Geospatial analysis of the associations between environmental contamination with livestock feces and children with chronic fascioliasis in the Anta province of Cusco, Peru.

Fasciola hepatica is a neglected parasitic infection with significant human health and livestock industry impact. The Andean Altiplano harbors an estimated 50% of the Fasciola's world infection burden. There is scarce data regarding the spatial associations between different Fasciola hosts. In this project, we aimed to determine the geospatial relationships between Fasciola eggs passed in feces of different livestock species and the risk of infection among each household as a unit. We used data from a cross-sectional study evaluating children and livestock feces for Fasciola infection around households in three districts of Anta province, in the Cusco region of Peru. Each sample was geographically tagged and evaluated for fascioliasis using microscopy methods. A total of 2070 households were included, the median age was 9.1 years (6.7-11.8), 49.5% were female, and 7.2% of the households had at least one infected child. A total of 2420 livestock feces samples were evaluated. The infection rate in livestock samples was 30.9%. The highest infection rate was found in sheep with 40.8%, followed by cattle (33.8%), and swine (26.4%). The median distance between a household with an infected child to a positive animal sample was 44.6 meters (IQR 14.7-112.8) and the distance between a household with no infected children to a positive animal sample was 62.2 meters (IQR 18.3-158.6) (p = 0.025). The multivariable logistic regression adjusted by presence of poor sanitation, unsafe water consumption, altitude, and presence of multiple infected children per household demonstrated an association between household infection and any cattle feces at a 50 meters radius (Uninfected: OR 1.42 (95%CI 1.07-1.89), p = 0.017. Infected: OR 1.89 (95%CI 1.31-2.73), p = 0.001), positive cattle feces at a 100 meters radius (OR 1.35 (95% CI 1.08-1.69), p = 0.008), and negative cattle feces at a 200 meters radius (OR 1.08 (95% CI 1.01-1.15), p = 0.022). We identified potential hot and cold spots for fascioliasis in the Anta province. An association between environmental contamination with feces from different livestock species and infected children in rural households was found in our study. Local health authorities may apply this strategy to estimate the risk of infection in human populations and apply targeted interventions.

Introduction

Fasciola hepatica is the causative agent of fascioliasis in the Andes region. It is considered an emergent infectious disease, with reports suggesting the establishment of new endemic areas in locations where sporadic transmission occurred in the past [1-3]. Climate change and man-made environmental modifications are probably associated with the increased prevalence and geographic distribution of Fasciola [4]. Fascioliasis causes significant financial burden in the livestock industry around the world. Over 90% of the human burden is located in low-resource areas in small farming communities [5,6]. School age children are disproportionally vulnerable to the infection [7,8].

Fasciola hepatica has a complex life cycle that includes snail intermediate hosts and a variety of mammalian definite hosts (Fig 1). Animals and humans with fascioliasis often have overlapping endemic areas. A Venezuelan study reported that animal and human fascioliasis was associated with snail presence and consumption of untreated water [9]. Mas-Coma et al. reported a prevalence of fascioliasis between 0–65% in cattle and 0–68% in humans with geographic overlap in different host endemic areas in the Bolivian Altiplano [10-11]. In Brazil, the low report of fascioliasis in humans over 60 years differed from the high reports of animal infection in the Southern states, specifically cattle [12-13]. In part, this could be explained by lack of surveillance and diagnosis in humans, as the disease seems to be spread in an area with overlapping human and livestock disease, however epidemiology is far from being elucidated [12]. The inter-host dynamics in Fasciola infection are not fully understood and may explain the inconsistencies between studies as well as differences in transmission patterns. Although no direct fecal-oral transmission of fascioliasis is possible, the role of Fasciola’s environmental load caused by infected livestock in human transmission needs further scrutiny.

Fig 1

Lifecycle of Fasciola hepatica, reproduced with permission from DpDx CDC Fasciola spp. lifecycle [14].

In this study, we aimed to determine the geospatial association between Fasciola eggs passed in feces of different livestock species and the risk of infection among each household in the Cusco region of Peru.

Methods

Ethics statement

The Institutional Ethics Committee of Universidad Peruana Cayetano Heredia and the University of Texas Medical Branch Institutional Review Boards approved the study protocol. The consent process was performed in Quechua or Spanish language according to subject’s preference. A written and verbal informed consent was obtained from the children’s guardians and assent was obtained from children older than 6 years before any study procedure. Children with fascioliasis were referred to the Ministry of Health for treatment with triclabendazole.

We performed a cross-sectional study among children in 26 communities of the Anta Province in the Cusco region of Peru [15]. Children between 3 and 16 years of age enrolled in pre-schools and schools of the districts of Ancahuasi, Anta, and Zurite, were invited to participate between August of 2013 and August of 2018. Containers and instructions to collect only their own feces sample were provided to each child at pre-school and schools. Freshly produced feces samples were collected the next morning in consecutive days. Children that failed to produce a feces sample were given instructions again to collect a sample the next day. Each child’s household was visited within 2 weeks of enrolment. Field workers visited each household to collect demographic, socio-economic, and epidemiological data. The Simple Poverty Scorecard (Microfinance Risk Management LLC) was used to evaluate the probability of the child’s household to be under the poverty lines [16]. The entrance to the main living space of the house was geographically tagged at the time of visit. For the present study, we selected all children that provided at least one fecal sample for testing and had Global Position System (GPS) coordinates for the house where they resided regardless of infection status. Households were considered the analysis unit and houses with one or multiple children infected were defined as Fasciola positive households. The analysis was adjusted for the presence of multiple infected children in one household. If no infected children were found in a house, the household was defined as Fasciola negative household. The field workers collected up to four fresh fecal samples passed by different livestock species in the immediate vicinity of each house. Livestock samples were considered fresh if they were still moist at the time of collection. The livestock samples closest to the household were selected, geographically tagged, and classified according to the species. For the present study we focused on feces passed by cattle, swine, and sheep and selected one sample per species in each household. We were unable to ascertain if feces samples were from livestock owned by a specific household, but the proximity between the living space and the samples made ownership likely. If no GPS data was available for a livestock sample, the house coordinates were used as a proxy only for the radius analysis but not for distance analysis.

