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. 2020 Mar 17;9:e54012. doi: 10.7554/eLife.54012

Impact of community piped water coverage on re-infection with urogenital schistosomiasis in rural South Africa

Polycarp Mogeni 1,2,3,✉, Alain Vandormael 1,2,3,4, Diego Cuadros 5,6, Christopher Appleton 7, Frank Tanser 1,2,8
Editors: Eduardo Franco9, Eduardo Franco10
PMCID: PMC7108860  PMID: 32178761

Abstract

Previously, we demonstrated that coverage of piped water in the seven years preceding a parasitological survey was strongly predictive of Schistosomiasis haematobium infection in a nested cohort of 1976 primary school children (Tanser, 2018). Here, we report on the prospective follow up of infected members of this nested cohort (N = 333) for two successive rounds following treatment. Using a negative binomial regression fitted to egg count data, we found that every percentage point increase in piped water coverage was associated with 4.4% decline in intensity of re-infection (incidence rate ratio = 0.96, 95% CI: 0.93–0.98, p=0.004) among the treated children. We therefore provide further compelling evidence in support of the scaleup of piped water as an effective control strategy against Schistosoma haematobium transmission.

Research organism: Other

Introduction

About 243 million people are infected with schistosomiasis worldwide, of whom ~ 93% reside in sub-Saharan Africa where children carry the greatest burden of the disease. In the tropics and subtropics, schistosomiasis is a major cause of disability among neglected tropical diseases (NTDs) accounting for 1.43 million disability-adjusted life-years lost in 2017 (GBD 2017 Disease and Injury Incidence and Prevalence Collaborators, 2018). Infection occurs when trematodes of the genus Schistosoma shed by infected freshwater snails (an intermediate host) penetrate the skin upon contact with infested water (Colley et al., 2014). Intensity of infection in the human host is a function of the parasite load and can indirectly be quantified by the number of eggs excreted. Host variations in worm burden has been attributed to recent chemoprophylaxis, heterogeneities in exposure and host susceptibility (Colley et al., 2014). Most human infections in sub-Saharan Africa (SSA) are due to Schistosoma mansoni, which causes intestinal schistosomiasis and Schistosoma haematobium responsible for urogenital schistosomiasis (Lai et al., 2015). However, Schistosoma haematobium has the widest geographical coverage in SSA and is the main cause of infection in the Hlabisa sub-district, where our study is based (Saathoff et al., 2004).

In our previous work, we assessed the impact of piped water coverage on the risk of Schistosoma haematobium infection in a rural South African community (Tanser et al., 2018). We argued that a measure of piped water access by individuals/households, which is commonly used in the existing literature (Grimes et al., 2014), is less sensitive than a measure of piped water coverage at the community level. Our hypothesis is that a higher community coverage of piped water reduces both an individual’s exposure to parasitic agents (direct benefit) as well as the number of contacts that infectious individuals in the surrounding have with open water bodies, thus decreasing the overall transmission intensity of Schistosoma haematobium within the community (indirect benefit).

We previously used novel geostatistical methods, annual population-based surveillance data, and a parasitological survey to quantify the risk of Schistosoma haematobium infection in a nested cohort of 1976 primary school children (Tanser et al., 2018). In this baseline parasitological survey, we showed that every percentage increase in community piped water (in the prior 7 years) was associated with a 2.5% decrease in the odds of Schistosoma haematobium infection. However, we did not determine if community piped water coverage reduced re-infection rates among the same children who were treated with praziquantel during the baseline survey.

Here, we report the results of two consecutive rounds of follow-up of infected children, which were undertaken between September and December of 2007 (round 1), and between April and August of 2008 (round 2) respectively. To the best of our knowledge, this is the first study to systematically evaluate the impact of community piped water coverage on Schistosoma haematobium re-infection rates following treatment with praziquantel and therefore address a major public health evidence gap highlighted in a recent review (Grimes et al., 2015). A strong relationship would provide compelling evidence for the protective effect of increased piped water coverage and have broad implications for the treatment and management of Schistosoma haematobium in resource-limited settings.

Results

During the baseline parasitological survey, a total of 2105 children from all 33 primary schools located in a contiguous geographical area in rural KwaZulu-Natal consented to participate in the study. Of these participants, 1976 were residents in the study area (Figure 1). The prevalence of baseline infection was 16.9% (95%CI: 15.2–18.6) (Figure 1). Further detailed baseline characteristics of the study participants and baseline analyses are presented in our previously reported findings (Tanser et al., 2018).

Figure 1. Baseline screening for Schistosoma haematobium infection and re-infection follow-up rounds of the study participants.

Figure 1.

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Participants who were not linked to the population-based cohort study were excluded from the baseline analysis presented in our previous analyses (Tanser et al., 2018) and those not treated for infection at baseline were excluded from the re-infection analysis. Participants who were treated at baseline but not screened at round 1 were eligible for screening at round 2 if they provided informed consent.

Re-infection cohort characteristics

Out of the 333 microscopically confirmed infections at baseline, 253 (76%) consented to screening for Schistosoma haematobium re-infection at round 1 and 125 consented for screening at round 2 (Figure 1). The prevalence of light re-infection and heavy re-infection was 15.4% (95%CI: 11.2–20.5) and 8.7% (95%CI: 5.5–12.9) for round 1, and 12.8% (95%CI: 7.5–20.0) and 6.4% (95%CI: 2.8–12.2) for round 2, respectively. The geometric mean egg counts were 16.7 (95%CI: 9.4–29.6) eggs/10 mL for round 1 and 18.2 (95%CI: 6.5–50.6) eggs/10 mL for round 2. Detailed characteristics of study participants for each follow-up round are presented in Table 1 and Figure 2A.

Table 1. Characteristics of children enrolled in the re-infection cohort.

