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

Download asset Open asset

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

Download asset Open asset

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

Download asset Open asset

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

Download asset Open asset

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

Download asset Open asset

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

Download asset Open asset

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.

1. 1. Appleton C

   (1978)

   Review of literature on abiotic factors influencing the distribution and life cycles of bilharziasis intermediate host snails

   Malacology Review 11:1–25.

   • Google Scholar
2. 1. Bergquist R
   2. Zhou XN
   3. Rollinson D
   4. Reinhard-Rupp J
   5. Klohe K

   (2017) Elimination of schistosomiasis: the tools required

   Infectious Diseases of Poverty 6:158.

   https://doi.org/10.1186/s40249-017-0370-7

   • PubMed
   • Google Scholar
3. 1. Brooker S

   (2007) Spatial epidemiology of human schistosomiasis in africa: risk models, transmission dynamics and control

   Transactions of the Royal Society of Tropical Medicine and Hygiene 101:1–8.

   https://doi.org/10.1016/j.trstmh.2006.08.004

   • PubMed
   • Google Scholar
4. 1. Chandiwana SK
   2. Woolhouse ME
   3. Bradley M

   (1991) Factors affecting the intensity of reinfection with Schistosoma haematobium following treatment with praziquantel

   Parasitology 102 Pt 1:73–83.

   https://doi.org/10.1017/S0031182000060364

   • PubMed
   • Google Scholar
5. 1. Chipeta MG
   2. Ngwira BM
   3. Simoonga C
   4. Kazembe LN

   (2014) Zero adjusted models with applications to analysing helminths count data

   BMC Research Notes 7:856.

   https://doi.org/10.1186/1756-0500-7-856

   • PubMed
   • Google Scholar
6. 1. Chisango TJ
   2. Ndlovu B
   3. Vengesai A
   4. Nhidza AF
   5. Sibanda EP
   6. Zhou D
   7. Mutapi F
   8. Mduluza T

   (2019) Benefits of annual chemotherapeutic control of schistosomiasis on the development of protective immunity

   BMC Infectious Diseases 19:219.

   https://doi.org/10.1186/s12879-019-3811-z

   • PubMed
   • Google Scholar
7. 1. Clennon JA
   2. King CH
   3. Muchiri EM
   4. Kariuki HC
   5. Ouma JH
   6. Mungai P
   7. Kitron U

   (2004) Spatial patterns of urinary schistosomiasis infection in a highly endemic area of coastal Kenya

   The American Journal of Tropical Medicine and Hygiene 70:443–448.

   https://doi.org/10.4269/ajtmh.2004.70.443

   • PubMed
   • Google Scholar
8. 1. Colley DG
   2. Bustinduy AL
   3. Secor WE
   4. King CH

   (2014) Human schistosomiasis

   The Lancet 383:2253–2264.

   https://doi.org/10.1016/S0140-6736(13)61949-2

   • Google Scholar
9. 1. Dawaki S
   2. Al-mekhlafi HM
   3. Ithoi I
   4. Ibrahim J
   5. Abdulsalam AM
   6. Ahmed A
   7. Sady H
   8. Atroosh WM
   9. Al-areeqi MA
   10. Elyana FN
   11. Nasr NA
   12. Surin J

   (2016) Prevalence and risk factors of schistosomiasis among hausa communities in Kano state, Nigeria

   Revista Do Instituto De Medicina Tropical De São Paulo 58:54.

   https://doi.org/10.1590/S1678-9946201658054

   • Google Scholar
10. Report

    1. Esri D
    2. Here T

    (2004)

    Intermap, Increment P Corp

    GEBCO, USGS, FAO, NPS, NRCAN, GeoBase, IGN, Kadaster NL, Ordnance Survey, Esri Japan, METI, Esri China (Hong Kong), Swisstopo, MapmyIndia, and the GIS User Community.

    • Google Scholar
11. 1. Fukushige M
    2. Mutapi F
    3. Woolhouse MEJ

    (2019) Population level changes in schistosome-specific antibody levels following chemotherapy

    Parasite Immunology 41:e12604.

    https://doi.org/10.1111/pim.12604

    • PubMed
    • Google Scholar
12. 1. GBD 2017 Disease and Injury Incidence and Prevalence Collaborators

    (2018) Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the global burden of disease study 2017

    The Lancet 392:1789–1858.

    https://doi.org/10.1016/S0140-6736(18)32279-7

    • PubMed
    • Google Scholar
13. 1. Geleta S
    2. Alemu A
    3. Getie S
    4. Mekonnen Z
    5. Erko B

    (2015) Prevalence of urinary schistosomiasis and associated risk factors among abobo primary school children in Gambella regional state, southwestern Ethiopia: a cross sectional study

    Parasites & Vectors 8:215.

    https://doi.org/10.1186/s13071-015-0822-5

    • PubMed
    • Google Scholar
14. 1. Grimes JE
    2. Croll D
    3. Harrison WE
    4. Utzinger J
    5. Freeman MC
    6. Templeton MR

    (2014) The relationship between water, sanitation and schistosomiasis: a systematic review and meta-analysis