All fecal samples were collected in individual clean containers, maintained at 4–8°C, and transported as soon as possible to the laboratory at the Cusco Branch–Tropical Medicine Institute of Universidad Peruana Cayetano Heredia in Cusco city. Children’s fecal samples were preserved fresh and in 10% formalin. Fresh samples were tested by the Kato–Katz method within 24–48 hours of collection. The samples preserved in 10% formalin were tested by the Lumbreras rapid sedimentation method within a week. [17] Chronic fascioliasis in children was defined as having F. hepatica eggs in any of the samples collected. Livestock feces were analyzed by the Lumbreras rapid sedimentation method for the presence of Fasciola eggs. Internal and external quality control procedures for data collection, data management, and laboratory procedures were used in the study. Known positive and negative stool samples were introduced in the laboratory routine to evaluate technician proficiency. Positive microscopy results were confirmed by a second observer.

The statistical analysis was performed using the SAS Statistical Package v. 9.4 (SAS Institute Inc., 2013). We used ArcGIS v. 10.7.1 (ESRI, 2019) for the analysis of infection distribution and inter-host associations. We calculated frequencies, means ± standard deviation (±SD), and medians with interquartile ranges (IQR) to describe the distribution of the variables. We calculated the median distances from the different households to the closest infected or non-infected livestock feces. Livestock feces without GPS data were excluded from the radius analysis. The student-t test, Fisher/X2 test, or Mann-Whitney-U test were used accordingly to compare the households with and without Fasciola infection. A backwards logistic regression analysis was performed using Fasciola positive household as the dependent variable to evaluate the spatial association between infection status of feces samples from different livestock species. We delineated areas around each household at 50, 100, and 200-meter radius and counted the number of positive and negative livestock feces samples within each radius area. In each radius model, we included the covariates animal feces count inside each radius area per species and infection status (sheep positive, sheep negative, cattle positive, cattle negative, pig positive, and pig negative). All models were adjusted by reported poor sanitation, unsafe water intake, and presence of multiple children infected in the household. Poor sanitation was defined as defecating in the fields or using superficial pits. Unsafe water intake was defined as not consuming water from the chlorinated municipal supply. Poverty likelihood based on the Simple Poverty Scorecard was not included in the regression model due to missing values in 17.6% of the sample. We used a p value < 0.2 for variable retention in the regression model.

The hot spots, cold spots, and spatial outliers analysis was performed in two different ways, one including all animals feces and another one including animals feces and household infection status. Identification of hot and cold spots of Fasciola infected households was performed via Getis-Ord G statistics [18]. The statistic takes into account the interaction of the zone with itself plus the relationships with its surroundings and allows for a statistical significance evaluation of clustering via p-value as compared to a random distribution [19-20]. Other spatial patterns of the infection (outliers and clusters) in each household were analyzed by using Anselin Local Moran’s I [18]. This index provides data regarding each location’s risk based on statistically significant clustering patterns (high-high, low-low) and outliers (high-low, low-high) via a Z score [19]. The Z score indicates the presence of apparent similarity (clusters) or dissimilarity (outliers) as compared to that expected from random distribution. A positive Z score indicated clustering of high or low values (positive or negative cases) while a negative Z score indicated outliers. A high-low outlier represents a statistically significant high/positive value surrounded by low/negative values, while a low-high outlier represents the opposite. All maps were created under the WGS 1984 UTM Zone 18S coordinate system and Transverse Mercator as the elected projected system. A p < 0.05 was considered statistically significant.

Results

Our study population consisted of 3000 children from 2122 households. After excluding 42 households lacking GPS information and 10 households where no livestock feces samples were collected, we included 2070 individual households in the analysis (Table 1). The median age was 9.1 years (6.7–11.8) and 1025 (49.5%) were female. Most children resided in the Anta district (50.8%). The median years of education was 6 (IQR 3–11) for the mothers and 9 (IQR 6–11) for the fathers. The mean poverty score was 39.6 (±10.7) indicating that about half of the study households had a 34.4% or higher likelihood of living under a USD 3.75/day poverty line. [16] The median elevation of the households was 3382 meters (IQR 3352–3479). Overall, 7.2% of the households had at least one Fasciola infected child. From the 611 households where more than one child was tested for Fasciola infection, only 12 had multiple infected children. There were significant differences between fascioliasis positive and negative households in maternal and paternal years of education, poverty score, altitude of the household, and presence of unsafe sanitation (Table 1).

Table 1

Children demographic characteristics in Fasciola positive and negative households.

		Total (N = 2070)	Fasciola Positive (n = 148)	Fasciola Negative (n = 1922)	p-value
Median (IQR)*
Education of the father (years)		9 (6–11)	7 (6–11)	9 (6–11)	0.007
Education of the mother (years)		6 (3–11)	6 (2–6.2)	6 (4–11)	<0.001
Altitude of the house (meters)		3382 (3352–3479)	3427 (3356–3610)	3381 (3351–3477)	<0.001
Mean (+/- SD)*
Poverty score		39.6 (±10.7)	34.9 (±9.4)	40.0 (±10.7)	<0.001
N (%)*
Sex	Male	1045 (50.5)	74 (7.1)	971 (92.9)	0.902
	Female	1025 (49.5)	74 (7.2)	951 (92.8)
Location	Anta	1052 (50.8)	71 (6.7)	981 (93.3)	0.085
	Ancahuasi	745 (35.9)	64 (8.6)	681 (91.4)
	Zurite	273 (13.1)	13 (4.8)	260 (95.2)
Unsafe water consumption#	YesNo	90 (4.3)1980 (95.7)	5 (5.6)143 (7.2)	85 (94.4)1837 (92.8)	0.5484
Poor sanitation$	YesNo	578 (27.9)1492 (72.1)	56 (9.7)92 (6.2)	522 (90.3)1400 (93.8)	0.0053

+ P value based on Mann-Whitney-U, X2 as appropriate. Bolded values = significant to p< 0.05

*Median and IQR values provided if variables failed to have a normal distribution by Shapiro Wilk test

# Defined as main water source not coming from municipal supply

$ Defined as defecation in the opened field or in shallow pits.