Follow-up round 1
(N = 253)		 Follow-up round 2
(N = 125)
Total Infected n(%) (95% CI) Total Infected n(%) (95% CI)
Overall 253 61 (24.1) (19.0–29.9) 125 24 (19.2) (12.7–27.2)
Gender
Female 86 16 (18.6) (11.0–28.4) 33 5 (15.2) (5.1–31.9)
Male 167 45 (27.0) (20.4–34.3) 92 19 (20.7 (12.9–30.4)
Age group
≤10 41 13 (31.7) (18.1–48.1) 28 7 (25.0) (10.7–44.9)
11 71 15 (21.1) (12.3–32.4) 34 5 (14.7) (5.0–31.1)
12 74 18 (24.3) (15.1–35.7) 29 6 (20.7) (8.0–39.7)
≥13 67 15 (22.4) (13.1–34.2) 34 6 (17.6) (6.8–34.5)
Community piped water coverage (%)
<70 58 17 (29.3) (18.1–42.7) 31 5 (16.1) (5.5–33.7)
70 - < 90 66 13 (19.7) (10.9–31.3) 28 8 (28.6) (13.2–48.7)
≥90 129 31 (24.0) (16.9–32.3) 66 11 (16.6) (8.6–27.9)
Altitude class (meters)
<50 17 5 (29.4) (10.3–56.0) 14 6 (42.9) (17.7–71.1)
50–100 140 31 (22.1) (15.6–29.9) 62 11 (17.7) (9.2–29.5)
100–150 84 21 (25.0) (16.2–35.6) 42 5 (11.9) (4.0–25.6)
150–200 7 2 (28.6) (3.7–71.0) 3 0 (0) (0–70.8)
≥200 5 2 (40.0) (5.3–85.3) 4 2 (50.0) (6.8–93.2)
Distance water body class
<1 km 92 20 (21.7) (13.8–31.6) 46 11 (23.9) (12.6–38.8)
1–2 km 98 25 (25.5) (17.2–35.3) 42 8 (19.1) (8.6–34.1)
2–3 km 46 13 (28.3) (16.0–43.5) 26 5 (19.2) (6.6–39.4)
>3 km 17 3 (17.7) (3.8–43.4) 11 0 (0) (0–28.5)
School grade
Grade 5 144 37 (25.7) (18.8–33.6) 74 13 (17.6) (9.7–28.2)
Grade 6 109 24 (22.0) (14.6–31.0) 51 11 (21.6) (11.3–35.3)
Toilet
No Toilet 47 13 (27.7) (15.6–42.6) 23 1 (4.3) (0.1–21.9)
Toilet 206 48 (23.3) (17.7–29.7) 102 23 (22.6) (14.9–31.9)
Land cover classification
Closed shrubland 145 35 (24.1) (17.4–31.9) 65 12 (18.5) (9.9–30.0)
Open shrubland 59 14 (23.7) (13.6–36.6) 34 6 (17.7) (6.8–34.5)
Sparse shrubland 41 11 (26.8) (14.2–42.9) 19 6 (31.6) (12.6–56.6)
Thickett 8 1 (12.50) (0.3–52.7) 7 0 (0) (0–41.0)
Baseline intensity of infection
Light infection 105 35 (33.3) (24.4–43.2) 50 12 (24.0) (13.1–38.2)
Heavy infection 148 26 (17.6) (11.8–24.7) 75 12 (16.0) (8.6–26.3)
Sample size (N) 253 125
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Figure 2. Histograms of Schistosoma haematobium egg counts and community piped water coverage for the re-infection cohort participants (n = 378).

Figure 2.

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Panel (A) shows the distribution of egg counts/10 mL among children observed at follow-up round 1 and 2, and panel (B) shows the distribution of community piped water coverage among all study participants. Community piped water coverage in the community surrounding each child was derived from the population-based 2007 piped water use survey conducted in all households in the study area.

In the pooled analysis (n = 378), 119 (31.5%) were girls and 69 (18.3%) were below 11 years of age. Overall the rate of re-infection was 36 (95%CI: 27–48) infections/100 person-years of follow-up. The median community piped water coverage in 2007 was 91.2% (inter quartile range (IQR), 71.0–97.7) (Figure 2B) and was geographical heterogeneous (Tanser et al., 2018). As we previously noted, the proportion of children with heavy infection at baseline increased from 5.1% (95%CI: 1.9–10.7) among children ≤ 9 years old to 14.8% (95%CI: 10.3–20.4) among children ≥ 14 years of age (χ2-test-of-trend=22.96, p<0.001) (Tanser et al., 2018). In contrast, in the re-infection cohort, the proportion of heavy re-infection decreased with increasing age, from 13.0% (95%CI: 6.1–23.3) among children < 11 years of age to 6.9% (95%CI: 2.8–13.8) among children > 12 years of age (χ2-test-of-trend=3.317, p=0.3454) although with lower statistical power. The decline in re-infection prevalence towards older children was more marked in girls than boys (Figure 3A and B).

Figure 3. Prevalence of Schistosoma haematobium re-infection and intensity of re-infection by age and sex among children taking part in the re-infection cohort (n = 378).

Figure 3.

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Blue represents light re-infections (<50 eggs per 10 ml urine) and Red represents heavy re-infections (≥50 eggs per 10 ml urine). Panels A and B show the prevalence of Schistosoma haematobium for female and male children respectively.

Impact of community piped water coverage on intensity of re-infection

In the pooled adjusted negative binomial model (n = 378), a percentage point increase in community piped water coverage in the area surrounding a child’s residence was associated with 4.4% decline in mean re-infection intensity (Model 2 in Table 2; incidence rate ratio (IRR) = 0.96, 95% CI: 0.93–0.98, p=0.004). Lower piped water coverage areas were associated with significantly higher mean egg counts albeit with relatively low numbers of children living at low piped water coverages (Figure 4).

Table 2. Predictors of intensity of re-infection with Schistosoma haematobium (pooled analysis, n = 378).

Model 1 presents results from the univariable negative binomial model and Model 2 presents results from the final parsimonious multivariable negative binomial model. Homestead level piped water coverage was derived from a Gaussian kernel density estimation using data from a survey conducted in 2007.

Model 1: univariable
(n = 378)		 Model 2: multivariable
(n = 378)
Covariates IRR 95% P-value IRR 95% P-value
Female 0.17 0.06–0.54 0.003 0.14 0.06–0.32 <0.001
Community piped water coverage (continuous effect) 0.96 0.93–0.98 0.002 0.96 0.93–0.98 0.004
Age at baseline (years) 0.68 0.50–0.93 0.017 0.78 0.59–1.04 0.094
Altitude class (ref < 50)
50–100 3.65 0.91–14.5 0.067 1.20 0.31–4.56 0.793
100–150 0.72 0.21–2.54 0.612 0.41 0.1–1.74 0.226
≥150 0.11 0.02–0.62 0.012 0.05 0.01–0.32 0.001
Land cover class (ref. Sparse shrubland)
Closed shrubland 1.96 0.51–7.57 0.327 0.86 0.34–2.21 0.754
Open shrubland/grassland 1.77 0.33–9.49 0.508 1.41 0.48–4.16 0.533
Thickett 0.01 0.00–0.06 <0.001 0.02 0.00–0.20 0.001
Toilet in household (ref. no toilet) 2.71 0.70–10.4 0.148 0.77 0.24–2.46 0.662
Grade (ref. Grade 5) 0.24 0.08–0.75 0.014 1.35 0.52–3.48 0.540
Visit (ref. Follow up 1) 1.01 0.21–4.92 0.989 0.74 0.31–1.76 0.494
Distance to water body class
(ref. < 1 km)
1–2 km 0.11 0.03–0.34 <0.001
2–3 km 0.18 0.04–0.85 0.031
>3 km 0.08 0.01–0.54 0.010
Household wealth index
(ref. 1st quintile)
2 3.75 0.49–28.7 0.203
3 0.38 0.08–1.83 0.233
4 3.22 0.63–16.3 0.159
5 1.71 0.03–1.70 0.432
Square root of slope 0.77 0.45–1.32 0.340
Baseline intensity of infection (ref. Light infection) 2.59 0.81–8.30 0.110
Alpha (overdispersion parameter) 22.6 17.9–28.3 <0.001
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Figure 4. Margin plot of piped water coverage and re-infection intensity.

