Schistosomiasis is a chronic parasitic disease caused by blood flukes of the genus Schistosoma and transmitted by snails found in fresh water bodies that have been contaminated by Schistosoma eggs. Approximately, 240 million people worldwide (WHO, 2010; Stothard et al., 2009) are infected with schistosomiasis, with a disease burden of 3.3 million disability-adjusted life-years (Murray et al., 2012). In sub-Saharan Africa alone, an estimated 163 million people were infected in 2012 (Lai et al., 2015). Following the London declaration (Uniting to Combat Neglected Tropical Diseases, 2012), the World Health Organization (WHO) has set a goal of increasing mass drug administration coverage to 75% of at-risk children in endemic countries (WHO, 2013). Despite progress in many so-called ‘neglected tropical diseases’, the fourth progress report of the London Declaration records that schistosomiasis has the lowest coverage of all helminth diseases treatable by mass drug administration (Uniting to Combat Neglected Tropical Diseases, 2016).

For achieving sustained control, elimination, or eradication of schistosomiasis, improvements of water, sanitation, and hygiene infrastructure and modification of risk behaviour are necessary (Freeman et al., 2013; WHO, 2015; Steinmann et al., 2006; Echazú et al., 2015). One important approach is the provision of safe water supplies to reduce the need for contact with contaminated water bodies and diminish the risk of schistosomiasis transmission. However, quality evidence for the impact of provision of safe water on schistosomiasis infection is currently lacking. Recently, a systematic review concluded that previous studies on this topic were of poor quality and likely confounded by multiple socio-economic and other variables (Grimes et al., 2014). The authors conclude that new research is needed ‘on the relationships between water, sanitation, hygiene, human behaviour, and schistosome transmission'. In this regard, the previously used exposure measures of household or individual access to safe water (Grimes et al., 2014) will underestimate the true health impacts of the provision of safe water at a population level. This is because the introduction of safe water supplies into a community offers protection from schistosomiasis infection in two ways. Firstly, it offers direct protection to individuals with access to safe water by reducing their contact with infected water bodies through household domestic water collection activities. Secondly, the scale-up in access to safe water will confer indirect protection to members of a community in a manner that is analogous to the concept of ‘herd immunity’ (Fine et al., 2011). This form of protection is conferred through a reduction in the number of contacts that infected individuals have with open water bodies leading to a decrease in the overall levels of intensity of infection in the surrounding community. Thus, the greater the proportion of individuals in a community who are not actively contributing to the perpetuation of the schistosomiasis transmission cycle, the smaller the probability that those who interact with open water bodies will come into contact with free swimming cercariae released by infected snails.

Hence, to capture the true overall population-level impact of the introduction of piped water, it is necessary to move beyond a simple household/individual level exposure approach towards a sensitive community-level exposure estimate that quantifies the coverage of piped water in the surrounding local community over an extended period of time preceding the measurement of the disease outcome. To date, no studies have quantified the (likely non-linear) relationship between coverage of piped water (%) at the community level and corresponding reductions in risk of schistosomiasis infection. From an epidemic control and eradication perspective, quantification of such a relationship would provide the best indicator of the true overall impact of the scale-up in piped water and would reveal critical coverage targets for achieving meaningful reductions in risk of schistosomiasis infection.

To accomplish this goal, we nested a Schistosoma haematobium survey within one of Africa’s largest population-based cohorts in which all children and their families (N≈90,000) have been intensively followed up in an endemic area of rural KwaZulu-Natal, South Africa. We exploit the heterogeneity in the space-time scale-up of piped water over seven years and detailed longitudinal risk factor information to provide a rigorous quantification of the impact of the introduction of piped water on risk of urogenital schistosomiasis infection measured in children living in the study community. We then validate the veracity of our findings using an instrumental variable approach.

Of the 2105 participants tested in the parasitological survey, 353 were infected with Schistosoma haematobium. The overall prevalence was 16.8% (95% CI: 15.2–18.4) and the prevalence of heavy infection (≥50 eggs/10 ml of urine) was 9.5% (95% CI: 8.3–10.8). Prevalence was twice as high in boys compared to girls (22.6% versus 11.4%) and peaked at 30% in boys at age 14 years and 15% in girls at age 12 years (Figure 1). Prevalence of heavy infection was 13.5% in boys (and peaked at 21.4% at age 14 years) and 5.7% in girls (peaking at 8.7% at age 12 years). 57% of all infected participants had heavy infections with a geometric mean egg burden of 65 eggs/10 ml urine. Overall, 80% of the egg burden was borne by just 3.4% of all pupils tested (20.4% of all infected participants).

Figure 1

Download asset Open asset

Overall, 67% of participants who were present at school on the day of testing had parental consent and assent and provided a urine specimen. Only 2.2% of linked participants reported receiving praziquantel in the last 12 months (Table 1). Participation in the parasitological survey did not differ significantly by piped water access or any of the environmental and socio-economic control variables used in the analysis. However, there were differences in levels of participation by school (p<0.05) and small differences by age and sex (p<0.1). After standardising for these differences according to age, sex and school, the prevalence remained virtually unchanged at 16.9%.

The geospatial analysis using the Gaussian kernel approach revealed substantial spatial variation in prevalence of urogenital schistosomiasis across the different communities within the surveillance area (Figure 2). The kernel-derived prevalence of urogenital schistosomiasis varied between 0% and 85% and revealed two clear regions of high transmission where prevalence was >50%. A formal micro-geographical clustering analysis using the Kulldorff spatial scan statistic revealed four significant foci of infection around open water collection sites (relative risk = 2.99–5.63, p<0.05; Figure 2, Table 2). Two clusters of infection were located along the Nsane River in the northeast of the surveillance area (clusters 2 and 3) and the other two located around water bodies towards the south of the study area.