We collected 2648 samples of livestock feces, but only 2420 had GPS information and were included for distance analysis (Table 2). Cattle was the most frequent livestock species included (40.8%), followed by swine (32.8%), and sheep (26.4%). The overall Fasciola infection rate in livestock samples was 30.9%. A similar number of feces samples were collected from Ancahuasi and Anta with no significant differences in overall prevalence (Table 2). The highest infection prevalence was found in sheep feces (258/640, 40.3%) with Ancahuasi (129/292, 44.1%) as the place with the highest prevalence followed by Anta (109/290, 37.5%) and Zurite (20/58, 34.4%). Most cattle positive feces (328/987, 33.2%) were found in Anta (153/420, 36.4%) followed by Zurite (46/131, 35.1%) and Ancahuasi (129/436, 29.6%). Swine positive feces (163/793, 20.5%) were more often found in Ancahuasi (87/353, 24.6%) followed by Anta (66/363, 18.2%) and Zurite (10/77, 12.9%) (Table 2).

Table 2

“Distribution of the livestock samples and comparison between Fasciola positive and negative samples “.

		Total (N = 2420) n (%)	Fasciola Positive (N = 749) n (%)	Fasciola Negative (N = 1671) n (%)	p-value*
Location*	Ancahuasi	1081 (44.7)	345 (31.9)	736 (68.1)	< 0.535
	Anta	1073 (44.3)	328 (30.6)	745 (69.4)
	Zurite	266 (11.0)	76 (28.6)	190 (71.4)
Species	Cattle	987 (40.8)	328 (33.2)	659 (66.8)	< 0.001
	Swine	793 (32.8)	163 (20.6)	630 (79.4)
	Sheep	640 (26.4)	258 (40.3)	382 (59.7)

*P value based on two-sided X2 tests.

Bolded values = significant to p< 0.05

The median distance from a Fasciola positive household to the closest infected livestock feces sample was 44.6 meters (IQR = 14.7–112.8), while the median distance from a negative household to the closest infected livestock feces sample was 62.2 meters (IQR = 18.3–158.6) (p = 0.025) (Table 3). There was no significant difference between the distances from a Fasciola positive or negative household to a non-infected livestock feces sample (18.7 meters (IQR = 9.1–47.4) versus 22.4 meters (IQR = 9.1–66.5), p = 0.219). The median distance from a positive household to the closest infected cattle feces sample was 85.9 meters (IQR = 32–220.8) while the distance from a negative household to the closest infected cattle feces sample was 137.4 meters (IQR = 44.5–344.1) (p = 0.006). The distance of a positive household to infected swine feces (207.3 meters (IQR = 66.6–444.2)) was significantly different to the median distance from a negative household (236.9 meters (IQR = 89–610.5)) (p = 0.044). The distances from Fasciola positive or negative households to positive sheep feces samples were not significantly different. No differences were observed between Fasciola positive or negative households and negative feces samples from any livestock species (Table 3). The logistic regression models analyzing the effect of the distance to the closest animal feces on the household infection status adjusted by multiple infections in the household and altitude of the house demonstrated an OR 0.997 (95% CI 0.995–1.000, p = 0.0174). The effect modification on the model by distance to the closest feces by livestock species demonstrated no significant relationship to the household infection status (p = 0.7053). Similarly, the effect of the distance to the closest feces by animal status was not statistically significant (p = 0.9384).

Table 3

“Median distances from Fasciola positive or negative households to closest positive or negative livestock feces sample”.

	Positive Household (median, IQR)	Negative Household (median, IQR)	p-value *
Distances to the closest livestock feces sample (meters)
Positive livestock feces	44.6 (14.7–112.8)	62.2 (18.3–158.6)	0.025
Negative livestock feces	18.7 (9.12–47.4)	22.4 (9.1–66.5)	0.219
Distances to the closest feces sample according to livestock species (meters)**
Positive cow feces	85.9 (32–220.8)	137.4 (44.5–344.1)	0.006
Positive pig feces	207.3 (66.6–444.2)	236.9 (89–610.5)	0.044
Negative sheep feces	82 (26.1–179.4)	99.2 (33–199.1)	0.235
Negative pig feces	49.1 (17–116.7)	57.5 (19.6–147.6)	0.290
Negative cow feces	48.7 (16.8–157.3)	56 (18.2–165)	0.578
Positive sheep feces	162.4 (82.7–317.2)	147.9 (57.3–373.6)	0.675

* P value calculated for Mann-Whitney-U statistics.

** Unable to report subgroup sizes, as feces might be close to more than one household.

Bolded values = significant to p< 0.05

The adjusted multivariable logistic regression using a 50-meter radius around households demonstrated a higher likelihood of finding cattle feces around infected households independently of their infection status. However, infected households had a higher likelihood of being close to Fasciola infected cattle feces (OR = 1.89 (95%CI 1.31–2.73), p = 0.007) than to Fasciola negative cattle feces (OR = 1.42 (95%CI 1.06–1.89), P = 0.017). Using a 50-meter radius around households, Fasciola positive sheep feces were associated with a lower likelihood of the household being infected with Fasciola (OR = 0.52 (95%CI 0.32–0.84), p = 0.007). Fasciola negative sheep feces (p = 0.939) and swine feces regardless of their infection status (positive p = 0.7711, negative p = 0.7968) were not associated with infection in the households (Tables 4 and S1). Using a 100-meter radius in the adjusted logistic regression model, infected households still had a higher likelihood of having Fasciola positive cattle feces in their surroundings as compared to uninfected households (OR = 1.35 (95%CI 1.08–1.69), p = 0.049) but had a decreased likelihood of having Fasciola positive sheep feces (OR 0.77 (95%CI 0.60–0.99), p = 0.042). Any swine feces and Fasciola negative cattle or sheep feces showed no association with household infection status (Tables 4 and S2). Using a 200-meter radius, infection in the household was only associated with Fasciola negative cattle feces in the adjusted analysis (OR = 1.08 (95%CI: 1.01–1.15), p = 0.022). Any swine or sheep feces, and Fasciola positive cattle feces showed no association with household infection status (Tables 4 and S3). All models were adjusted by presence of poor sanitation, unsafe water intake, and presence of multiple infectious within a household.

Table 4

“Multivariable logistic regression modeling factors assessing the likelihood of household positivity status by proximity to different types of livestock feces under different distance radius”.