Figure 4.

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The margin plot was constructed from the final parsimonious multivariable negative binomial regression model for the pooled dataset (n = 378, incidence rate ratio = 0.96, p=0.004). Piped water coverage was estimated using the Gaussian kernel density methodology.

An analysis of each follow-up survey round separately (N = 253 and N = 125 for round 1 and round 2 respectively) revealed a consistent pattern in which community piped water was strongly protective against Schistosoma haematobium re-infection intensity. That is, every percentage increase in community piped water coverage was associated with ~3% and~8% decrease in intensity of re-infection at follow-up round 1 and 2 respectively (Model 2 of Supplementary file 1 and Model 2 of Supplementary file 2). In both follow-up rounds, boys were at a higher risk of intense re-infection than girls (Model 2 of Supplementary file 1, and Model 2 of Supplementary file 2).

Details of cohort retention are provided in Supplementary file 3 and Supplementary file 4. Dropout was higher among participants residing away from water bodies (>2 km) in follow-up round 1 and among girls in follow-up round 2. However, there was no clear pattern between dropout and piped water coverage.

Clusters of high re-infection intensity

In a pooled analysis (n = 378), the weighted Gaussian kernel density estimation revealed marked geographical heterogeneity in the geometric mean egg counts across the study area (Figure 5). In addition, we detected one significant cluster of higher than average geometric mean egg count (radius = 6.93 km, geometric mean egg count = 54.95, p=0.006) near the Mfolozi river in the southeastern part of the study area. This cluster partially overlapped with one of the existing clusters detected in our baseline analysis (Tanser et al., 2018).

Figure 5. Geospatial heterogeneity in Schistosoma haematobium geometric mean egg counts (intensity of re-infection) across the study area.

Figure 5.

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The map shows the geographical distribution of mean egg counts/10 mL estimated using the Gaussian kernel of 3 km radius for the pooled re-infection cohort datasets (n = 378). Superimposed on the map is the local cluster (radius = 6.93 km, geometric mean egg count = 54.95, p=0.006) detected using Kulldorff’s spatial scan statistic.

Discussion

Using data from one of Africa’s largest population-based cohorts, we previously demonstrated that high coverage of piped water in the seven years preceding a parasitological survey was strongly predictive of Schistosoma haematobium infection in a nested cohort of primary school children (Tanser et al., 2018). Here we report on the prospective follow up of infected members of this nested cohort for two successive rounds of testing and treatment. Our results demonstrate a large impact of community piped water coverage on intensity of re-infection. Every percentage increase in piped water coverage was associated with 4.4% decrease in re-infection intensity. Taking these findings together with our previously reported baseline analyses (Tanser et al., 2018), we conclude that the scaleup of piped water coverage in the local community surrounding a child’s residence is strongly protective against Schistosoma haematobium infection and re-infection after treatment.

Unsurprisingly, we previously (Tanser et al., 2018) noted a strong positive relationship between age and Schistosoma haematobium infection prevalence likely due to the increasing cumulative exposure to infested water (and hence infection with Schistosoma haematobium) with increasing age (Dawaki et al., 2016; Wami et al., 2014). In this study and consistent with previous work (Mbanefo et al., 2014; Roberts et al., 1993), we observed a higher risk of re-infection among the younger age groups where intensity of exposure to contaminated water is likely higher (Mbanefo et al., 2014; Chandiwana et al., 1991). Evidence has shown that naturally acquired immunity against Schistosoma haematobium infection reduces both intensity of infection and infection prevalence in older age groups in endemic areas (Mitchell et al., 2011). Whilst developing this protective immunity take time, treatment with praziquantel has been shown to enhance host protective immunity by exposing large quantities of the parasite antigens required to develop immunity (Roberts et al., 1993; Chisango et al., 2019; Fukushige et al., 2019). In this study, naturally acquired immunity may also have played a role in the observed decline in prevalence of re-infection with increasing age following treatment. However, the impact is likely to be minimal given the narrow age range that was examined.

Differences in gender roles resulting from cultural differences may differentially predispose girls (Gyuse et al., 2010) or boys (Chandiwana et al., 1991; Liao et al., 2011; Geleta et al., 2015; Senghor et al., 2015) to increased contact with infested water therefore increasing their risk of re-infection with Schistosoma haematobium after treatment (Mbanefo et al., 2014). These differences explain the heterogeneous findings on gender observed across different geographical settings (Mbanefo et al., 2014; Liao et al., 2011). In our study and consistent with studies conducted elsewhere (Chandiwana et al., 1991; Liao et al., 2011; Geleta et al., 2015; Senghor et al., 2015), girls were at a lower risk of intense re-infection compared to boys (Table 2).

We detected a significant local cluster of intense re-infection in the current analysis that partially overlaps with the cluster of Schistosoma haematobium infection observed in the baseline survey (Tanser et al., 2018), demonstrating that exposure to Schistosoma haematobium infested water in the study area is heterogeneous and that transmission is concentrated in certain key locations in keeping with evidence from this and other settings (Brooker, 2007; Simoonga et al., 2008; Manyangadze et al., 2016). Schistosoma haematobium clusters can potentially be targeted with available control interventions to interrupt transmission (Simoonga et al., 2008) and subsequently achieve elimination (Bergquist et al., 2017; Ross et al., 2017).

Our study had important strengths: firstly, we utilized a cohort of children who were treated for Schistosoma haematobium infection at baseline and had two consecutive rounds of followed-up to assess re-infection intensity. This design presents a strong basis to directly quantify the causal association between community piped water coverage and Schistosoma haematobium infection. Secondly, the study was nested within a large population-based cohort in rural KwaZulu-Natal province with detailed homestead level geospatial data linking each child to their residence within the demographic surveillance area. Thirdly, we had access to a comprehensive survey of household level piped water use and asset ownership conducted in 2007 that we utilized to derive an index of community piped water coverage and household wealth index. Finally, we utilized longitudinal databases of environmental predictors of disease infection, described in detail in our previous work (Tanser et al., 2018), to adjust for potential confounding in the regression models.

A limitation to our study is that we did not assess praziquantel treatment effect after drug administration. It may therefore be difficult to ascertain whether a positive diagnosis was due to re-infection or treatment failure. However, approximately 80% cure rates at 4 weeks after treatment with praziquantel has been reported in KwaZulu-Natal (Saathoff et al., 2004) and Côte d'Ivoire (N'Goran et al., 2001), suggesting that most positive diagnoses after treatment were likely re-infections. Furthermore, we observed a shift in burden of heavy infections from older age groups at baseline survey (pre-treatment) to younger age groups in the re-infection cohort analysis that cannot be accounted for by treatment failure, thus providing further evidence suggesting that the impact of treatment failure was minimal. Whilst we demonstrated a clear relationship between piped water coverage and re-infection intensity, the study was not powered to detect differences in the absolute rate of re-infection by piped water coverage category. Furthermore, we observed an increase in the proportion of dropouts with increasing distance from the water bodies (follow-up round 1, Supplementary file 3) and among girls (follow-up round 2, Supplementary file 4). An increase in dropout rates among individuals who are at a lower risk of infection (that is, residing away from water bodies or girls [Tanser et al., 2018]) may partially explain the high re-infection rates documented in follow-up round 1 and follow-up round 2. In our baseline analysis we showed that sex was a strong independent predictor of infection with Schistosoma haematobium and that the impact of community piped water coverage was greater among girls than among boys (Tanser et al., 2018). Therefore, a higher proportion of attrition among girls will potentially bias the association of piped water on re-infection intensity towards the null hypothesis, implying that our effect estimates may be marginally conservative.