Figure 2

Download asset Open asset

Between 2001 and 2007, the provision of piped water in the surveillance area nearly doubled from 42% to 80% (Figure 3). The community-based mean coverage of piped water between 2001 and 2007 was 61.7% (Figure 3, bottom right). There is a high degree of temporal and spatial consistency in the patterns of expansion of piped water access across the five respective surveys, providing assurance of the quality of the underlying population-based data.

Figure 3

Download asset Open asset

We identified a clear dose-response between the risk of urogenital schistosomiasis infection and piped water coverage in the surrounding local community (Figure 4a). Participants living in communities with a high coverage of piped water (median coverage = 95.5%) were eight times less likely to be infected (aOR = 0.12, 95% CI 0.06–0.26, p<0.001; Model 1 of Table 3) in comparison to those living in communities with lowest coverage (median coverage = 8.4%). This reduction was greater for girls than boys (aOR = 0.07 versus aOR = 0.17 in boys; Model 1 of Table 4) after controlling for key socio-economic and environmental predictors of infection.

Figure 4 with 1 supplement see all

Download asset Open asset

Using a continuous measure of community-level piped water coverage, we found that every 1% increase in the coverage of community piped water was independently associated with a 2.5% decrease in the odds of infection (aOR = 0.975, 95% CI 0.966–0.985, p<0.001; see Figure 4b). The relationship is a non-linear one because it is derived from the piped water coverage regression coefficient (–0.025) and then exponentiated to derive an odds ratio. The resulting relationship would imply that relatively modest levels of piped water coverage in a given community could have a large impact on reduction in the odds of infection. For example, a piped water coverage of 30% would correspond to a greater than 50% decrease in the odds of infection for individuals living in that community (Figure 4b). We also found that the result of our primary analysis (ie effect of community-level piped water coverage) was robust to the exact specification of the community (kernel) evaluating the piped water coverage around each member of the cohort. For example, when we increased the size of the kernel’s search radius from 2 km to 3 km then we found that both the continuous (aOR = 0.97, 95% CI: 0.96–0.98, p<0.001) and categorical effect-size estimates (quintile five versus quintile one aOR = 0.12, 95% CI: 0.06–0.24, p<0.001) remained virtually unchanged. Further, we report similar results for a parallel set of analyses (using a Poisson model) to derive risk ratios of infection (Figure 4—figure supplement 1, Supplementary file 2).

For a comparative exercise, we replaced the mean community-level piped water exposure (2001–2007) exposure variable with the piped water coverage only in the year of the parasitological survey (2007). As anticipated, we find in this model that the resulting risk-reduction is attenuated (by 44%) but nevertheless still significant (aOR = 0.986, 95% CI: 0.98–0.99, p=0.004), providing further empirical support for the analytical approach taken in the longitudinal measurement of mean piped water coverage in the years leading up to the parasitological survey.

As a secondary objective, we analysed the impact of household access to piped water on risk of urogenital schistosomiasis infection. We found that this exposure was independently associated with a significant reduction in the odds of infection (aOR = 0.54, 95% CI: 0.33–0.89, p=0.017; Model 2 of Table 3), which was considerably smaller than its community-level counterpart. Once again this reduction was driven mostly by the reduction in risk in girls (aOR = 0.38, 95% CI: 0.19–0.75, p=0.005; Model 2 of Table 4), which was substantially larger than the corresponding impact in boys (aOR = 0.76, 95% CI: 0.41–1.42, p=0.38; Model 2 of Table 4, see also Figure 5). When formally testing this as an interaction effect in the combined-sex model, the reduction in risk was significantly greater for girls (p=0.014).

Figure 5

Download asset Open asset

Our risk-reduction estimate for access to piped water at the household level (aOR = 0.54) is close to the Schistosoma haematobium specific aOR of 0.57 generated as part of Grimes et al. systematic review (Grimes et al., 2014) comprising 17 previous studies (Abou-Zeid et al., 2012; Al-Waleedi et al., 2013; Awoke et al., 2013; Dame et al., 2006; Dawet, 2012; Farooq et al., 1966; Howarth et al., 1988; Knopp et al., 2013; Nworie et al., 2012; Reuben et al., 2013; Sady et al., 2013) (Figure 6). The revised overall risk-reduction estimate changes marginally after inclusion of our results and rerunning the meta-analysis, to 0.56 (95% CI: 0.45–0.70). The fact that our effect-size estimate for this secondary analysis is close to that obtained from the systematic review and meta-analysis (based on 17 data-points), lends further credence to our findings - including the risk-reduction result reported from our primary analysis (described above).

Figure 6

Download asset Open asset

Finally, our IV analysis to address any potential residual confounding confirms all of our main results. Our instrumental variable met the relevance condition of instrumental variable estimation, as shown by the size of the F-test for instrument strength (F-statistic = 55). In the IV analysis using a linear model, a 1% increase in the coverage of piped water in the surrounding community was independently associated with a 0.48% decrease in the risk of urogenital schistosomiasis infection (Coef= –0.0048, p<0.001; Model 2 of Supplementary file 2). This effect-size estimate is larger than the risk reduction observed without the IV, which was 0.30% (Coef = –0.003, p<0.001; Model 1 of Supplementary file 2). Household access to piped water was associated with a 15.6% reduction in the risk of infection (Coef = –0.156, p=0.035; Model 4 of Supplementary file 2). This effect-size is higher than the estimate in the analysis without the IV, which was 9.2% (Coef = –0.092; p=0.013; Model 3 of Supplementary file 2). These findings demonstrate that the reductions in urogenital schistosomiasis risk are not overestimated as a result of other unmeasured variables or residual confounding.