Radius length *	Odds Ratio	95% Confidence Interval	p-value***
50 meters **
Cattle negative	1.42	1.07–1.89	0.017
Sheep positive	0.52	0.32–0.84	0.007
Cattle positive	1.89	1.31–2.73	0.001
100 meters **
Sheep positive	0.77	0.60–0.99	0.049
Cattle positive	1.35	1.08–1.69	0.008
200 meters **
Cattle negative	1.08	1.01–1.15	0.022

* The radius calculations included all animal feces, the house GPS was used in lieu of the feces sample GPS data when the information was missing.

** models adjusted by presence of poor sanitation, unsafe water, multiple household Fasciola infections, altitude of the household.

*** Only statistically significant values to p< 0.05 are shown

When assessing for effect of altitude in the household infection status, the models were analyzed by altitude strata at each radius using median altitude as the cutoff (S4 Table). Using a model adjusted for sanitation, safe water intake, and multiple household infections at 50, 100, and 200 meters radius, there were no variables associated with the household infection status in the lower altitude strata. At the higher altitude strata and using a 50 meter radius, the presence of cattle positive feces (OR = 2.22 (95% CI = 1.28–3.84), p = 0.0046), cattle negative feces (OR = 1.68 (95%CI = 1.16–2.43) p = 0.0058), sheep positive feces (OR = 0.59 (95%CI = 0.35–0.97), p = 0.0389) were associated with Fasciola positive household status. At the higher altitudes and using a 100 meters radius, cattle positive feces (OR = 1.51 (95%CI = 1.02–2.24), p = 0.0370), cattle negative feces (OR = 1.35 (95%CI = 1.07–1.70), p = 0.0100), and sheep positive feces (OR = 0.71 (95%CI = 0.52–0.99), p = 0.0446) remained associated with Fasciola infected households (S5 Table).

For the identification of outlier and cluster analysis of all livestock and household fascioliasis, we localized 76 high-high clusters, 726 low-low clusters, 62 high-low outliers, and 136 low-high outliers (Fig 2A). When only livestock feces were analyzed, we localized 141 high-high clusters, 338 low-low clusters, 65 high-low outliers, and 134 low-high outliers (Fig 2B). For the identification of hot/cold spots based on the confidence intervals, when livestock and household fascioliasis were considered, we identified 16 hot and 3 cold spots zones with over > 90% confidence (Fig 3A). We identified 4 hot and 3 cold spot zones when all livestock feces were considered at > 90% confidence (Fig 3B). When comparing data from households in hot and cold spots areas, hot spot households were located at higher median altitude (3574 meters (IQR = 3427.5–3764) versus 3473 meters (IQR = 3370–3497), p = 0.001), had lower socioeconomic scores (34 (IQR = 28.5–41) versus 43 (IQR = 36–49), p < 0.001), had less median years of education for the fathers (6 (IQR 5–10) versus 11 (IQR = 6–11), p < 0.001) and the mothers (4.5 (IQR = 2–6) versus 6 (IQR = 4–11), p < 0.001) than cold spot households.

Fig 2

“Outlier and Cluster Analysis map based on Anselin Local Moran’s I statistic of all livestock and human Fascioliasis in the Anta province of Cusco, Peru” [21].

(A) Map based on infected and uninfected livestock and household infection status. (B) Map based on infected and uninfected livestock feces (cattle, sheep, swine). (C) Inset showing the location of the study area (maroon dot) in the Cusco region of Peru. This map was created using ArcGIS v. 10.7.1 (ESRI, 2019). https://www.arcgis.com/home/item.html?id=67372ff42cd145319639a99152b15bc3.

Fig 3

“Significant hot/cold spot analysis of livestock and human fascioliasis in the Anta province of Cusco, Peru” [18].

(A) Map based on infected and uninfected livestock feces and household infection status. (B) Map based on infected and uninfected livestock feces (cattle, sheep, swine). (C) Inset showing the location of the study area (maroon dot) in the Cusco region of Peru. This map was created using ArcGIS v. 10.7.1 (ESRI, 2019). https://www.arcgis.com/home/item.html?id=f33a34de3a294590ab48f246e99958c9.

Discussion

Geographic coordinate systems and computational spatial analysis are important tools to study infectious diseases epidemiology [22]. Geographic information systems have been most useful for creating dynamic scenarios of infectious disease epidemics, forecasting transmission patterns of epidemic outbreaks, and assessing the effectiveness of infection control interventions [23]. Given its complex lifecycle, multiple reservoirs, patchy distribution, and emerging character, Fasciola hepatica is an ideal parasite to model using quantitative spatiotemporal analysis [24]. Using these tools, we found a positive association between environmental contamination with cattle feces and households where children with chronic fascioliasis reside. The presence of any cattle feces was associated with Fasciola infection in the household with increasing infection likelihood with decreasing distance. Cattle can pass between 60–106 pounds of manure daily which could explain a higher burden of environmental contamination compared to other livestock species [25]. Parkinson et al. found a significant correlation (R 0.769, p = 0.02) between human and cattle infection in 12 rural communities of the Bolivian Altiplano [26]. Mas Coma et al. in the same area, reported a high prevalence of cattle fascioliasis and, based on metacercaria production and infectivity, suggested that this livestock species plays an important role in maintaining transmission to human [27]. Other authors have suggested that consumption of contaminated water from natural sources or uncontrolled man-made canals is associated with fascioliasis transmission in humans and livestock [11,28]. Open defecation due to lack of latrines have been associated with Fasciola infection in observational studies in Bolivian Altiplano [11]. No direct transmission of fascioliasis between livestock and human is possible and the interactions between definitive hosts and snails are in need of further scrutiny to understand parasite transmission dynamics.

In our study, the presence of any cattle feces around the house, in particular Fasciola infected cattle feces, increased the likelihood of finding infection in the household and finding Fasciola infected sheep feces was associated with a decreased likelihood. These associations were maintained only at the higher altitude strata in the altitude stratified analysis. Higher altitude has been correlated with higher rates of fascioliasis likely due to increased survival of the intermediate host, longer cercarial shedding periods, and higher cercarial production [29]. Studies in South America have suggested an important role of sheep as Fasciola reservoirs at different altitudes based on infection prevalence [30]. Similarly, in our study, the prevalence of infection in sheep samples was the highest, but overall the proportion of sheep feces collected was the smallest compared to cattle and swine. It is unclear if this could have introduced sampling bias affecting our results. In the Anta province, sheep owners with the largest number of animals tend to keep herds close to pastures and away from their households especially at lower altitudes which may have also affected the evaluation of the environmental burden of sheep feces containing Fasciola eggs. Another potential explanation is a higher socioeconomic status among sheep owners which has been demonstrated to decrease the risk of infection in children [15]. The unexpected inverse association between household fascioliasis and sheep positive feces emphasize the complexities of Fasciola circulation between hosts and the need for further research on transmission.