Conclusion

The WHO recommends mass drug administration (MDA) with a single oral dose of 40 mg/kg of praziquantel for the global control and elimination of schistosomiasis. Although this strategy has clear short-term benefits of reduced morbidity, sustained benefits are uncertain given the current global MDA coverage of circa 20%, low drug compliance and efficacy, and rapid re-infection rates (Ross et al., 2017). Our study provides evidence that improved access to piped water in the community significantly reduces the intensity of re-infection among school going children. Therefore, Schistosoma haematobium control programs should consider scaleup of piped water coverage in the community as a preventive strategy against re-infection to supplement the MDA strategy. Secondly, targeting households within the high risk areas would mean the most vulnerable population is prioritized with the added benefits of reducing transmission to the entire community (Lai et al., 2015; Bergquist et al., 2017; Ross et al., 2017). We recommend targeting children living along the river Mfolozi and river Nyalazi (cluster locations) to achieve greater impact on reducing morbidity and transmission potential. Finally, we recommend further studies to examine the impact of an integrated approach (Knopp et al., 2019) that include MDA, piped water coverage and behavioral change education (potentially including installation of safe water recreational areas [Kosinski et al., 2012]) on re-infection.

Materials and methods

Ethical approval was provided by the Biomedical Research Ethics Committee of the university of KwaZulu-Natal (reference #E165/05). Written informed consent was sought from parents or guardians of the participating children for both rounds of follow-up in 2007 and 2008 and assent obtained from the children during the follow-up surveys.

Study site

We undertook our study in all 33 primary schools located within the catchment area of the Africa Health Research Institute (AHRI, previously called the Africa Centre Demographic Information System)(Tanser et al., 2008). The study area is located in the coastal lowland area of the northern KwaZulu-Natal province, South Africa (Figure 6A and B) and is one of the largest and comprehensive HIV population-based cohorts in sub-Saharan Africa. The surveillance area covers approximately 438 km2 with a population of ~90,000 people living in ~10,000 households. Tri-annual household surveys are conducted by trained field workers under the management of AHRI. Field workers interview a key household informant, who provides information on the births, deaths, migration events, and relationship characteristics of all household members. All households have been geolocated to an accuracy <2 m.

Figure 6. Location of the study area in South Africa.

Figure 6.

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Panel A displays the map of South Africa highlighting the major towns and the location of the study area. Panel B displays the map of the study area showing the major roads and the coverage of piped water (%) in 2007. Community piped water coverage was estimated using the Gaussian kernel methodology (Tanser et al., 2018).

Re-infection cohort design

The re-infection followup rounds were undertaken between September and December of 2007 (round 1), and between April and August of 2008 (round 2) respectively. The cohort included children who had microscopically confirmed Schistosoma haematobium infection at baseline, received treatment for baseline infection and provided informed consent to participate in any of the follow-up rounds. Participants with confirmed infection at baseline or during the follow-up rounds were treated immediately at school. Schistosoma haematobium infection status was determined using the urine reagent strips for evaluating microhematuria with confirmation using microscopy diagnosis. A single oral dose of 40 mg/kg body weight of praziquantel was administered to children with detectable microhematuria following recommended World Health Organization (WHO) guidelines on Schistosoma haematobium preventive chemotherapy (Saathoff et al., 2004; WHO Expert Committee on the Control of Schistosomiasis, 2002) under strict supervision of trained nurses. Children who had false negative urine reagent strip results (based on microscopy gold standard) were subsequently traced back in school, and became eligible for treatment and inclusion in the cohort analysis. However, because of the time lag between laboratory testing and treatment, some children with a false negative test results missed treatment for baseline infection if they were absent from school during tracing and were therefore not eligible for inclusion in the cohort analysis. In addition, some parents/guardians of children who were screened and treated for baseline infection did not give consent for their children to participate in the follow-up rounds (Figure 1).

Therefore, the re-infection cohort was defined as children who had microscopically confirmed Schistosoma haematobium infection at baseline, were treated for baseline infection and consented to participate in at least one follow-up round. Eligibility for the second round of follow-up was subject to treatment at round 1 (for those found to be infected during screening) and consent at round 2 regardless of the infection status at round 1, or children who were treated for baseline infection and consented for only round 2 of screening and treatment.

Sample collection and laboratory analysis

Sample collection, consenting and laboratory testing procedures were similar to those conducted during the baseline parasitological survey that we reported previously (Tanser et al., 2018). Urine samples were collected between 10:00 and 12:00 hr using 500 mL honey jars (one sample per child). Each sample testing for infection was done in two stages: 1) testing using urinalysis reagent strips (Bayer Uristix) in schools and 2) confirmatory microscopy analysis in the laboratory. The urine samples were aliquoted and processed in duplicates of 10 mL sub-samples and diluted using 2% methiolate in 5% Formalin. Filtration was done using the polycarbonate filters (diameter of 25 mm and 8.0 μm pore size). Upon sample filtration, each urine filter, potentially containing Schistosoma haematobium eggs, was placed on a glass slide, stained and examined under a microscope with x10 magnification. Eggs were counted by trained laboratory technicians and expressed per 10 mL of urine. The primary outcome of interest was the subject level egg count determined by averaging the duplicate egg counts for each participating child in each follow-up round. Here, infection or re-infection status refers to microscopically confirmed Schistosoma haematobium eggs in urine samples. We categorized egg counts into a priori groups of heavy (≥50 eggs/10 mL) and light (<50 eggs/10 mL) re-infections following WHO classification guidelines (WHO Expert Committee on the Control of Schistosomiasis, 2002) on intensity of re-infection with Schistosoma haematobium.

Data analysis

Descriptive statistics were used to describe demographic and environmental characteristics of Schistosoma haematobium re-infection. We used geometric means to summarize egg counts and estimated disease burden by computing prevalence with 95% confidence intervals (95% CI). Royston’s χ2-test-of-trend and the classical chi-square test for nominal data were used to assess the linear trend and nominal associations between variables respectively.

Re-infection rates

Given that the exact date of re-infection after treatment is not observed, we randomly imputed a re-infection date between the treatment date and the testing date assuming a uniform distribution (Vandormael et al., 2018) for all children who were re-infected with Schistosoma haematobium in both rounds of follow-up. We used the imputed dates of re-infection or testing date for non-infected children to compute time at risk that was used as a denominator when computing the 2007–2008 re-infection rates and computed the 95% CIs assuming the Poisson distribution.