We have linked longitudinal population-based exposures to schistosomiasis outcomes in a large cohort of children to provide a rigorous quantification of the relationship between piped water coverage and the risk of urogenital schistosomiasis infection in a typical rural sub-Saharan African population. This approach provides a strong indicator of the overall impact of the introduction of piped water because it captures both the direct and indirect protection conferred by the scale-up. Our results reveal a large impact of piped water scale-up on urogenital schistosomiasis infection. This impact is substantially greater than previous results had suggested (Grimes et al., 2014), based on measurement of access to safe water supplies at the household or individual level. In our study, children living in communities with a high coverage of piped water were eight times less likely to be infected relative to those living in areas with little or no access to piped water. Every 1% increase in the coverage of piped water in the surrounding local community was associated with a corresponding 2.5% decrease in the odds of infection. As a secondary objective, we also provide a robust household-level piped water risk-reduction estimate (aOR = 0.54) after controlling for key socio-economic and environmental predictors of infection. This result is also important, because the relationship between piped water and schistosomiasis infection at the household or individual level would be confounded in different ways by multiple socio-economic and environmental factors, as the recent systematic review of the existing literature points out (Grimes et al., 2014). Importantly, we validate the veracity of all of the above findings using an instrumental variable approach to control for the influence of residual unobserved confounding that could have biased our effect-size estimates.

Our results show that nearly 1 in 5 primary school children (median age = 11 years) were infected with urogenital schistosomiasis and nearly 1 in 10 were heavily infected. These levels of urogenital schistosomiasis infection are markedly lower than the overall prevalence of 60–70% recorded in the general area thirty years earlier (Gear et al., 1980) and 69% prevalence recorded in a more recent survey undertaken in a neighbouring district at similar altitude (<300 metres above sea level) but with limited access to piped water (Saathoff et al., 2004). Importantly, our geographical analyses revealed remarkable spatial heterogeneity in urogenital schistosomiasis prevalence across the relatively small surveillance area. For example, the prevalence of infection was >80% in some communities where the roll-out of piped water had not taken place. At a micro-geographical level, we identified clear spatial clustering of infections around four specific open water collection sites. This heterogeneity in schistosomiasis transmission is consistent with the results of a 2004 study conducted 200 km north of the surveillance area (Saathoff et al., 2004).

Another important finding to emerge from the work is that the greater burden of urogenital schistosomiasis infection is borne by boys who had more than twice the prevalence of infection in comparison to girls. In addition, there were clear sex differentials in the pathways of infection and the impact of piped water at a household and community level. Whilst environmental variables (distance to water-body, altitude and landcover class) were highly predictive of infection in both sexes, higher socio-economic status was independently associated with a decrease in the odds of infection for girls but not for boys. At the community level, piped water coverage was associated with significantly greater reductions in the infection risk for girls. Similarly, household access to piped water was associated with a significant reduction in risk in girls but not in boys. These sex differences at both the community and household level could reflect the cultural context in which girls are responsible for the majority of the domestic water collection and/or a situation where boys swim more often in infected water bodies (Ngorima et al., 2008; Thomassen Morgas et al., 2010).

The differential in the community/household risk-reduction result for boys suggests that the scale-up of piped water has conferred substantial indirect protection to boys through the lowering of the overall parasite reservoir in a particular community. Had we only analysed access to piped water as a single household-level exposure, we would have failed to demonstrate that the scale-up this intervention offered any significant protection to boys. Boys are exposed in the community to free swimming cercariae released by snails infected not just by themselves but also by their family members and neighbours. Therefore, in order to fully capture the impact of piped water in reducing schistosomiasis transmission risk, it is crucial to look beyond the household and consider the piped water coverage level in the surrounding local community. This view is also expressed by other authors (Esrey and Habicht, 1986). The greater than four-fold ratio in the community-level (aOR = 0.12) versus household-level (aOR = 0.54) risk-reduction estimate provides empirical support for this viewpoint. Whilst no such community-level analyses have previously been conducted for Schistosoma haematobium, a study in China found Schistosoma japonicum infection rates to be significantly lower in communities where more than 50% of people used ‘hygienic lavatories’ (Yang et al., 2009).

Our study design has several strengths that overcome many of the limitations typically associated with school-based surveys in which the exposures and disease outcomes are measured at the same point in time. By nesting the parasitological survey within a population cohort followed over seven years preceding the measurement of the disease, we utilise longitudinal data collected both on the participants of the parasitological survey, as well as the entire population to provide a rigorous quantification of the impact of the introduction of piped water on risk of urogenital schistosomiasis infection. A key strength of the work is that rather than treating access to piped water as a household/individual level exposure as in previous work (Abou-Zeid et al., 2012; Al-Waleedi et al., 2013; Awoke et al., 2013; Dame et al., 2006; Dawet, 2012; Farooq et al., 1966; Howarth et al., 1988; Knopp et al., 2013; Nworie et al., 2012; Reuben et al., 2013; Sady et al., 2013), we were able to quantify the clear dose-response in the relationship between community coverage of piped water and risk of infection. In addition, we used a novel spatial methodology to perform a micro-geographical clustering analysis, based on the exact location of the participants’ households, to pinpoint clear transmission foci around four open water collection points. Crucially, through the use of high resolution satellite imagery, ancillary environmental data and variables collated as part of the ongoing population cohort, we controlled for the key individual, social, environmental and community determinants of schistosomiasis infection which are vital to quantifying the independent impact of piped water. Notwithstanding the above, we wanted to completely exclude the possibility of residual confounding (on the basis of unmeasured social and environmental factors) leading to an over-estimate of the impact of the introduction of piped water on risk of infection. To address this possibility, we performed an instrumental variables (IV) analysis using the year that piped water was first introduced into the community as the instrument. Our results remained robust in this analysis: all effect-size estimates remained significant and increased to some extent in comparison to the main results. These findings demonstrate that the reductions in risk reported in this paper are robust and may be regarded as being marginally conservative.