Infected animal feces were significantly closer to infected households as compared to non-infected feces, especially cattle and swine feces. Experimental studies involving Fasciola eggs of sheep, cattle, and pigs have shown no differences in infection intensity or infectivity rates [30-31]. The particular association with cattle could be partially explained by the high number of this livestock species in the area probably constituting the main reservoir of fascioliasis in the Anta province and having a major role in transmission [11]. Using spatial analysis, we identified areas with high and low transmission of disease [32,33]. The large number of low-low outliers reflects the confidence on the locations of low risk of the disease. The small number of high-high outliers as compared to large number of “hot spots” when human data was added to livestock data, may suggest a discrepancy between human and animal risk of infection. In our study, the rates of human fascioliasis were considerably lower compared to the rates of animal fascioliasis. Areas of low-high and high-low transmission, remained similar when more data was added suggesting that in some locations of the Anta province infection risk is not well-defined [33]. In studies in Bolivia, the areas with higher prevalence of animal and human disease were located near water sources [10]. There is a lack of spatial analysis data about fascioliasis in South America and extrapolation of data from European studies might not be appropriate given differences in epidemiological behaviors. For example, spatial analysis done on Danish cattle herds showed that the presence of streams, wetlands, and pastures had a significant association with the presence of livestock infection [34]. While a study based in Switzerland, demonstrated that cattle were most likely infected in streams as they correspond to areas where the snail prevalence is higher [35]. In this study, we did not account for distance to large water bodies, as this information was not available. In Figs 2B or 3B, we can locate a large water body (northeast of the border) where the small number of children sampled failed to show a higher infection rate. This could indicate a prominent presence of snails in smaller bodies of water influencing the infection prevalence in the area [36]. The hot spots were more likely to be in locations where poverty likelihood and altitude were higher and parent’s education was lower compared to cold spots. Previous studies by our group, have associated lower maternal education status and lower socioeconomic status with increased Fasciola infection rates [15]. In the future, comparing the outliers with the differences in their corresponding environmental conditions could be analyzed to define other risk factors for fascioliasis.

There are several limitations in our study that need to be considered. We did not have data regarding distance to different waterbodies (small or large) which may have limited our analysis model. In our study, we were aiming to understand the rarely explored relationship between livestock and human fascioliasis. Livestock feces found around the households was collected and no samples were collected from individual animals. This precluded the calculation of fascioliasis prevalence in the different livestock species. Due to missing data, we were unable to use the socioeconomic score in the predictive model selection. However, elevation of the household correlated well with the socioeconomic score and we used it as a surrogate measure of poverty. Despite the relatively large sample of children tested for fascioliasis, the number of infected subjects was low limiting our statistical power. The lack of specific topographic variables for the predictive model could have decreased the accuracy of our risk estimates.

Conclusion

In the Anta province of Cusco, the spatial distribution of Fasciola hepatica eggs in the environment was associated with the distribution of human fascioliasis. There was a different risk depending on livestock species with egg positive cattle and swine feces being independently associated with fascioliasis in the household. Further research to characterize areas where environmental egg contamination clusters with human cases is need to understand the dynamics of parasite transmission and identify potential strategies to optimize surveillance. The complexities of Fasciola infection among human and livestock emphasize the need for a one-health approach to research and control.

Number of livestock feces inside the 50 m buffer as per household status.

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Number of livestock feces inside the 100 m buffer as per household status.

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Number of livestock feces inside the 200 m buffer as per household status.

(DOCX)

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Adjusted univariate regression model for sheep positivity feces on household positivity status stratified by altitude of the household.

(DOCX)

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Adjusted Multivariate Logistic regression analysis stratified by higher or lower altitudes above the sea level associated with amount of livestock feces around different radiuses around the household.

(DOCX)

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7 Feb 2022

Dear Dr. Cabada,

Thank you very much for submitting your manuscript "“Geospatial analysis of the associations between environmental contamination with Fasciola infected livestock feces and children in the Anta province of Cusco, Peru.”" for consideration at PLOS Neglected Tropical Diseases. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

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Sincerely,

Uwem Friday Ekpo, PhD

Associate Editor

PLOS Neglected Tropical Diseases

Dileepa Ediriweera, PhD

Deputy Editor

PLOS Neglected Tropical Diseases

***********************

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: Overall comments are that while the objective of the study was clear, the description of the study design was confusing and the statistical methods were under-described and inadequate for meeting the objectives. The sample size should be adequate and there are no obvious issues with ethical requirements.

Specific comments on methods:

Feces and stool are both used in this paper. Pick one and be consistent in use.

Lines 95-96. More detail is needed on how the three fecal samples were collected, especially from older children, and when this occurred in the context of a cross-sectional household visit. Were there repeat visits?

Line 98. My understanding is that children were enrolled, then had 3 stools collected and tested. However, line 98 suggests that this study was instead a two-staged process with pre-testing of lots of kids to identify “households with multiple children infected with Fasciola”, followed by some sort of secondary enrollment of “only one infected child selected to participate while other children in the household with or without the infection were excluded.” By default if you are testing child stool for Fasciola, then they are participating. Please revise to improve clarity.

Lines 100-102. Are these animals of the participating household or potentially those of a neighbor? Also, were any criteria used to eliminate animal stool that seemed old or desiccated?

Line 108. “Children’s” or “Child”

Lines 113-114. Related to my comment about lack of clarity on study enrollment, the difference between “Chronic fascioliasis” and “Positive Fasciola household” outcomes is not clear from the study design, and the individual-level fascioliasis outcome is not reported or analyzed as an outcome anywhere in the analysis, although it should be.