Covariates for schistosomiasis re-infection

We have previously presented a detailed description of the procedure used to derive the exposure variable and potential confounders (Tanser et al., 2018). Water supply from the reservoir to the study area is mainly through gravitated PVC pipes. A household had access to safe water supplies if there was reliable piped water in the dwelling or if the key household informant reported that the household used water from the public tab, borehole, protected dug well, protected spring or rainwater from storage tanks, for the household chores. We derived the community piped water coverage for each residential homestead of the participating child from a weighted two-dimensional Gaussian kernel of 2 km radius using data from the 2007 homestead level survey on access to piped water (Figure 6B). The 2 km radius was selected based on our previous analysis in which a tradeoff between sensitivity to local variations and robustness to random noise was considered (Tanser et al., 2018). We used the derived community piped water coverage (exposure variable), and the well-established potential confounders (environmental and social economic covariables [Tanser et al., 2018; Lai et al., 2015; Brooker, 2007; Appleton, 1978; Simoonga et al., 2009; Clennon et al., 2004]) in both descriptive and inferential analysis. We also obtained the altitude, slope, distance to the nearest water body, landcover classification and household wealth index as we previously described (Tanser et al., 2018).

Regression modelling

Count models have previously been utilized to assess determinants of host intensity of re-infection and account for host heterogeneity inherent in the distribution of schistosomiasis egg counts (Chipeta et al., 2014). The negative binomial model was the best fit to our data among the plausible count models that we examined in a data driven model selection procedure (Tang et al., 2012). We used univariable analyses (retaining significant covariates at p<0.1) and backwards exclusion of non-significant covariates (p>0.05) to arrive at a final parsimonious model. The exposure variable (community piped water coverage), age, toilet and sex were included in multivariable models regardless of their significance in the univariable analyses. We used predictive margins to estimate egg counts at various predefined levels of the exposure variable from the final multivariable regression model. To account for correlation among children who contributed data to both the 1st and 2nd follow-up rounds (pooled analysis), we obtained robust standard errors in models adjusted for the fixed effects of follow-up round and further conducted subgroup analyses on each follow-up round separately.

We performed statistical analyses using Stata 14 (Stata Corp, College Station, TX, USA) and computed the spatial scan statistics (Kulldorff et al., 2009) using SaTScan version 9.4.2 software (Havard medical School, Boston, MA, USA). We used ArcGIS version 10.3 (ESRI, Redlands, CA, USA) (Esri and Here, 2004) for the standard Gaussian kernel density estimation and cartographic display.

Local cluster detection

In our baseline analysis (Tanser et al., 2018), we described geographical heterogeneity of Schistosoma haematobium infection prevalence and identified 4 clusters of significantly high relative risk. Here, we used the Gaussian kernel density estimations to describe the geographical heterogeneity of re-infection intensity and identified the presence of significant geographical clusters of intense re-infection using the scan statistic implemented in SaTScan software. Briefly, we examined for geographical areas experiencing significantly higher intensity of re-infection (mean log-egg count) than would be expected by chance using the normal probability model (Kulldorff et al., 2009). SaTScan software imposes a scanning window (predefined here to be circular and non-overlapping) that moves systematically across geographical space and with varying radius. For each geographical location and scanning window size, the geometric mean egg count is computed, and significance testing performed using the Monte-Carlo simulation.

Role of the funding source

The funders had no role in the study design, data collection, analysis, interpretation of results, manuscript writing or decision to submit for publication. The corresponding author had full access to the data and made the final decision to submit for publication after obtaining approval from the coauthors.

Acknowledgements

We thank the primary school children for their willingness to participate in this study. We thank the children’s parents and/or guardians, school principals and teachers for the assistance in conducting the study. The authors are indebted to the study staff at AHRI and School-Health team at Hlabisa Hospital for their invaluable assistance in conducting the parasitological survey. The authors wish to express their grateful thanks to Colleen Archer (University of KwaZulu-Natal) for conducting the microscopic analysis and Colin Newell for database support. The study was funded through the National Institute of Health via the International Collaboration in Infectious Disease Research (ICIDR). The Africa Health Research Institute is funded by the Wellcome Trust, UK.

Funding Statement

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Contributor Information

Polycarp Mogeni, Email: pkambona11@gmail.com.

Eduardo Franco, McGill University, Canada.

Eduardo Franco, McGill University, Canada.

Funding Information

This paper was supported by the following grants:

  • National Institutes of Health International Collaboration in Infectious Disease Research (ICIDR) to Christopher Appleton, Frank Tanser.

  • Wellcome Trust Africa Health Research Institute to Frank Tanser.

Additional information

Competing interests

No competing interests declared.

Author contributions

Conceptualization, Data curation, Formal analysis, Validation, Visualization, Writing - original draft, Writing - review and editing.

Conceptualization, Formal analysis, Writing - original draft, Writing - review and editing.

Formal analysis, Visualization, Writing - original draft, Writing - review and editing.

Conceptualization, Funding acquisition, Investigation, Methodology, Writing - original draft, Project administration, Writing - review and editing.

Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Methodology, Writing - original draft, Project administration, Writing - review and editing.

Ethics

Human subjects: Ethical approval was provided by the Biomedical Research Ethics Committee of the university of KwaZulu-Natal (reference #E165/05). Written informed consent was sought from parents or guardians of the participating children for both rounds of follow-up in 2007 and 2008 and assent obtained from the children during the follow-up surveys.

Additional files

Source data 1. Prevalence of re-infection, intensity of re-infection and re-infection rate (per 100-person year of follow-up) among individuals treated at baseline for S. haematobium infection.
elife-54012-data1.docx (12.7KB, docx)
Supplementary file 1. Predictors of Schistosoma haematobium re-infection using data from follow up round 1 only.

Model 1 presents results from a univariable negative binomial model and Model 2 presents results from a multivariable negative binomial model (N = 253).

elife-54012-supp1.docx (14.3KB, docx)
Supplementary file 2. Predictors of Schistosoma haematobium re-infection using data from follow up round 2 only.

Model 1 presents results from a univariable negative binomial and Model 2 presents results from a multivariable negative binomial model (N = 125).

elife-54012-supp2.docx (13.6KB, docx)
Supplementary file 3. Characteristics of participants who dropped out of the study at follow-up round 1.

Piped water coverage (exposure variable) was similar between participants who dropped out of the study and those that were enrolled and examined. Significantly higher dropouts were only observed among participants residing further from water bodies. Piped water coverage was derived from the Gaussian kernel density estimation of radius three kilometers.

elife-54012-supp3.docx (17.2KB, docx)
Supplementary file 4. Characteristics of participants who dropped out of the study at follow-up round 2.

