intestinalis Exponential r = 0.0199 (Haas et al. 1999; U.S. EPA 2012; Health Canada 2019
Poliovirus Exponential r = 0.0091 (Haas et al. 1999; U.S. EPA 2012
Salmonella spp. β-Poisson α = 0.3126
β = 2,884 
(Haas et al. 1999; Westrell 2004; U.S. EPA 2012
Shigella spp. β-Poisson α = 0.21
β = 42.86 
(Haas et al. 1999; Mara 2004; U.S. EPA 2012
V. cholerae β-Poisson α = 0.25
β = 16.2 
(Haas et al. 1999; Mara 2004; U.S. EPA 2012

Risk characterization

Uncertainty analysis through Monte Carlo simulation

The data demonstrate variability in the ingestion of feces by master composters, and the concentrations of G. intestinalis and V. cholerae in the feces were subjected to a Monte Carlo simulation to address the inherent uncertainties. The @RISK software, version 8.4.0 developed by Palisade Corporation, was used for this purpose. Log-normal distribution was chosen as the appropriate probability distribution, in accordance with Schonning et al. (2007). A total of 10,000 iterations were executed. The median (50th percentile) was utilized for result interpretation, representing the realistic scenario, while the 95th percentile was employed to represent the pessimistic scenario. The variables selected for uncertainty analysis are listed in Table 3.

Table 3

Selected variables for uncertainty analysis in the Monte Carlo simulation

VariableUnitsMinimumLikeliestMaximumSD
Giardia concentration in feces Cyst/g 102 550 103 450 
V. cholerae concentration in feces CFU/g 102 50,050 105 49,950 
Fecal ingestion by master composters 0.05 0.265 0.48 0.215 
VariableUnitsMinimumLikeliestMaximumSD
Giardia concentration in feces Cyst/g 102 550 103 450 
V. cholerae concentration in feces CFU/g 102 50,050 105 49,950 
Fecal ingestion by master composters 0.05 0.265 0.48 0.215 

SD: standard deviation.

Determining the risk of infection and illness

The risk or probability of infection at each exposure was obtained from Equations (2) and (3). The annual probability of infection (Pinf/year) was calculated using Equation (4).
formula
(4)
where n is the number of exposures per year.

In the context of this study, n = 52 for emptiers, since the bucket is emptied once a week on average. Similarly, for the master composters, n = 52, as they work throughout the year at a frequency of once a week.

To determine the probability of illness occurring following an infection, Equation (5) was used.
formula
(5)
where Pill is the probability of illness and Pill/inf is the probability of illness by infection.
The term Pill/inf is defined by Equation (6) and is proposed by Havelaar & Swart (2014).
formula
(6)
where η and ρ are parameters of an underlying Gamma distribution for the duration of infection (Havelaar & Swart 2014). Values of 5.15 10−4 and 0.167 are suggested by Havelaar & Swart (2014) for η and ρ, respectively.

Risk classification

A risk classification model inspired by the work of Westrell et al. (2004) was used to facilitate the interpretation of the study results. This model classifies the risks as insignificant, minor, moderate, major, and highly elevated, in accordance with Table 4. Insignificant risk corresponds to the level of acceptable risk mentioned in the Introduction, which is equivalent to 10−4 per person per year (10−4 pppy).

Table 4

Proposed classification of microbial risks according to the probability of infection and/or illness (adapted from Westrell et al. (2004))

Risk levelPercentage (%)
Insignificant 0.01 
Minor 0.02 to <1 
Moderate 1 to <5 
Major 5–25 
Highly elevated >25 
Risk levelPercentage (%)
Insignificant 0.01 
Minor 0.02 to <1 
Moderate 1 to <5 
Major 5–25 
Highly elevated >25 

Probability of infection

Probability of infection per exposure

The calculations carried out using the selected dose-response functions and the ingestion doses per exposure made it possible to determine the probability of infection associated with each operation. These results are summarized in Table 5.