Lines 124-131. Why are you using backward regression modeling with only four (three in practice) variables included in the model? If this was truly the process, then you need to provide the full stepwise rules you used for evaluating model fit at each stage of model simplification. Related to this and my comment above, why has the model not also been adjusted for multi-infections among household members and proximity of water bodies with snails? Children could be infected by siblings shedding eggs into the environment as easily as from animals. And, as you clearly stated in the introduction, none of this matters without the intermediate snail host. The model is poorly examining the obligate components of the transmission cycle (Figure 1).

Lines 128-129. The count variable is not reported anywhere in the results. If feces counts for each species are used to code species infection status variables also included in the model, these needs to be described.

Lines 131-136. This is not an adequate description of the cluster analysis intent and method. What specific relationships are you probing to define as hot zone or cold zone? Jumping ahead to Figure 2, it appears your research questions include animal to animal geospatial relationships and animal to household. This all needs to be described.

Reviewer #2: Needs a little more on snail habitats

Reviewer #3: Objectives are clear.

Design not appropriate.

Population appropriate.

Sample size almost sufficiient.

Statistical analyses correct.

Ethical approvals need more data.

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Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: The analysis do aim to address the study objective but do not match the analysis plan as described in methods, the results lack detail, and Tables and Figures lack organization and sufficient detail to be free standing summaries of presented material.

Specific comments on results:

Line 146-147. Please also report how this sample size was achieved in terms of number of children per year, whether there were year-to-year differences in prevalence, and how often you came across multiple children testing positive in the same household. Also, “households” should be singular.

Table 1. Age is reported as mean (SD) while other quantitative variables with a potentially normally distributed distribution like altitude and poverty score are in median (IQR). Did these two variables in particular not follow a Gaussian distribution?

Lines 161-165. Were there any differences in types of animals by geographic district that might occur for example by clusters of livestock production sectors?

Line 165-166. The statement “Most feces samples were collected in the Ancahuasi district and more samples were positive in this district” does not accurately reflect what is shown in Table 2. There are extremely minimal differences in number of samples collected and positivity rate between Ancahuasi and Anta.

Table 2. There is an “n” missing in “Total (N = 2420)(%)”. The Table header could be made clearer by putting either the (total N) or the n (%) on its own row.

Lines 170-182 and Table 3. I believe this analysis is missing a final step for interpretability. Individual tests for differences in distance by positive or negative animal status and household outcome is confusing in presentation and resulting in non-interpretable significant associations. Closer scrutiny of the distances shows that households with a fasciola positive child are more likely to be close to cattle and swine feces, regardless of whether the animal feces itself was positive for the parasite, with sheep going in the opposite direction. The lack of statistical association in the animal-negative comparisons is mostly likely limited power to detect differences due to small sample sizes in the household-positive group, although the lack of reporting on group size makes this difficult to tell. A better way to do this would be to estimate the effect of distance to the animal feces sample on household positivity status, then test for effect modification by animal stool sample testing status. If the relationship between distance and outcome is modified by animal stool testing status, then report sub-group effects. Or, calculate the sub-group conditional probabilities for increases or decreases in distance on household status and the risk ratio for the probability of positive household status where the animal stool contains parasites versus where it does not. For cattle and swine, you will likely see no impact of animal feces positivity on the distance to outcome relationship, although sheep might be interesting. Be sure to adjust these models for altitude and (see above) presence of other positive children in household and water bodies.

Table 3. Report your sub-group sizes and be consistent in presenting data. Either always do negative then positive feces or vice a versa. Similarly, your species specific positives and negatives are not organized in any specific way, much less aligned in flow with the way material is presented in the results text.

Line 195. Should be “Table 4”.

Line 189-204 and Table 4. I like the idea of this spatially comprehensive analysis approach more than the oversimplified analysis of median distance with one animal feces sample, as commented on above. However, you have not yet demonstrated the value of this multi-animal stool assessment approach by describing overall count or proportion of positive animal stools out of total counted within 50, 100, or 200 for positive and negative households. Another approach if your total counts are low is to use ratio of positive to negative at <50 meters, and <100 meters, and <200 meters. This could be put in a table in supplemental materials, but is necessary for defending exposure variable validity and for explaining why you simplified what you said you modeled (“overall count of animal feces” and “the individual species sample infection status”) vs what you report (sheep positive, cattle positive, swine positive). For that matter, I cannot figure out how the effects for the variables reported relate to the variables included in the model. A second point on the analysis method is that on lines 124-126 you stated you were using a backward selection modeling process. Backwards selection suggests you were seeking model parsimony, rather than fully adjusted effects. If that is the case, you must describe whether variables were removed, in what order, and how much that improved model fit. Considering you were only starting with the 2 variables above and altitude, I am not clear what the purpose for backwards selection was.

Table 4. (1) Please reorder the types of animals so that they are consistent across each distance unit, either Swine, Sheep, then Cattle, or Sheep, Swine, then Cattle. The 200 meter comparison lacks Sheep positivity altogether with no explanation. (2) The title is not descriptive of the analysis, which assessed the likelihood or odds of household positivity status by proximity to different types of domesticated animal vectors. (3) The table must be free standing, meaning when you say “multivariable”, you need to clarify in the footnote what variables were included in the model, either as exposure variables or interest or confounders.

Table 4. Please clarify what you mean by using household GPS where animal stool gps data was missing and how this relates to the (more statistically appropriate) statement on line 122 that “Livestock feces without GPS data were excluded from the buffer/radius analysis”.

Lines 208-214 and Figure 2. Define high-high, low-low, etc. in methods and Figure footnotes, and be sure to provide some context to help readers interpret what clusters of these mean in the figures. Also, you cannot cite Figure 2B before 2A; either flip your pictures or your text so 2a comes first.

Reviewer #2: Yes

Reviewer #3: The analysis presented matches the analysis plan.

Results do not consider all factors.

Figures to be pronouncedly improved.

Tables need clarifications.

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Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: I do not believe the results, as they exist currently, support the conclusions. The limitations are not complete. Finally, the impact of the results on broader understanding is limited by not considering the role of within-household transmission and the conditional dependency of transmission on intermediate snail vectors in the explicit study design and analysis plan.

Lines 240-243. Per my comments above about weaknesses in the analysis approach, this conclusion is not yet supported by the evidence.

Lines 279-281. Where is this information? Any sort of assessment about location of water bodies that could harbor the snail vectors would address my comment about this being lacking as an adjustment factor in the analysis.