However, the study has some limitations. The results are based on a single urine sample and as a result we would likely have missed some light infections (Kosinski et al., 2011). The true prevalence of infection is therefore probably higher than 16.8% reported in this study. A further limitation is that, while South Africa is a country with near-universal primary school attendance (Lund, 2011), we could be missing some children who did not attend school on the day of testing because they have schistosomiasis-related morbidity. This would lead to an under-estimate of the prevalence of urogenital schistosomiasis. In addition, the high mobility of this population (Dobra et al., 2017) means that children would be moving between communities, resulting in an ascertainment error leading ultimately to a reduction in the true effect-size, which would bias the result towards the null hypothesis of no piped water impact. In addition, infrequent, ad-hoc schistosomiasis ‘test and treat’ activities conducted in schools in the highest-risk areas (where piped water coverage was lowest), could theoretically bias the effect-size towards the null hypothesis of no piped water impact. However, introduction of such a bias by these activities is unlikely given the large piped water effect-sizes that we report and the fact that only 2% of participants in our survey reported receiving praziquantel in the previous 12 months (which we use as a control variable in the statistical model). In any event, any related ‘residual confounding’ effect if it did exist, would be controlled for in the IV approach which confirmed all of our main findings.

Schistosomiasis and soil-transmitted helminthiasis affect more than 1.5 billion of the world's poorest people (Lai et al., 2015). Without a change in strategy to control these diseases, the population of sub-Saharan Africa will lose an estimated US$3.5 billion of economic productivity every year, which is comparable to recent acute epidemics, including the 2014 Ebola and 2015 Zika epidemics (Lo et al., 2017). Our analytical approach (quantifying the combined direct and indirect impact of the scale-up in piped water) has captured a large measurable association between the introduction of piped water and the reduction in risk of urogenital schistosomiasis in a rural African community. This reduction is substantially greater than had been suggested by previous work (Grimes et al., 2014), which focused on household or individual level access to safe water. The continued expansion of safe water supplies, decreasing prevalence and highly focal nature of disease transmission, suggest that the disease is retreating and could be vulnerable to eradication in similar African contexts. Recent modelling work indicates that the current WHO treatment goals (which aim to mass-treat 75% of at-risk children) should be successful in eradicating schistosomiasis transmission if continued for a decade in areas of low to moderate transmission (Anderson et al., 2015). However, once mass treatment is suspended, the prevalence of infection can rebound to pre-control levels over a period of 25–30 years (Gurarie et al., 2015), emphasising the need for a fully integrated approach that includes the provision of safe water supplies, community health education as well as the possibility of focal snail control (Knopp et al., 2013; King et al., 2015).

The overall moderate levels of infection (significantly lower than historical surveys (Gear et al., 1980) combined with the extreme inter-host and geographical heterogeneity as well as ever increasing access to safe water suggest that sustained mass treatment campaigns in such populations could drive schistosomiasis towards low levels of endemicity (Crompton and WHO, 2006). Our results emphasise the need to rapidly increase the provision of safe water supplies to schistosomiasis endemic areas, where the resulting impact will extend well beyond prevention of transmission of schistosomiasis to diarrhoeal diseases, for example (Esrey and Habicht, 1986); as well as having substantial related economic benefits (Lo et al., 2017). Scale-up in the provision of safe water and sanitation, along with the small cost of drugs combined with the renewed commitment to increase funding for neglected tropical diseases, including schistosomiasis, means that there is a real opportunity to push back the disease from areas where it has long been endemic.

1. 1. Abou-Zeid AH
   2. Abkar TA
   3. Mohamed RO

   (2012) Schistosomiasis and soil-transmitted helminths among an adult population in a war affected area, Southern Kordofan state, Sudan

   Parasites & Vectors 5:133.

   https://doi.org/10.1186/1756-3305-5-133

   • PubMed
   • Google Scholar
2. 1. Al-Waleedi AA
   2. El-Nimr NA
   3. Hasab AA
   4. Bassiouny HK
   5. Al-Shibani LA

   (2013) Urinary schistosomiasis among schoolchildren in Yemen: prevalence, risk factors, and the effect of a chemotherapeutic intervention

   The Journal of the Egyptian Public Health Association 88:130–136.

   https://doi.org/10.1097/01.EPX.0000441277.96615.96

   • PubMed
   • Google Scholar
3. 1. Anderson RM
   2. Turner HC
   3. Farrell SH
   4. Yang J
   5. Truscott JE

   (2015) What is required in terms of mass drug administration to interrupt the transmission of schistosome parasites in regions of endemic infection?

   Parasites & Vectors 8:553.

   https://doi.org/10.1186/s13071-015-1157-y

   • PubMed
   • Google Scholar
4. 1. Angrist JD
   2. Imbens GW
   3. Rubin DB

   (1996) Identification of causal effects using instrumental variables

   Journal of the American Statistical Association 91:444–455.

   https://doi.org/10.1080/01621459.1996.10476902

   • Google Scholar
5. 1. Appleton CC
   2. Kvalsvig JD

   (2006) A school-based helminth control programme successfully implemented in KwaZulu-Natal

   Southern African Journal of Epidemiology and Infection 21:55–67.

   https://doi.org/10.1080/10158782.2006.11441265

   • Google Scholar
6. 1. Appleton CC
   2. Madsen H

   (2012) Human schistosomiasis in wetlands in southern Africa

   Wetlands Ecology and Management 20:253–269.

   https://doi.org/10.1007/s11273-012-9266-2

   • Google Scholar
7. 1. Awoke W
   2. Bedimo M
   3. Tarekegn M

   (2013) Prevalence of schistosomiasis and associated factors among students attending at elementary schools in Amibera District, Ethiopia

   Open Journal of Preventive Medicine 3:199–204.

   https://doi.org/10.4236/ojpm.2013.32027

   • Google Scholar
8. 1. Baiocchi M
   2. Cheng J
   3. Small DS

   (2014) Instrumental variable methods for causal inference

   Statistics in Medicine 33:2297–2340.

   https://doi.org/10.1002/sim.6128

   • PubMed
   • Google Scholar
9. Book

   1. Baum CF

   (2006)

   An Introduction to Modern Econometrics Using Stata

   Texas: Stata press.