Piped water coverage (exposure variable) was similar between participants who dropped out of the study and those that were enrolled and examined. Significantly higher dropouts were only observed among girls. Piped water coverage was derived from the Gaussian kernel density estimation of radius three kilometers.

elife-54012-supp4.docx (17.1KB, docx)
Transparent reporting form

Data availability

The datasets used for the analysis presented in this study are available from the Africa Health Research Institute (AHRI) data repository https://data.africacentre.ac.za/index.php/auth/login/?destination=. To access the licensed datasets, the applicant must agree to the terms and conditions of use by completing an Application for Access to a Licensed Dataset. This request will be reviewed by the AHRI Data Release Committee, who may decide to approve the request, to deny access to the data, or to request additional information from the applicant.

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Decision letter

Editor: Eduardo Franco1

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Thank you for submitting your article "Impact of community piped water coverage on re-infection with urogenital schistosomiasis in rural South Africa" for consideration by eLife. Your submission is a Research Advance; this format is for substantial developments that directly build upon a previous eLife paper. Specifically, the present submission is intended to expand on a paper by your colleagues entitled "Impact of the scale-up of piped water on urogenital schistosomiasis infection in rural South Africa", authored by Tanser et al.

With the above context in mind, your article has been reviewed by two peer reviewers, and the evaluation has been overseen by a Senior/Reviewing Editor. The reviewers have opted to remain anonymous.

As is customary in eLife, the reviewers have discussed their reviews with one another. What follows below is an edited compilation of the essential and ancillary points provided by reviewers in their critiques and in their interaction post-review. Although we expect that you will address these comments in your response letter we also need to see the corresponding revision in the text of the manuscript. Some of the reviewers' comments may seem to be simple queries or challenges that do not prompt revisions to the text. Please keep in mind, however, that readers may have the same perspective as the reviewers. Therefore, it is essential that you attempt to amend or expand the text to clarify the narrative accordingly.

Summary:

The manuscript entitled "Impact of community piped water coverage on re-infection with urogenital schistosomiasis in rural South Africa" presents a study with new results supporting the scale-up of piped water coverage against Schistosoma haematobium transmission. Nested in a broader project that was published last year in eLife, this longitudinal study shows that a one-percent increase in piped water coverage was associated with 4.4% decline in re-infection intensity. These are relevant public health results that could improve the control of an important though neglected tropical disease.

Essential revisions:

1) Although the authors referred whenever possible to their previous article, it should also be possible to read the current manuscript as a self-contained work. Please provide one or two sentences of background on Schistosoma haematobium as a clinical and epidemiological entity.

2) You demonstrate a 15.4% reinfection after one round of praziquantel mass drug administration, and 12.8% reinfection rate after the second round of MDA. While your main findings are focused on re-infection intensity, this is but one metric to focus on. Relatedly, re-infection rates are also extremely important. What is the significance of piped water contributing to a 4.4% decline in infection intensity? Infection intensity may be driven by many factors, including degree of exposure, questionable development of protective immunity, and time since the most recent praziquantel treatment. There is ample evidence (e.g. Taabo District, Cote d'Ivoire, among many other sites), where MDA will decrease environmental contamination of water sources and partially block the lifecycle of the parasite. Over only a few years, we expect to see a decline in infection rates plus after even 2 rounds of MDA (typically annually or biannually) most infections are <50 eggs per 10 cc urine (low intensity as per WHO guidelines). Please clarify why the infection rates are similar after two rounds of MDA, and why there is only a very modest drop in infection intensity given the piped water intervention plus MDA.

3) It is unclear how many observations were included in the results of the analysis presented in the different figures and tables. It is difficult to assess the internal validity of the results and how they were affected by attrition.

4) Introduction, last paragraph: the number of children included in the follow-up round should be presented in the Results section. Indeed only few details can be given in this section. As a result readers could be misled to think that 333 children took part in both rounds.

Results:

5) Information is lacking in certain figures; please expand the figure legends.

6) "In the pooled adjusted negative binomial model…" should the readers assume that 378 measurements were included in this analysis?

7) Subsection “Impact of community piped water coverage on intensity of re-infection”: Figure 4 and Figure 2B do not clearly give a measure of the association between mean egg counts and piped water coverage, the authors should consider to mention these results explicitly in the Results section.

8) Figure 5: Consider replacing the study area in a map of South-Africa.

Discussion:

9) Discuss attrition and how power may have been impaired by the size of your study sample. Could differential dropout have biased your results?

10) "… or whether the drug given was taken in all situations". Are you suggesting that the praziquantel treatment might not have been taken by all children? Was the treatment administration not supervised?

11) Discussion, last paragraph: a cohort is by essence longitudinal.

12) Discussion, last paragraph: please italicize [Schistosoma haematobium]

Materials and methods

13) Subsection “Re-infection cohort design”: it is unclear how children could have a false negative RDT for the baseline infection and yet been included. Please clarify.

14) Subsection “Sample collection and laboratory analysis”: The authors discuss using RDT testing with test strips. I presume they are referring to "urine reagent strips". RDT may be misinterpreted for the RDT point-of-care test for S. mansoni (the POC-CAA test). Please rephrase this to state that these are urine reagent strips, and that you are evaluating for hemoglobinuria.

15) Subsection “Sample collection and laboratory analysis”: More detail is required about urine processing. What size filters were used? How many samples were provided by each child? What time of day was urine collected? Traditionally this is between 10:00 and 14:00 to maximize egg collection due to circadian variability in excretion.

eLife. 2020 Mar 17;9:e54012. doi: 10.7554/eLife.54012.sa2

Author response

Essential revisions:

1) Although the authors referred whenever possible to their previous article, it should also be possible to read the current manuscript as a self-contained work. Please provide one or two sentences of background on Schistosoma haematobium as a clinical and epidemiological entity.

We have included a paragraph in the Introduction giving a summary of the epidemiology as shown below.

“About 243 million people are infected with schistosomiasis worldwide, of whom ~93% reside in sub-Saharan Africa where children carry the greatest burden of the disease. […] However, Schistosoma haematobiumhas the widest geographical coverage in SSA and is the main cause of infection inthe Hlabisa sub-district, where our study is based[1].”

2) You demonstrate a 15.4% reinfection after one round of praziquantel mass drug administration, and 12.8% reinfection rate after the second round of MDA. While your main findings are focused on re-infection intensity, this is but one metric to focus on. Relatedly, re-infection rates are also extremely important. What is the significance of piped water contributing to a 4.4% decline in infection intensity? Infection intensity may be driven by many factors, including degree of exposure, questionable development of protective immunity, and time since the most recent praziquantel treatment.

We agree that re-infection rates are another important outcome metric. However, re-infection intensity provides a more precise and nuanced outcome measure, which is therefore often used to quantify the effect of interventions in schistosomiasis research [2-7] and to map transmission intensity [8, 9].