Lines 281-284. Please explain contextual situation in Cusco further to explain how poverty in safe drinking water and sanitation would facilitate completion of the Fasciola life cycle.

Lines 288-295. The limitation should also acknowledge that analysis did not account for the role of other household members as influences on the index child’s infection status, nor the dependency of any human to human or animal to human transmission on availability of intermediate snail vectors. Directionality cannot be inferred from this cross sectional study design, and it is also possible that children could be sources of infections in the animals in their community. Adjusting for household conditions like use of latrines/toilets would have improved such inference.

Reviewer #2: (No Response)

Reviewer #3: Conclusions pose doubts because of methodology and overlook of factors.

Limitations section to be widely extended.

Authors discussion needs improvements.

Public health relevance is addressed.

--------------------

Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #1: These recommendations are embedded in the section by section review, but generally, the tables lacked organization and both tables and Figures needed footnotes and improvements in titles.

Reviewer #2: (No Response)

Reviewer #3: See below

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Summary and General Comments

Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.

Reviewer #1: A geospatial analysis of proximity to animal feces with parasites on human infection is valuable. The biggest issue is that while authors used rigor in confirming Fasciola infection in a large subset of children and local animals, they did not apply such rigor to measuring and analyzing all required conditions for Fasciola transmission. This lack in comprehensive analysis was surprising given the acknowledgement of the complex life cycle in the introduction and Figure 1. The dependency of Fasciola on a intermediate host for maturation and infectivity means that all relationships between infected and susceptible hosts is by default conditional on the host presence, and therefore the mechanism underlying and validating any relationships between animal feces and households with infections is unclear. A vague reference in discussion to location of water bodies suggests this ecological data might be available to authors but the manuscript as it stands does not suggest proof of mechanistic relationships can be resolved. Another major issue is that while multiple within-household infections was considered at some point in study design, it was not considered important as a confounder in the detection of Fasciola among siblings and nearby domestic animals and when and why multiple children in the household were tested is unclear. This may be a point that authors can easily address through adjustment of the analysis approach and would improve the interpretability of the results. Last, the methodological section suggests authors could greatly improve analysis by seeking council from a biostatistician or epidemiologist.

Reviewer #2: (No Response)

Reviewer #3: This manuscript has the purpose of evaluating the spatial relationships between fascioliasis infection in children and the environmental burden of Fasciola eggs passed by different livestock in the Cusco region of Peru, that is the presence of liver fluke eggs on fecal samples collected on the soil.

The manuscript includes many methodological problems, the baseline knowledge on the scenario is clearly insufficient regarding key factors, and there are several misinterpretations of basic concepts of the disease. Nevertheless, the originality of the approach merits an effort in the way for the needed improvement.

1.- The first general consideration refers to the literature used. One may conclude that authors consider that human fascioliasis follows similar transmission and epidemiological characteristics everywhere. This disease is markedly heterogeneous in epidemiological facies. So, using examples from Nepal, Pakistan, Egypt, Iran, Ghana, Zambia, Indonesia, Denmark, Switzerland, or Malaysia, has no sense here. Fascioliasis in Andean areas differs very pronouncedly from fascioliasis in the aforementioned countries. So, the literature should be reviewed and articles referring to fascioliasis in maximum South America used, above all concerning altitude areas in Andean zones.

2.- The second problem concerns the lack of knowledge, or at least the overlooking of key epidemiological aspects, regarding the study area:

A) The origin of human infection is mainly through contaminated vegetables, predominantly freshwater plants, but also drinking of natural water or beverages made with sylvatic plants and natural water. In all the human endemic areas in the Andes, the infection of children occurs mainly (i) along the way to and from the school, (ii) when accompanying livestock for grazing and drinking in freshwater collections, and (iii) at home when eating contaminated plants and drinking natural water collected outside. This means that taking the household as the reference for such an analysis is not appropriate, i.e. for instance the distance from the school may be more important.

B) The dependence of the rural inhabitants from natural water sources lead them to choose places for their dwelling located close to freshwater collections. These freshwater collections used to be inhabited by lymnaeid vectors and to be visited by livestock for drinking. So, these foci act for the infection of both humans and animals.

C) In these rural areas, livestock moves freely around. Fences are not used, so that cattle, sheep and pigs use to appear mixed in the same grazing area. And the image one gets from a given place may differ one day from another, and one season from another Thus, no sense to compare with countries where animals are strictly controlled in farms, livestock maintained within fences, and separated according to species.

D) Rural inhabitants give, however, more importance to cattle because of milk, meat and even family prestige inside the community. This means that the type of ownership by each family is a crucial factor to be considered. The infection risk in a household of a big owner may be different from that of a small owner.

The following two papers may help in showing the aforementioned aspects of disease heterogeneity in humans worldwide and its complexity in the neighboring Bolivia:

Angles R. et al., 2022. One Health action against human fascioliasis in the Bolivian Altiplano: Food, water, housing, behavioural traditions, social aspects, and livestock management linked to disease transmission and infection sources. Int. J. Environ. Res. Public Health 19, 1120, 44 pp.

Mas-Coma S. et al., 2018. Human fascioliasis infection sources, their diversity, incidence factors, analytical methods and prevention measures. Parasitology 145 (13, Special Issue): 1665-1699.

3.- Lines 47-48: "The factors associated with transmission of the disease among humans have not been well described." This is no true. Add "... in the area studied." or similar.

4.- Lines 74-77: "However, studies in areas of the world with lower prevalence of infection have reached different conclusions.[11] A study in Qena, Egypt showed an animal prevalence ranging from 17.2 to 33.7%, but no evidence of human fascioliasis in the same region.[12]." This has no sense here. Just for information: human fascioliasis concentrates in Lower Egypt, that is, in the widely irrigated Nile Delta, where G. truncatula with other lymnaeid vectors are present, whereas Qena is in Upper Egypt, surrounded by desert and with only R. natalensis present. In the Nile Delta, human infection intensities similar to those in the Andes have recently been found. See the following:

Periago M.V. et al., 2021. Very high fascioliasis intensities in schoolchildren of Nile Delta governorates: The Old World highest burdens found in lowlands. Pathogens, 10: 1210, 20 pp.

5.- Line 78: Differences of results between studies are not inconsistencies, but the reflection of different transmission patterns and epidemiological situations. There are many articles on that.