   • Google Scholar
10. 1. Bärnighausen T
    2. Oldenburg C
    3. Tugwell P
    4. Bommer C
    5. Ebert C
    6. Barreto M
    7. Djimeu E
    8. Haber N
    9. Waddington H
    10. Rockers P
    11. Sianesi B
    12. Bor J
    13. Fink G
    14. Valentine J
    15. Tanner J
    16. Stanley T
    17. Sierra E
    18. Tchetgen ET
    19. Atun R
    20. Vollmer S

    (2017) Quasi-experimental study designs series-paper 7: assessing the assumptions

    Journal of Clinical Epidemiology 89:53–66.

    https://doi.org/10.1016/j.jclinepi.2017.02.017

    • PubMed
    • Google Scholar
11. Book

    1. Crompton DWT
    2. WHO

    (2006)

    Preventive Chemotherapy in Human Helminthiasis: Coordinated Use of Anthelminthic Drugs in Control Interventions: A Manual for Health Professionals and Programme Managers

    Geneva: World Health Organization.

    • Google Scholar
12. 1. Dame J
    2. Banwat E
    3. Egah D
    4. Shabi M

    (2006)

    Schistosomiasis among students in a local government area of Kaduna State in Northern Nigeria

    Highland Medical Research Journal 4:60–69.

    • Google Scholar
13. 1. Dawet A

    (2012) Prevalence and intensity of Schistosoma haematobium among residents of Gwong and Kabong in Jos north local government area, Plateau State, Nigeria

    International Journal of Biological and Chemical Sciences 6:1557–1565.

    https://doi.org/10.3923/ijtmed.2012.69.73

    • Google Scholar
14. 1. Dobra A
    2. Bärnighausen T
    3. Vandormael A
    4. Tanser F

    (2017) Space-time migration patterns and risk of HIV acquisition in rural South Africa

    AIDS 31:137–145.

    https://doi.org/10.1097/QAD.0000000000001292

    • PubMed
    • Google Scholar
15. Book

    1. Doumenge J-P
    2. Mott KE
    3. Cheung C

    (1987)

    Atlas of the Global Distribution of Schistosomiasis Continued

    Presses universitaires de Bordeaux.

    • Google Scholar
16. Book

    1. Eastman J

    (2009)

    IDRISI Taiga Guide to GIS and Image Processing

    Worcester: Clark University.

    • Google Scholar
17. 1. Echazú A
    2. Bonanno D
    3. Juarez M
    4. Cajal SP
    5. Heredia V
    6. Caropresi S
    7. Cimino RO
    8. Caro N
    9. Vargas PA
    10. Paredes G
    11. Krolewiecki AJ

    (2015) Effect of Poor Access to Water and Sanitation As Risk Factors for Soil-Transmitted Helminth Infection: Selectiveness by the Infective Route

    PLoS Neglected Tropical Diseases 9:e0004111.

    https://doi.org/10.1371/journal.pntd.0004111

    • PubMed
    • Google Scholar
18. 1. Esrey SA
    2. Habicht JP

    (1986) Epidemiologic evidence for health benefits from improved water and sanitation in developing countries

    Epidemiologic Reviews 8:117–128.

    https://doi.org/10.1093/oxfordjournals.epirev.a036290

    • PubMed
    • Google Scholar
19. 1. Farooq M
    2. Nielsen J
    3. Samaan SA
    4. Mallah MB
    5. Allam AA

    (1966)

    The epidemiology of Schistosoma haematobium and S. mansoni infections in the Egypt-49 project area. 2. Prevalence of bilharziasis in relation to personal attributes and habits

    Bulletin of the World Health Organization 35:293.

    • PubMed
    • Google Scholar
20. 1. Fine P
    2. Eames K
    3. Heymann DL

    (2011) "Herd immunity": a rough guide

    Clinical Infectious Diseases 52:911–916.

    https://doi.org/10.1093/cid/cir007

    • PubMed
    • Google Scholar
21. 1. Freeman MC
    2. Ogden S
    3. Jacobson J
    4. Abbott D
    5. Addiss DG
    6. Amnie AG
    7. Beckwith C
    8. Cairncross S
    9. Callejas R
    10. Colford JM
    11. Emerson PM
    12. Fenwick A
    13. Fishman R
    14. Gallo K
    15. Grimes J
    16. Karapetyan G
    17. Keene B
    18. Lammie PJ
    19. Macarthur C
    20. Lochery P
    21. Petach H
    22. Platt J
    23. Prabasi S
    24. Rosenboom JW
    25. Roy S
    26. Saywell D
    27. Schechtman L
    28. Tantri A
    29. Velleman Y
    30. Utzinger J

    (2013) Integration of water, sanitation, and hygiene for the prevention and control of neglected tropical diseases: a rationale for inter-sectoral collaboration

    PLoS Neglected Tropical Diseases 7:e2439.

    https://doi.org/10.1371/journal.pntd.0002439

    • PubMed
    • Google Scholar
22. Book

    1. Gear J
    2. Pitchford R
    3. Van Eeden J

    (1980)

    Atlas of Bilharzia in South Africa

    Johannesburg: South African Institute for Medical Research.