    PLOS Neglected Tropical Diseases 8:e3296.

    https://doi.org/10.1371/journal.pntd.0003296

    • PubMed
    • Google Scholar
15. 1. Grimes JE
    2. Croll D
    3. Harrison WE
    4. Utzinger J
    5. Freeman MC
    6. Templeton MR

    (2015) The roles of water, sanitation and hygiene in reducing schistosomiasis: a review

    Parasites & Vectors 8:156.

    https://doi.org/10.1186/s13071-015-0766-9

    • PubMed
    • Google Scholar
16. 1. Gyuse KI
    2. Ofoezie IE
    3. Ogunniyi TAB

    (2010)

    The effect of urinary schistosomiasis on the health of children in selected rural communities of osun state, Nigeria

    Journal of Tropical Medicine and Parasitology 33:7–16.

    • Google Scholar
17. 1. Knopp S
    2. Person B
    3. Ame SM
    4. Ali SM
    5. Hattendorf J
    6. Juma S
    7. Muhsin J
    8. Khamis IS
    9. Mohammed KA
    10. Utzinger J
    11. Hollenberg E
    12. Kabole F
    13. Rollinson D

    (2019) Evaluation of integrated interventions layered on mass drug administration for urogenital schistosomiasis elimination: a cluster-randomised trial

    The Lancet Global Health 7:e1118–e1129.

    https://doi.org/10.1016/S2214-109X(19)30189-5

    • PubMed
    • Google Scholar
18. 1. Kosinski KC
    2. Adjei MN
    3. Bosompem KM
    4. Crocker JJ
    5. Durant JL
    6. Osabutey D
    7. Plummer JD
    8. Stadecker MJ
    9. Wagner AD
    10. Woodin M
    11. Gute DM

    (2012) Effective control of Schistosoma haematobium infection in a ghanaian community following installation of a water recreation area

    PLOS Neglected Tropical Diseases 6:e1709.

    https://doi.org/10.1371/journal.pntd.0001709

    • PubMed
    • Google Scholar
19. 1. Kulldorff M
    2. Huang L
    3. Konty K

    (2009) A scan statistic for continuous data based on the normal probability model

    International Journal of Health Geographics 8:58.

    https://doi.org/10.1186/1476-072X-8-58

    • PubMed
    • Google Scholar
20. 1. Lai YS
    2. Biedermann P
    3. Ekpo UF
    4. Garba A
    5. Mathieu E
    6. Midzi N
    7. Mwinzi P
    8. N'Goran EK
    9. Raso G
    10. Assaré RK
    11. Sacko M
    12. Schur N
    13. Talla I
    14. Tchuenté LA
    15. Touré S
    16. Winkler MS
    17. Utzinger J
    18. Vounatsou P

    (2015) Spatial distribution of schistosomiasis and treatment needs in sub-Saharan africa: a systematic review and geostatistical analysis

    The Lancet Infectious Diseases 15:927–940.

    https://doi.org/10.1016/S1473-3099(15)00066-3

    • PubMed
    • Google Scholar
21. 1. Liao CW
    2. Sukati H
    3. Nara T
    4. Tsubouchi A
    5. Chou CM
    6. Jian JY
    7. Huang YC
    8. Chang PW
    9. Chiu WT
    10. Huang YH
    11. Fan CK

    (2011)

    Prevalence of Schistosoma haematobium infection among schoolchildren in remote Areas devoid of sanitation in Northwestern Swaziland, southern africa

    Japanese Journal of Infectious Diseases 64:322–326.

    • PubMed
    • Google Scholar
22. 1. Manyangadze T
    2. Chimbari MJ
    3. Gebreslasie M
    4. Mukaratirwa S

    (2016) Risk factors and micro-geographical heterogeneity of Schistosoma haematobium in Ndumo Area, uMkhanyakude district, KwaZulu-Natal, South Africa

    Acta Tropica 159:176–184.

    https://doi.org/10.1016/j.actatropica.2016.03.028

    • PubMed
    • Google Scholar
23. 1. Mbanefo EC
    2. Huy NT
    3. Wadagni AA
    4. Eneanya CI
    5. Nwaorgu O
    6. Hirayama K

    (2014) Host determinants of reinfection with schistosomes in humans: a systematic review and meta-analysis

    PLOS Neglected Tropical Diseases 8:e3164.

    https://doi.org/10.1371/journal.pntd.0003164

    • PubMed
    • Google Scholar
24. 1. Mitchell KM
    2. Mutapi F
    3. Savill NJ
    4. Woolhouse ME

    (2011) Explaining observed infection and antibody age-profiles in populations with urogenital schistosomiasis

    PLOS Computational Biology 7:e1002237.

    https://doi.org/10.1371/journal.pcbi.1002237

    • PubMed
    • Google Scholar
25. 1. N'Goran EK
    2. Utzinger J
    3. N'Guessan AN
    4. Müller I
    5. Zamblé K
    6. Lohourignon KL
    7. Traoré M
    8. Sosthène BA
    9. Lengeler C
    10. Tanner M