6.- Figure 1: This drawing cannot be accepted and reflects a lack of expertise on fascioliasis. This should be corrected, if not there are afterwards students reproducing it in their master's theses, etc. Please correct the following: (i) eggs do not seek an intermediate host, this is for the hatched miracidium to do; (ii) the drawing of the snail represents a terrestrial snail, with eyes on the tips of the tentacles and the shell of an helicid or planorbid; when one sees a lymnaeid, one never forgets it; (iii) illustrating a cercaria by means of a drop does not appear to be logical; please substitute the drops by simplified cercariae with their body and long tail; (iv) cercariae do not infest aquatic plants, it is up to the encysted metacercariae to attach to the vegetables.

7.- Line 114: How and where the livestock feces were collected should be specified in detail. Simply collecting them from the soil? Cattle feces are well visible, but feces from sheep and pigs may be easily overlooked. This may underlie an important bias in the results, because of inadvertently giving more weight to cattle. How was this solved?

8.- Line 115: Which quality control procedure was used?

9.- Line 127: How were areas around each household at 50, 100, and 200-meter radius delineated. In the map with Google Earth? If in the field, how were the 200 m measured?

10.- Line 137-138: The numbers and dates of the official approval documents should be added.

11.- Table 1: It needs clarifications to be added to the legend. For instance, parentheses sometimes seem to include means, but in other cases they refer to ranges.

12.- Table 3: Something does no fit well here. Positive feces in sheep and pig are clearly higher than positive feces in cattle. This contradicts the conclusion of the manuscript.

13.- Figure 2: There is no way to know which color of the circles concern cattle, sheep and pigs. Subfigure C has no sufficient resolution.

14.- Figure 3: Similar interpretation problem as in Figure 2.

15.- Lines 243-245: More manure per cow does not mean a higher number of liver fluke eggs, but eggs more diluted.

16.- Line 251: Add "in the Cuzco area" or similar. In the Bolivian Altiplano, all these aspects are already well elucidated.

17.- Lines 264-265: "Infected animal feces were significantly closer to infected households as compared to non infected stool, especially cattle and swine feces." This means that dwellings are located close to freshwater collections, as in the Altiplano.

18.- Lines 276-279: Delete these examples about Denmark and Switzerland. Situations are not comparable.

19.- Lines 282-284: Again, for the same reason, delete the example of Malaysia, which moreover deals on intestinal monoxenous helminths. Same with the example of Iran. Use the aforementioned reference of Angles et al. on the Altiplano to refer to the link with the socioeconomic status. There is also an article on that aspect in Cajamarca - see in references of Angles et al.

20.- Lines 288-295: This section is a good place to again refer to what has been noted above in points 2 A-D, although these crucial aspects should be referred to at least in the Discussion.

21.- Lines 297-298: Add "... eggs shed by cattle, sheep and goats" to avoid confusion with human stools. Outdoor defecation by rural inhabitants is commonly practiced in the Andean altitude areas.

22.- Line 301: Change "need" to "needed".

23.- Line 302: Change "surveillance" to "control measures".

--------------------

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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21 Apr 2022

Submitted filename: REVIEW RESPONSES PLOS NTDS 2022.docx

Click here for additional data file.

14 May 2022

Dear Dr. Cabada,

We are pleased to inform you that your manuscript '“Geospatial analysis of the associations between environmental contamination with Fasciola infected livestock feces and children in the Anta province of Cusco, Peru.”' has been provisionally accepted for publication in PLOS Neglected Tropical Diseases.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

Best regards,

Uwem Friday Ekpo, PhD

Associate Editor

PLOS Neglected Tropical Diseases

Dileepa Ediriweera

Deputy Editor

PLOS Neglected Tropical Diseases

***********************************************************

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #2: (No Response)

Reviewer #3: All Ok

**********

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #2: (No Response)

Reviewer #3: All Ok

**********

Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #2: (No Response)

Reviewer #3: All Ok

**********

Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #2: (No Response)

Reviewer #3: All Ok

**********

Summary and General Comments

Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.

Reviewer #2: Revision is satisfactory

Reviewer #3: This new version has clarified the problems raised by this reviewer and the manuscript has markedly improved its contents.

Just to help a little bit more with additional information (not for manuscript modification):

1. Lines 85-87: Leave the text as it is if you want, but, just for your information, the reason for the differences inside Brazil have already been elucidated molecularly, experimentally and in the field. Indeed, from the point of view of fascioliasis Brazil appears to be a continuation of Uruguay because of historical reasons. Unfortunately Schwantes et al. 2019 overlooked the corresponding study were all this is clarified. The main factors causing the differences are altitude and snail vector species. See the following:

Bargues et al., 2017. DNA multigene characterization of Fasciola hepatica and Lymnaea neotropica and its fascioliasis transmission capacity in Uruguay, with historical correlation, human report review and infection risk analysis. PLoS Neglected Tropical Diseases, 11 (2): e0005352 (33 pp.).

2. Discussion, line 363: Just for your information, lymnaeids at altitude are found in small water bodies, not in large ones. See the following:

Bargues et al., 2021. One Health initiative in the Bolivian Altiplano human fascioliasis hyperendemic area: Lymnaeid biology, population dynamics, microecology and climatic factor influences. Brazilian Journal of Veterinary Parasitology, 30 (2): e025620 (24 pp.).

3. Discussion, line 366-368: For your information, the significant direct relationship between altitude and prevalences in children has already been established in Andean valleys where an altitudinal gradient can be assessed. See the following:

Gonzalez et al., 2011. Hyperendemic human fascioliasis in Andean valleys: An altitudinal transect analysis in children of Cajamarca province, Peru. Acta Tropica, 120: 119-129.

**********

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Reviewer #2: No

Reviewer #3: No

13 Jun 2022

Dear Dr. Cabada,

We are delighted to inform you that your manuscript, "“Geospatial analysis of the associations between environmental contamination with Fasciola infected livestock feces and children in the Anta province of Cusco, Peru.”," has been formally accepted for publication in PLOS Neglected Tropical Diseases.

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Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

Best regards,

Shaden Kamhawi

co-Editor-in-Chief

PLOS Neglected Tropical Diseases

Paul Brindley

co-Editor-in-Chief

PLOS Neglected Tropical Diseases