    • Google Scholar
23. 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
24. 1. Guo G
    2. Zhao H

    (2000) Multilevel modeling for binary data

    Annual Review of Sociology 26:441–462.

    https://doi.org/10.1146/annurev.soc.26.1.441

    • Google Scholar
25. 1. Gurarie D
    2. Yoon N
    3. Li E
    4. Ndeffo-Mbah M
    5. Durham D
    6. Phillips AE
    7. Aurelio HO
    8. Ferro J
    9. Galvani AP
    10. King CH

    (2015) Modelling control of Schistosoma haematobium infection: predictions of the long-term impact of mass drug administration in Africa

    Parasites & Vectors 8:529.

    https://doi.org/10.1186/s13071-015-1144-3

    • PubMed
    • Google Scholar
26. 1. Harris R
    2. Bradburn M
    3. Deeks J

    (2008)

    Metan: fixed-and random-effects meta-analysis

    The Stata Journal 8:3–28.

    • Google Scholar
27. 1. Hegertun IE
    2. Sulheim Gundersen KM
    3. Kleppa E
    4. Zulu SG
    5. Gundersen SG
    6. Taylor M
    7. Kvalsvig JD
    8. Kjetland EF

    (2013) S. haematobium as a common cause of genital morbidity in girls: a cross-sectional study of children in South Africa

    PLoS Neglected Tropical Diseases 7:e2104.

    https://doi.org/10.1371/journal.pntd.0002104

    • PubMed
    • Google Scholar
28. 1. Howarth SE
    2. Wilson JM
    3. Ranaivoson E
    4. Crook SE
    5. Denning AM
    6. Hutchings MS

    (1988)

    Worms, wells and water in western Madagascar

    The Journal of Tropical Medicine and Hygiene 91:255–264.

    • PubMed
    • Google Scholar
29. 1. Jinabhai CC
    2. Taylor M
    3. Coutsoudis A
    4. Coovadia HM
    5. Tomkins AM
    6. Sullivan KR

    (2001) A health and nutritional profile of rural school children in KwaZulu-Natal, South Africa

    Annals of Tropical Paediatrics 21:50–58.

    https://doi.org/10.1080/02724930124553

    • PubMed
    • Google Scholar
30. 1. King CH
    2. Sutherland LJ
    3. Bertsch D
    4. Review S

    (2015) Systematic Review and Meta-analysis of the Impact of Chemical-Based Mollusciciding for Control of Schistosoma mansoni and S. haematobium Transmission

    PLoS Neglected Tropical Diseases 9:e0004290.

    https://doi.org/10.1371/journal.pntd.0004290

    • PubMed
    • Google Scholar
31. 1. Knopp S
    2. Person B
    3. Ame SM
    4. Mohammed KA
    5. Ali SM
    6. Khamis IS
    7. Rabone M
    8. Allan F
    9. Gouvras A
    10. Blair L
    11. Fenwick A
    12. Utzinger J
    13. Rollinson D

    (2013) Elimination of schistosomiasis transmission in Zanzibar: baseline findings before the onset of a randomized intervention trial

    PLoS Neglected Tropical Diseases 7:e2474.

    https://doi.org/10.1371/journal.pntd.0002474

    • PubMed
    • Google Scholar
32. 1. Kosinski KC
    2. Bosompem KM
    3. Stadecker MJ
    4. Wagner AD
    5. Plummer J
    6. Durant JL
    7. Gute DM

    (2011) Diagnostic accuracy of urine filtration and dipstick tests for Schistosoma haematobium infection in a lightly infected population of Ghanaian schoolchildren

    Acta Tropica 118:123–127.

    https://doi.org/10.1016/j.actatropica.2011.02.006

    • PubMed
    • Google Scholar
33. 1. Kreuter F
    2. Valliant R

    (2007)

    A survey on survey statistics: What is done and can be done in Stata

    The Stata Journal 1:1–21.

    • Google Scholar
34. 1. Kulldorff M
    2. Heffernan R
    3. Hartman J
    4. Assunção R
    5. Mostashari F

    (2005) A space-time permutation scan statistic for disease outbreak detection

    PLoS Medicine 2:e59.

    https://doi.org/10.1371/journal.pmed.0020059

    • PubMed
    • Google Scholar
35. 1. Kulldorff M

    (1997) A spatial scan statistic

    Communications in Statistics - Theory and Methods 26:1481–1496.

    https://doi.org/10.1080/03610929708831995

    • Google Scholar
36. Software

    1. Kulldorff M

    (2006) Software for the spatial and space-time statistics, version v7.0

    National Cancer Institute.

    http://www.satscan.org
37. 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
38. 1. Lo NC
    2. Addiss DG
    3. Hotez PJ
    4. King CH
    5. Stothard JR
    6. Evans DS
    7. Colley DG
    8. Lin W
    9. Coulibaly JT
    10. Bustinduy AL
    11. Raso G
    12. Bendavid E
    13. Bogoch II
    14. Fenwick A
    15. Savioli L
    16. Molyneux D
    17. Utzinger J
    18. Andrews JR

    (2017) A call to strengthen the global strategy against schistosomiasis and soil-transmitted helminthiasis: the time is now

    The Lancet Infectious Diseases 17:e64–e69.

    https://doi.org/10.1016/S1473-3099(16)30535-7

    • PubMed
    • Google Scholar
39. 1. Lund F

    (2011) A step in the wrong direction: linking the South Africa Child Support Grant to school attendance

    Journal of Poverty and Social Justice 19:5–14.

    https://doi.org/10.1332/175982711X559118

    • Google Scholar
40. 1. Moodley I
    2. Kleinschmidt I
    3. Sharp B
    4. Craig M
    5. Appleton C

    (2003) Temperature-suitability maps for schistosomiasis in South Africa

    Annals of Tropical Medicine & Parasitology 97:617–627.