Table 5

Probability of infection per exposure

PathogensEmptiersMaster composters
50th percentile50th percentile95th percentile
A. lumbricoides 4.48 × 10−1 5.43 × 10−1 5.95 × 10−1 
Campylobacter spp. 2.16 × 10−1 3.83 × 10−1 4.77 × 10−1 
C. parvum 1.29 × 10−1 5.78 × 10−1 9.38 × 10−1 
E. coli O157:H7 4.87 × 10−2 1.94 × 10−1 3.44 × 10−1 
G. intestinalis 2.78 × 10−1 8.23 × 10−1 1.00 
Poliovirus 1.00 1.00 1.00 
Salmonella spp. 3.33 × 10−2 1.66 × 10−1 3.11 × 10−1 
Shigella spp. 3.65 × 10−1 5.58 × 10−1 6.53 × 10−1 
V. cholerae 6.51 × 10−1 7.83 × 10−1 8.61 × 10−1 
PathogensEmptiersMaster composters
50th percentile50th percentile95th percentile
A. lumbricoides 4.48 × 10−1 5.43 × 10−1 5.95 × 10−1 
Campylobacter spp. 2.16 × 10−1 3.83 × 10−1 4.77 × 10−1 
C. parvum 1.29 × 10−1 5.78 × 10−1 9.38 × 10−1 
E. coli O157:H7 4.87 × 10−2 1.94 × 10−1 3.44 × 10−1 
G. intestinalis 2.78 × 10−1 8.23 × 10−1 1.00 
Poliovirus 1.00 1.00 1.00 
Salmonella spp. 3.33 × 10−2 1.66 × 10−1 3.11 × 10−1 
Shigella spp. 3.65 × 10−1 5.58 × 10−1 6.53 × 10−1 
V. cholerae 6.51 × 10−1 7.83 × 10−1 8.61 × 10−1 

These results show that operators are highly exposed to a risk of infection if basic precautions are not taken. For emptiers, the highest risks are related to poliovirus (100%), V. cholerae (approximately 65%), A. lumbricoides (nearly 45%), Shigella spp. (nearly 37%), and G. intestinalis (nearly 28%). For master composters, in the realistic scenario, the highest risks are linked to poliovirus (100%), G. intestinalis (approximately 82%), V. cholerae (approximately 78%), and C. parvum (nearly 58%). In the pessimistic scenario (95th percentile), the highest risks were associated with poliovirus (100%), G. intestinalis (100%), C. parvum (nearly 94%), V. cholerae (approximately 86%), Shigella spp. (approximately 65%), and A. lumbricoides (nearly 60%).

The most likely pathogens to cause infection during an operation are ranked in descending order as follows: poliovirus > V. cholerae > G. intestinalis > A. lumbricoides > Shigella spp. This ranking is consistent with the information provided by Jean et al. (2017) on the most prevalent pathologies in the region, which are presented in the section ‘Presentation of the Study Area’.

Annual probability of infection

The results show that the yearly probability of infection is, on average, two times higher than the probability of infection per operation, equal to 1.00 for all the pathogens considered, except for E. coli O157:H7 and Salmonella spp., where it is 9.25 × 10−1 and 8.28 × 10−1, respectively, among emptiers.

The yearly risk of infection was 8,281.36–10,000 times higher than the acceptable level of risk (10−4 pppy) depending on the pathogen considered. However, it should be noted that infection does not necessarily lead to illness. The probability of illness following a given infection depends on a range of factors, including age, the host's immune system status, and previous exposure to other pathogens (De Giudici et al. 2011; U.S. EPA 2012).

Probability of illness

Probability of illness per exposure

The calculated values of the probability of illness per exposure are summarized in Table 6.