Our cohort study, by design, only included individuals who were at a high risk of re-infection (i.e. only participants who had confirmed Schistosoma haematobium infection at baseline) and the use of a less precise binary measure like re-infection, whilst potentially useful would require a larger sample size to be able to detect significant effects. We do recognize that re-infection intensity is a function of a set of features as pointed out by the reviewers. In this respect we adjusted for exposure time since recent treatment and age (a proxy for acquisition of immunity) in the regression models. Time since recent treatment, was not a significant predictor in the model partly because the follow-up timepoints were predefined. Although immunity is important, it may not play a major role in this study given the limited age range observed. Therefore, we predict that the intensity of infection observed was due to the varying degree of exposure and that high piped water coverage significantly decreases exposure resulting to lower intensity of infection.

There is ample evidence (e.g. Taabo District, Cote d'Ivoire, among many other sites), where MDA will decrease environmental contamination of water sources and partially block the lifecycle of the parasite. Over only a few years, we expect to see a decline in infection rates plus after even 2 rounds of MDA (typically annually or biannually) most infections are <50 eggs per 10 cc urine (low intensity as per WHO guidelines). Please clarify why the infection rates are similar after two rounds of MDA, and why there is only a very modest drop in infection intensity given the piped water intervention plus MDA.

The reviewer raises an important point here. Unlike most MDA studies (which target all children regardless of their infection status and include all children treated at baseline in the denominator when computing re-infection rates), our re-infection cohort participants were children who were infected and were treated for baseline infection (the denominator only includes children who were infected, treated at baseline and examined at any given follow-up round). Therefore, our cohort participants were children who were at a higher risk of re-infection given their baseline status. The prevalence of re-infection at round 1 and 2 would have been much lower if the entire cohort were to be examined and included in the denominator (as done in MDA) given their predicted low exposure to infection. We also note that, our study only tested and treated grade 5 and 6 children. Thus, there were still many untreated and infected children of other ages capable of sustaining the transmission cycle in these communities. This ultimately results in a high potential for re-infection in our cohort of previously infected children.

We include the following statement in the Discussion section:

“Finally, we recommend further studies to examine the impact of an integrated approach [10] that include MDA, piped water coverage and behavioral change education (potentially including installation of safe water recreational areas [11]) on re-infection.”

3) It is unclear how many observations were included in the results of the analysis presented in the different figures and tables. It is difficult to assess the internal validity of the results and how they were affected by attrition.

We have included the number of observations in both the figures and table legends where appropriate and included them in tables as well. For instance:

“Figure 2: Histograms of Schistosoma haematobium egg counts and community piped water coverage for the re-infection cohort participants (n=378). […] Community piped water coverage in the community surrounding each child was derived from the population-based 2007 piped water use survey conducted among all households in the study area.”

“Figure 4: Impact of piped water coverage in the community surrounding each child’s residence on Schistosomahaematobium re-infection intensity. The margin plot was constructed from the final parsimonious multivariable negative binomial regression model for the pooled dataset (n=378, incidence rate ratio = 0.96, P=0.002). Piped water coverage was estimated using the Gaussian kernel density methodology.”

“Table 2: Predictors of intensity of re-infection with Schistosomahaematobium (pooled analysis, n=378). […] Homestead level piped water coverage was derived from a Gaussian kernel density estimation using data from a survey conducted in 2007.”

4) Introduction, last paragraph: the number of children included in the follow-up round should be presented in the Results section. Indeed only few details can be given in this section. As a result readers could be misled to think that 333 children took part in both rounds.

We agree with the reviewers’ comments and delete the number of children from this section. We provide a detailed breakdown of the number of children participating in each round on the flowchart presented as Figure 1. See Figure 1 legend.

Results:

5) Information is lacking in certain figures; please expand the figure legends.

We have expanded the figure legends to clearly show the sample size and other missing information.

“Figure 2: Histograms of Schistosomahaematobium egg counts and community piped water coverage for the re-infection cohort participants (n=378). […] Community piped water was derived from 2007 piped water use survey conducted in the study area.”

“Figure 4: Impact of piped water coverage in the community surrounding the child’s residence on Schistosoma haematobium re-infection intensity. The margin plot was constructed from the final parsimonious multivariable negative binomial regression model for the pooled dataset (n=378, incidence rate ratio = 0.96, P=0.002). Piped water coverage was estimated using the Gaussian kernel density methodology.”

6) "In the pooled adjusted negative binomial model…" should the readers assume that 378 measurements were included in this analysis?

We have added the sample size as shown below: see the first paragraph of the subsection “Impact of community piped water coverage on intensity of re-infection”.

7) Subsection “Impact of community piped water coverage on intensity of re-infection”: Figure 4 and Figure 2B do not clearly give a measure of the association between mean egg counts and piped water coverage, the authors should consider to mention these results explicitly in the Results section.

We have expanded the legend for Figure 4 to clearly show the association as derived from the final negative binomial model (n=378). The graph shows the predicted margins from the final multivariable model. We include the information in the figure legend.

“Figure 4: Impact of piped water coverage in the community surrounding the child’s residence on Schistosoma haematobium re-infection intensity. […] Piped water coverage was estimated using the Gaussian kernel density methodology.”

We have also deleted reference to Figure 2B from the following statement:

“Lower piped water coverage areas were associated with significantly higher mean egg counts albeit with relatively low numbers of children living at low piped water coverages (Figure 4)”.

8) Figure 5: Consider replacing the study area in a map of South-Africa.

In the Materials and methods section, we have included the map of South Africa (Figure 6) showing the study area. We have also included the study area showing the major roads and piped water coverage for the year 2007.

Discussion:

9) Discuss attrition and how power may have been impaired by the size of your study sample. Could differential dropout have biased your results?

Attrition will inevitably lead to reduced power to detect meaningful associations. We however demonstrated a significant association between piped water coverage and re-infection intensity. We have included Supplementary files 3 and 4, and a paragraph discussing the potential bias attributable to attrition in our analysis and argued that the potential bias is likely towards the null and thus our effect sizes are likely marginally conservative.

We include the following paragraph in the Results section:

“Details of cohort retention are provided in Supplementary file 3 and Supplementary file 4. […] However, there was no clear pattern between dropout and piped water coverage.”

We also discuss the limitation in the Discussion section:

“Whilst we demonstrated a clear relationship between piped water coverage and re-infection intensity, we were not powered to detect differences in the rate of re-infection by piped water coverage category. […] Therefore, a higher proportion of attrition among girls will potentially bias the association of piped water on re-infection intensity towards the null hypothesis and implying our effect estimates are conservative.”

10) "… or whether the drug given was taken in all situations". Are you suggesting that the praziquantel treatment might not have been taken by all children? Was the treatment administration not supervised?

We agree that the statement is unclear. We confirm that treatment administration was supervised and delete the statement from the Discussion section.

We also spell this out in the Materials and methods section:

“A single oral dose of 40 mg/kg body weight of praziquantel was administered to children with detectable microhematuria following recommended World Health Organization (WHO) guidelines on Schistosoma haematobium preventive chemotherapy [1, 12] under strict supervision of trained nurses.”

11) Discussion, last paragraph: a cohort is by essence longitudinal.