    https://doi.org/10.1179/000349803225001445

    • PubMed
    • Google Scholar
41. 1. Murray CJ
    2. Vos T
    3. Lozano R
    4. Naghavi M
    5. Flaxman AD
    6. Michaud C
    7. Ezzati M
    8. Shibuya K
    9. Salomon JA
    10. Abdalla S
    11. Aboyans V
    12. Abraham J
    13. Ackerman I
    14. Aggarwal R
    15. Ahn SY
    16. Ali MK
    17. Alvarado M
    18. Anderson HR
    19. Anderson LM
    20. Andrews KG
    21. Atkinson C
    22. Baddour LM
    23. Bahalim AN
    24. Barker-Collo S
    25. Barrero LH
    26. Bartels DH
    27. Basáñez MG
    28. Baxter A
    29. Bell ML
    30. Benjamin EJ
    31. Bennett D
    32. Bernabé E
    33. Bhalla K
    34. Bhandari B
    35. Bikbov B
    36. Bin Abdulhak A
    37. Birbeck G
    38. Black JA
    39. Blencowe H
    40. Blore JD
    41. Blyth F
    42. Bolliger I
    43. Bonaventure A
    44. Boufous S
    45. Bourne R
    46. Boussinesq M
    47. Braithwaite T
    48. Brayne C
    49. Bridgett L
    50. Brooker S
    51. Brooks P
    52. Brugha TS
    53. Bryan-Hancock C
    54. Bucello C
    55. Buchbinder R
    56. Buckle G
    57. Budke CM
    58. Burch M
    59. Burney P
    60. Burstein R
    61. Calabria B
    62. Campbell B
    63. Canter CE
    64. Carabin H
    65. Carapetis J
    66. Carmona L
    67. Cella C
    68. Charlson F
    69. Chen H
    70. Cheng AT
    71. Chou D
    72. Chugh SS
    73. Coffeng LE
    74. Colan SD
    75. Colquhoun S
    76. Colson KE
    77. Condon J
    78. Connor MD
    79. Cooper LT
    80. Corriere M
    81. Cortinovis M
    82. de Vaccaro KC
    83. Couser W
    84. Cowie BC
    85. Criqui MH
    86. Cross M
    87. Dabhadkar KC
    88. Dahiya M
    89. Dahodwala N
    90. Damsere-Derry J
    91. Danaei G
    92. Davis A
    93. De Leo D
    94. Degenhardt L
    95. Dellavalle R
    96. Delossantos A
    97. Denenberg J
    98. Derrett S
    99. Des Jarlais DC
    100. Dharmaratne SD
    101. Dherani M
    102. Diaz-Torne C
    103. Dolk H
    104. Dorsey ER
    105. Driscoll T
    106. Duber H
    107. Ebel B
    108. Edmond K
    109. Elbaz A
    110. Ali SE
    111. Erskine H
    112. Erwin PJ
    113. Espindola P
    114. Ewoigbokhan SE
    115. Farzadfar F
    116. Feigin V
    117. Felson DT
    118. Ferrari A
    119. Ferri CP
    120. Fèvre EM
    121. Finucane MM
    122. Flaxman S
    123. Flood L
    124. Foreman K
    125. Forouzanfar MH
    126. Fowkes FG
    127. Fransen M
    128. Freeman MK
    129. Gabbe BJ
    130. Gabriel SE
    131. Gakidou E
    132. Ganatra HA
    133. Garcia B
    134. Gaspari F
    135. Gillum RF
    136. Gmel G
    137. Gonzalez-Medina D
    138. Gosselin R
    139. Grainger R
    140. Grant B
    141. Groeger J
    142. Guillemin F
    143. Gunnell D
    144. Gupta R
    145. Haagsma J
    146. Hagan H
    147. Halasa YA
    148. Hall W
    149. Haring D
    150. Haro JM
    151. Harrison JE
    152. Havmoeller R
    153. Hay RJ
    154. Higashi H
    155. Hill C
    156. Hoen B
    157. Hoffman H
    158. Hotez PJ
    159. Hoy D
    160. Huang JJ
    161. Ibeanusi SE
    162. Jacobsen KH
    163. James SL
    164. Jarvis D
    165. Jasrasaria R
    166. Jayaraman S
    167. Johns N
    168. Jonas JB
    169. Karthikeyan G
    170. Kassebaum N
    171. Kawakami N
    172. Keren A
    173. Khoo JP
    174. King CH
    175. Knowlton LM
    176. Kobusingye O
    177. Koranteng A
    178. Krishnamurthi R
    179. Laden F
    180. Lalloo R
    181. Laslett LL
    182. Lathlean T
    183. Leasher JL
    184. Lee YY
    185. Leigh J
    186. Levinson D
    187. Lim SS
    188. Limb E
    189. Lin JK
    190. Lipnick M
    191. Lipshultz SE
    192. Liu W
    193. Loane M
    194. Ohno SL
    195. Lyons R
    196. Mabweijano J
    197. MacIntyre MF
    198. Malekzadeh R
    199. Mallinger L
    200. Manivannan S
    201. Marcenes W
    202. March L
    203. Margolis DJ
    204. Marks GB
    205. Marks R
    206. Matsumori A
    207. Matzopoulos R
    208. Mayosi BM
    209. McAnulty JH
    210. McDermott MM
    211. McGill N
    212. McGrath J
    213. Medina-Mora ME
    214. Meltzer M
    215. Mensah GA
    216. Merriman TR
    217. Meyer AC
    218. Miglioli V
    219. Miller M
    220. Miller TR
    221. Mitchell PB
    222. Mock C
    223. Mocumbi AO
    224. Moffitt TE
    225. Mokdad AA
    226. Monasta L
    227. Montico M
    228. Moradi-Lakeh M
    229. Moran A
    230. Morawska L
    231. Mori R
    232. Murdoch ME
    233. Mwaniki MK
    234. Naidoo K
    235. Nair MN
    236. Naldi L
    237. Narayan KM
    238. Nelson PK
    239. Nelson RG
    240. Nevitt MC
    241. Newton CR
    242. Nolte S
    243. Norman P
    244. Norman R
    245. O'Donnell M
    246. O'Hanlon S
    247. Olives C
    248. Omer SB
    249. Ortblad K
    250. Osborne R
    251. Ozgediz D
    252. Page A
    253. Pahari B
    254. Pandian JD
    255. Rivero AP
    256. Patten SB
    257. Pearce N
    258. Padilla RP
    259. Perez-Ruiz F
    260. Perico N
    261. Pesudovs K
    262. Phillips D
    263. Phillips MR
    264. Pierce K
    265. Pion S
    266. Polanczyk GV
    267. Polinder S
    268. Pope CA
    269. Popova S
    270. Porrini E
    271. Pourmalek F
    272. Prince M
    273. Pullan RL
    274. Ramaiah KD
    275. Ranganathan D
    276. Razavi H
    277. Regan M
    278. Rehm JT
    279. Rein DB
    280. Remuzzi G
    281. Richardson K
    282. Rivara FP
    283. Roberts T
    284. Robinson C
    285. De Leòn FR
    286. Ronfani L
    287. Room R
    288. Rosenfeld LC
    289. Rushton L
    290. Sacco RL
    291. Saha S
    292. Sampson U
    293. Sanchez-Riera L
    294. Sanman E
    295. Schwebel DC
    296. Scott JG
    297. Segui-Gomez M
    298. Shahraz S
    299. Shepard DS
    300. Shin H
    301. Shivakoti R
    302. Singh D
    303. Singh GM
    304. Singh JA
    305. Singleton J
    306. Sleet DA
    307. Sliwa K
    308. Smith E
    309. Smith JL
    310. Stapelberg NJ
    311. Steer A
    312. Steiner T
    313. Stolk WA
    314. Stovner LJ
    315. Sudfeld C
    316. Syed S
    317. Tamburlini G
    318. Tavakkoli M
    319. Taylor HR
    320. Taylor JA
    321. Taylor WJ
    322. Thomas B
    323. Thomson WM
    324. Thurston GD
    325. Tleyjeh IM
    326. Tonelli M
    327. Towbin JA
    328. Truelsen T
    329. Tsilimbaris MK
    330. Ubeda C
    331. Undurraga EA
    332. van der Werf MJ
    333. van Os J
    334. Vavilala MS
    335. Venketasubramanian N
    336. Wang M
    337. Wang W
    338. Watt K
    339. Weatherall DJ
    340. Weinstock MA
    341. Weintraub R
    342. Weisskopf MG
    343. Weissman MM
    344. White RA
    345. Whiteford H
    346. Wiebe N
    347. Wiersma ST
    348. Wilkinson JD
    349. Williams HC
    350. Williams SR
    351. Witt E
    352. Wolfe F
    353. Woolf AD
    354. Wulf S
    355. Yeh PH
    356. Zaidi AK
    357. Zheng ZJ
    358. Zonies D
    359. Lopez AD
    360. AlMazroa MA
    361. Memish ZA