    (2001) Reinfection with Schistosoma haematobium following school-based chemotherapy with praziquantel in four highly endemic villages in côte d'ivoire

    Tropical Medicine and International Health 6:817–825.

    https://doi.org/10.1046/j.1365-3156.2001.00785.x

    • PubMed
    • Google Scholar
26. 1. Roberts M
    2. Butterworth AE
    3. Kimani G
    4. Kamau T
    5. Fulford AJ
    6. Dunne DW
    7. Ouma JH
    8. Sturrock RF

    (1993) Immunity after treatment of human schistosomiasis: association between cellular responses and resistance to reinfection

    Infection and Immunity 61:4984–4993.

    https://doi.org/10.1128/IAI.61.12.4984-4993.1993

    • PubMed
    • Google Scholar
27. 1. Ross AG
    2. Chau TN
    3. Inobaya MT
    4. Olveda RM
    5. Li Y
    6. Harn DA

    (2017) A new global strategy for the elimination of schistosomiasis

    International Journal of Infectious Diseases 54:130–137.

    https://doi.org/10.1016/j.ijid.2016.09.023

    • PubMed
    • Google Scholar
28. 1. Saathoff E
    2. Olsen A
    3. Magnussen P
    4. Kvalsvig JD
    5. Becker W
    6. Appleton CC

    (2004) 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 4:40.

    https://doi.org/10.1186/1471-2334-4-40

    • PubMed
    • Google Scholar
29. 1. Senghor B
    2. Diaw OT
    3. Doucoure S
    4. Sylla SN
    5. Seye M
    6. Talla I
    7. Bâ CT
    8. Diallo A
    9. Sokhna C

    (2015) Efficacy of praziquantel against urinary schistosomiasis and reinfection in senegalese school children where there is a single well-defined transmission period

    Parasites & Vectors 8:362.

    https://doi.org/10.1186/s13071-015-0980-5

    • PubMed
    • Google Scholar
30. 1. Simoonga C
    2. Kazembe LN
    3. Kristensen TK
    4. Olsen A
    5. Appleton CC
    6. Mubita P
    7. Mubila L

    (2008) The epidemiology and small-scale spatial heterogeneity of urinary schistosomiasis in lusaka province, Zambia

    Geospatial Health 3:57–67.

    https://doi.org/10.4081/gh.2008.232

    • PubMed
    • Google Scholar
31. 1. Simoonga C
    2. Utzinger J
    3. Brooker S
    4. Vounatsou P
    5. Appleton CC
    6. Stensgaard AS
    7. Olsen A
    8. Kristensen TK

    (2009) Remote sensing, geographical information system and spatial analysis for schistosomiasis epidemiology and ecology in Africa

    Parasitology 136:1683–1693.

    https://doi.org/10.1017/S0031182009006222

    • PubMed
    • Google Scholar
32. Book

    1. Tang W
    2. He H
    3. Tu X

    (2012) Applied Categorical and Count Data Analysis

    Chapman and Hall/CRC.

    https://doi.org/10.1111/insr.12051_14

    • Google Scholar
33. 1. Tanser F
    2. Hosegood V
    3. Bärnighausen T
    4. Herbst K
    5. Nyirenda M
    6. Muhwava W
    7. Newell C
    8. Viljoen J
    9. Mutevedzi T
    10. Newell ML

    (2008) Cohort profile: africa centre demographic information system (ACDIS) and population-based HIV survey

    International Journal of Epidemiology 37:956–962.

    https://doi.org/10.1093/ije/dym211

    • PubMed
    • Google Scholar
34. 1. Tanser F
    2. Azongo DK
    3. Vandormael A
    4. Bärnighausen T
    5. Appleton C

    (2018) Impact of the scale-up of piped water on urogenital schistosomiasis infection in rural south Africa

    eLife 7:e33065.

    https://doi.org/10.7554/eLife.33065

    • PubMed
    • Google Scholar
35. 1. Vandormael A
    2. Dobra A
    3. Bärnighausen T
    4. de Oliveira T
    5. Tanser F

    (2018) Incidence rate estimation, periodic testing and the limitations of the mid-point imputation approach

    International Journal of Epidemiology 47:236–245.

    https://doi.org/10.1093/ije/dyx134

    • PubMed
    • Google Scholar
36. 1. Wami WM
    2. Nausch N
    3. Bauer K
    4. Midzi N
    5. Gwisai R
    6. Simmonds P
    7. Mduluza T
    8. Woolhouse M
    9. Mutapi F

    (2014) Comparing parasitological vs serological determination of Schistosoma haematobium infection prevalence in preschool and primary school-aged children: implications for control programmes

    Parasitology 141:1962–1970.

    https://doi.org/10.1017/S0031182014000213

    • PubMed
    • Google Scholar
37. Report

    1. WHO Expert Committee on the Control of Schistosomiasis

    (2002) Prevention and Control of Schistosomiasis and Soil-Transmitted Helminthiasis

    World Health Organization Technical Report Series.

    https://apps.who.int/iris/handle/10665/42588

    • Google Scholar

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

• 1,087

  views

• 106

  downloads

• 9

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.