Table 6

Probability of illness per exposure

PathogensEmptiersMaster composters
50th percentile50th percentile95th percentile
A. lumbricoides 1.16 × 10−2 7.27 × 10−2 7.97 × 10−2 
Campylobacter spp. 6.06 × 10−4 8.1 × 10−3 10−2 
C. parvum 3.64 × 10−4 1.22 × 10−2 1.98 × 10−2 
E. coli O157:H7 4.55 × 10−5 1.42 × 10−3 2.52 × 10−3 
G. intestinalis 4.32 × 10−4 9.89 × 10−3 1.2 × 10−2 
Poliovirus 1.77 × 10−1 3.87 × 10−1 3.87 × 10−1 
Salmonella spp. 8.61 × 10−4 2.22 × 10−2 4.17 × 10−2 
Shigella spp. 9.44 × 10−3 7.47 × 10−2 8.75 × 10−2 
V. cholerae 5.59 × 10−2 2.27 × 10−1 2.5 × 10−1 
PathogensEmptiersMaster composters
50th percentile50th percentile95th percentile
A. lumbricoides 1.16 × 10−2 7.27 × 10−2 7.97 × 10−2 
Campylobacter spp. 6.06 × 10−4 8.1 × 10−3 10−2 
C. parvum 3.64 × 10−4 1.22 × 10−2 1.98 × 10−2 
E. coli O157:H7 4.55 × 10−5 1.42 × 10−3 2.52 × 10−3 
G. intestinalis 4.32 × 10−4 9.89 × 10−3 1.2 × 10−2 
Poliovirus 1.77 × 10−1 3.87 × 10−1 3.87 × 10−1 
Salmonella spp. 8.61 × 10−4 2.22 × 10−2 4.17 × 10−2 
Shigella spp. 9.44 × 10−3 7.47 × 10−2 8.75 × 10−2 
V. cholerae 5.59 × 10−2 2.27 × 10−1 2.5 × 10−1 

Analysis of these data revealed that the probability of illness per operation was 2.58–1,071.19 times lower than the probability of infection per operation. During each operation, the emptiers and the master composters were exposed to two major risks, namely those related to poliovirus and V. cholerae; other risks were considered moderate and/or minor. The pessimistic scenarios indicate that master composters were exposed to two highly elevated risks of illness, approximately 39% for poliovirus and 25% for V. cholera, and two major risks, almost 8% for A. lumbricoides and nearly 9% for Shigella spp. Therefore, the most likely pathogens to cause disease are ranked as follows: poliovirus > V. cholerae > Shigella spp. > A. lumbricoides.

Annual probability of illness

The results of the annual probability of illness presented in Table 7 show that the yearly probability of illness was 1.16–24.87 times higher than the probability of illness per operation and 2.58–1,071.19 times lower than the annual probability of infection. Emptiers and master composters were only exposed to two major risks, which were related to poliovirus and V. cholerae; the risks associated with the other target pathogens were, overall, classified as moderate and/or minor, ranging from 0.08 to 2.58%. It is observed that master composters were 2.18–8.48 times more exposed to microbial risk than the emptiers. This confirmed that master composters are the most exposed to microbial risk. As for the risk per operation, the most likely pathogens to cause disease were poliovirus, V. cholerae, Shigella spp., and A. lumbricoides. It is noteworthy that the results of the pessimistic scenario are identical to those of the realistic scenario in terms of the annual probability of illness.

Table 7

Annual probability of illness

PathogensEmptiersMaster composters
50th percentile50th percentile
A. lumbricoides 2.58 × 10−2 1.34 × 10−1 
Campylobacter spp. 2.81 × 10−3 2.11 × 10−2 
C. parvum 2.81 × 10−3 2.11 × 10−2 
E. coli O157:H7 8.64 × 10−4 7.33 × 10−3 
G. intestinalis 1.55 × 10−3 1.2 × 10−2 
Poliovirus 1.77 × 10−1 3.87 × 10−1 
Salmonella spp. 2.14 × 10−2 1.34 × 10−1 
Shigella spp. 2.58 × 10−2 1.34 × 10−1 
V. cholerae 8.59 × 10−2 2.9 × 10−1 
PathogensEmptiersMaster composters
50th percentile50th percentile
A. lumbricoides 2.58 × 10−2 1.34 × 10−1 
Campylobacter spp. 2.81 × 10−3 2.11 × 10−2 
C. parvum 2.81 × 10−3 2.11 × 10−2 
E. coli O157:H7 8.64 × 10−4 7.33 × 10−3 
G. intestinalis 1.55 × 10−3 1.2 × 10−2 
Poliovirus 1.77 × 10−1 3.87 × 10−1 
Salmonella spp. 2.14 × 10−2 1.34 × 10−1 
Shigella spp. 2.58 × 10−2 1.34 × 10−1 
V. cholerae 8.59 × 10−2 2.9 × 10−1 