We have deleted the word “longitudinal”

12) Discussion, last paragraph: please italicize [Schistosomahaematobium]

We have italicized “Schistosoma haematobium”

Materials and methods

13) Subsection “Re-infection cohort design”: it is unclear how children could have a false negative RDT for the baseline infection and yet been included. Please clarify.

Thank you for this question. Urine reagent strips (changed from RDT given the highlighted potential confusion) were used in schools to determine children who were infected and therefore eligible for treatment immediately in school. In addition, we conducted a confirmatory microscopy analysis in our laboratories. Children who had false negative results by urine reagent strips at baseline were traced back in school and treated and were therefore eligible for inclusion in the cohort analysis. However, false negative children that were not treated at baseline (absent in school during tracing) were not eligible for inclusion in the cohort analysis.

We have included the following statements:

“Children who had false negative urine reagent strip results (based on microscopy gold standard) were subsequently traced back in school, treated and included in the cohort analysis. However, because of the time lag between laboratory testing and treatment, some children with a false negative test results missed treatment for baseline infection if they were absent from school during tracing and were therefore not eligible for inclusion in the cohort analysis.”

14) Subsection “Sample collection and laboratory analysis”: The authors discuss using RDT testing with test strips. I presume they are referring to "urine reagent strips". RDT may be misinterpreted for the RDT point-of-care test for S. mansoni (the POC-CAA test). Please rephrase this to state that these are urine reagent strips, and that you are evaluating for hemoglobinuria.

We have changed this, see the Materials and methods section:

“Schistosomahaematobium infection status was determined using the urine reagent strips for evaluating microhematuria with confirmation using microscopy diagnosis.”

15) Subsection “Sample collection and laboratory analysis”: More detail is required about urine processing. What size filters were used? How many samples were provided by each child? What time of day was urine collected? Traditionally this is between 10:00 and 14:00 to maximize egg collection due to circadian variability in excretion.

We have included this information in the Materials and methods section:

“Urine samples were collected between 10:00 and 12:00 hours using 50 mL conical tubes (one sample per child). Each sample testing for infection was done in two stages: 1) testing using urinalysis reagent strips (Bayer Uristix) in schools and 2) confirmatory microscopy analysis in the laboratory. […] Eggs were counted by trained laboratory technicians and expressed per 10 mL of urine.

References

1) Saathoff, E., et al., Patterns of Schistosoma haematobium infection, impact of praziquantel treatment and re-infection after treatment in a cohort of schoolchildren from rural KwaZulu-Natal/South Africa. BMC Infectious Diseases, 2004. 4(1): p. 40.

2) Anderson, R.M., et al., What is required in terms of mass drug administration to interrupt the transmission of schistosome parasites in regions of endemic infection? Parasites and vectors, 2015. 8: p. 553-553.

3) Bah, Y.M., et al., Schistosomiasis in School Age Children in Sierra Leone After 6 Years of Mass Drug Administration With Praziquantel. Frontiers in public health, 2019. 7: p. 1-1.

4) Chandiwana, S.K., M.E. Woolhouse, and M. Bradley, Factors affecting the intensity of reinfection with Schistosoma haematobium following treatment with praziquantel. Parasitology, 1991. 102 Pt 1: p. 73-83.

5) Knopp, S., et al., A 5-Year intervention study on elimination of urogenital schistosomiasis in Zanzibar: Parasitological results of annual cross-sectional surveys. PLoS neglected tropical diseases, 2019. 13(5): p. e0007268-e0007268.

6) Phillips, A.E., et al., Assessing the benefits of five years of different approaches to treatment of urogenital schistosomiasis: A SCORE project in Northern Mozambique. PLoS neglected tropical diseases, 2017. 11(12): p. e0006061-e0006061.

7) Toor, J., et al., The design of schistosomiasis monitoring and evaluation programmes: The importance of collecting adult data to inform treatment strategies for Schistosoma mansoni. PLoS neglected tropical diseases, 2018. 12(10): p. e0006717-e0006717.

8) Vounatsou, P., et al., Bayesian geostatistical modelling for mapping schistosomiasis transmission. Parasitology, 2009. 136(13): p. 1695-1705.

9) Chadeka, E.A., et al., A high-intensity cluster of Schistosoma mansoni infection around Mbita causeway, western Kenya: a confirmatory cross-sectional survey. Tropical medicine and health, 2019. 47: p. 26-26.

10) Knopp, S., et al., Evaluation of integrated interventions layered on mass drug administration for urogenital schistosomiasis elimination: a cluster-randomised trial. The Lancet. Global health, 2019. 7(8): p. e1118-e1129.

11) Kosinski, K.C., et al., Effective control of Schistosoma haematobium infection in a Ghanaian community following installation of a water recreation area. PLoS neglected tropical diseases, 2012. 6(7): p. e1709-e1709.

12) WHO, E., Committee, Prevention and control of schistosomiasis and soil-transmitted helminthiasis. World Health Organ Tech Rep Ser, 2002. 912: p. i-vi, 1-57, back cover.

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    Source data 1. Prevalence of re-infection, intensity of re-infection and re-infection rate (per 100-person year of follow-up) among individuals treated at baseline for S. haematobium infection.
    elife-54012-data1.docx (12.7KB, docx)
    Supplementary file 1. Predictors of Schistosoma haematobium re-infection using data from follow up round 1 only.

    Model 1 presents results from a univariable negative binomial model and Model 2 presents results from a multivariable negative binomial model (N = 253).

    elife-54012-supp1.docx (14.3KB, docx)
    Supplementary file 2. Predictors of Schistosoma haematobium re-infection using data from follow up round 2 only.

    Model 1 presents results from a univariable negative binomial and Model 2 presents results from a multivariable negative binomial model (N = 125).

    elife-54012-supp2.docx (13.6KB, docx)
    Supplementary file 3. Characteristics of participants who dropped out of the study at follow-up round 1.

    Piped water coverage (exposure variable) was similar between participants who dropped out of the study and those that were enrolled and examined. Significantly higher dropouts were only observed among participants residing further from water bodies. Piped water coverage was derived from the Gaussian kernel density estimation of radius three kilometers.

    elife-54012-supp3.docx (17.2KB, docx)
    Supplementary file 4. Characteristics of participants who dropped out of the study at follow-up round 2.

    Piped water coverage (exposure variable) was similar between participants who dropped out of the study and those that were enrolled and examined. Significantly higher dropouts were only observed among girls. Piped water coverage was derived from the Gaussian kernel density estimation of radius three kilometers.

    elife-54012-supp4.docx (17.1KB, docx)
    Transparent reporting form
    elife-54012-transrepform.docx (70.4KB, docx)

    Data Availability Statement

    The datasets used for the analysis presented in this study are available from the Africa Health Research Institute (AHRI) data repository https://data.africacentre.ac.za/index.php/auth/login/?destination=. To access the licensed datasets, the applicant must agree to the terms and conditions of use by completing an Application for Access to a Licensed Dataset. This request will be reviewed by the AHRI Data Release Committee, who may decide to approve the request, to deny access to the data, or to request additional information from the applicant.

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