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.

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

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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.

Figure 2

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Table 1

	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

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

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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).

Figure 4

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Table 2

	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

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

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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.

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

Request a detailed protocol

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

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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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Author details

1. Polycarp Mogeni

   1. Africa Health Research Institute, KwaZulu-Natal, South Africa
   2. School of Nursing and Public Health, University of KwaZulu-Natal, KwaZulu-Natal, South Africa
   3. KwaZulu-Natal Innovation and Sequencing Platform (KRISP), University of KwaZulu-Natal, KwaZulu-Natal, South Africa

   Contribution

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

   For correspondence

   pkambona11@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1926-7576

2. Alain Vandormael

   1. Africa Health Research Institute, KwaZulu-Natal, South Africa
   2. School of Nursing and Public Health, University of KwaZulu-Natal, KwaZulu-Natal, South Africa
   3. KwaZulu-Natal Innovation and Sequencing Platform (KRISP), University of KwaZulu-Natal, KwaZulu-Natal, South Africa
   4. Heidelberg Institute of Global Health, Faculty of Medicine, University of Heidelberg, Heidelberg, Germany

   Contribution

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

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5742-0511

3. Diego Cuadros

   1. Department of Geography and Geographic Information Science, University of Cincinnati, Cincinnati, United States
   2. Health Geography and Disease Modeling Laboratory, University of Cincinnati, Cincinnati, United States

   Contribution

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

   Competing interests

   No competing interests declared

4. Christopher Appleton

   School of Life Sciences, University of KwaZulu-Natal, KwaZulu-Natal, South Africa

   Contribution

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

   Competing interests

   No competing interests declared

5. Frank Tanser

   1. Africa Health Research Institute, KwaZulu-Natal, South Africa
   2. School of Nursing and Public Health, University of KwaZulu-Natal, KwaZulu-Natal, South Africa
   3. Lincoln International Institute for Rural Health, University of Lincoln, Lincoln, United Kingdom

   Contribution

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

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9797-0000

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