Limitations

The QMRA is now recognized as an important tool for decision-makers in preventing infections and/or infectious illnesses related to water, excreta, and food. This tool has some limitations, such as not taking into account the potential immunity of a portion of the exposed population. However, it is important to note that its purpose is not to determine the quantity of infection and illness in a given area, but rather to assess the probability that infection and illness could occur in the area based primarily on available microbiological, epidemiological, and demographic data. This is precisely the perspective from which the QMRA was used in this study to quantitatively evaluate the potential microbial health risks associated with the use of CBTs.

Like any study of this type, this study is subject to uncertainties. Data on the concentration of pathogens in feces were not collected in Grande Plaine but mainly came from previous studies that were not carried out in Haiti. Data specific to the study area would be more relevant. Furthermore, the equation used in the QMRA framework assumes that the ingested dose is the same at each exposure and does not take into account the fact that some people who have previously been infected with certain pathogens may become immune to them (Health Canada 2019). In reality, the most vulnerable people are generally those who are immunocompromised (people with AIDS and others), seniors, pregnant women, infants, and people suffering from malnutrition (Haas et al. 1999; De Giudici et al. 2011; U.S. EPA 2012). Epidemiological and demographic data on the area (population health status, age groups, number of pregnant women, etc.) would allow for the identification of the most vulnerable groups and a more exhaustive analysis of the situation, but such data are not available in the existing literature.

Microbial risk management primarily falls under the jurisdiction of political authorities, namely the Ministry of Public Health and Population (MSPP). The MSPP is responsible for developing and enforcing barrier measures in accordance with the sanitation approaches adopted in the country to protect the health of the population. Implementing adequate hygienic and sanitary measures can significantly reduce the microbial risks associated with the use of CBTs. The measures adopted must prevent any contact with feces, such as the use of PPE (gloves, boots, and protective masks) and hand washing. Another way to prevent contact with feces is to reduce the concentration of pathogens in the feces by removing the full bucket from the CBT and allowing the sludge to dry, while another bucket is put into service right next to it. The full bucket would be covered with ash and a lid to reduce the nuisance caused by odors and harmful insects.

The study results show that the risk of infection by most of the targeted pathogenic organisms is high among operators (emptiers and master composters), which is not the case for the risk of illness. The results highlight the fact that the risks of illness associated with poliovirus, V. cholerae, and Ascaris are generally the highest, while those associated with E. coli O157:H7 and C. parvum are the lowest. The annual infection risks were found to be 8,281.36–10,000 times higher than the established acceptable risk level, while the annual disease risks ranged from 8.64 to 3,870.28 times higher than the acceptable risk level, depending on the pathogen considered. However, these results do not necessarily mean that the operators in question will be infected and/or fall ill, but rather illustrate what could happen if they do not take necessary precautions during their usual operations.

The obtained results suggest that political authorities should develop guidelines in this regard to ensure a safer use of the technology. This requires training and raising awareness in the population concerned, either by public authorities or local organizations mandated by the authorities. These actions would be a suitable lever for implementing barrier measures and self-protection mechanisms for regular monitoring of feces-composting operations to ensure that established guidelines have been respected and that the compost produced is truly sanitized.

The lead author would like to thank the Association des Originaires de Grande Plaine (AOG) for providing information about the study area with regard to the use of CBTs.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

Association des Originaires de Grande Plaine
2022
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