    (2012) Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010

    The Lancet 380:2197–2223.

    https://doi.org/10.1016/S0140-6736(12)61689-4

    • PubMed
    • Google Scholar
42. Conference

    1. Ngorima E
    2. Nkuna Z
    3. Manase G

    (2008) Addressing rural health and poverty through water sanitation and hygiene: Gender perspectives

    2nd CSIR Biennial Conference.

    http://hdl.handle.net/10204/2541

    • Google Scholar
43. 1. Nworie O
    2. Nya O
    3. Anyim C
    4. Okoli C
    5. Okonkwo E

    (2012)

    Prevalence of urinary schistosomiasis among primary school children in Afikpo North Local government area of Ebonyi State

    Annals of Biological Research 3:3894–3897.

    • Google Scholar
44. 1. Reuben RC
    2. Tanimu H
    3. Musa JA

    (2013)

    Epidemiology of urinary schistosomiasis among secondary school students in Lafia, Nasarawa State, Nigeria

    Journal of Biology, Agriculture and Healthcare  3:73–82.

    • Google Scholar
45. Book

    1. Richards JA
    2. Jia X

    (2006)

    Remote Sensing Digital Image Analysis: An Introduction

    Berlin: Springer-Verlag.

    • Google Scholar
46. 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
47. 1. Sady H
    2. Al-Mekhlafi HM
    3. Mahdy MA
    4. Lim YA
    5. Mahmud R
    6. Surin J

    (2013) Prevalence and associated factors of Schistosomiasis among children in Yemen: implications for an effective control programme

    PLoS Neglected Tropical Diseases 7:e2377.

    https://doi.org/10.1371/journal.pntd.0002377

    • PubMed
    • Google Scholar
48. Book

    1. Shiff C

    (2001)

    Epidemiology of helminth infections

    In: Nelson K, Masters-Williams C, Graham N, editors. Infectious Disease Epidemiology. Gaithersburg: Aspen Publishers. pp. 711–726.

    • Google Scholar
49. 1. Steinmann P
    2. Keiser J
    3. Bos R
    4. Tanner M
    5. Utzinger J

    (2006) Schistosomiasis and water resources development: systematic review, meta-analysis, and estimates of people at risk

    The Lancet Infectious Diseases 6:411–425.

    https://doi.org/10.1016/S1473-3099(06)70521-7

    • PubMed
    • Google Scholar
50. 1. Stothard JR
    2. Chitsulo L
    3. Kristensen TK
    4. Utzinger J

    (2009) Control of schistosomiasis in sub-Saharan Africa: progress made, new opportunities and remaining challenges

    Parasitology 136:1665–1675.

    https://doi.org/10.1017/S0031182009991272

    • PubMed
    • Google Scholar
51. 1. Tanser F
    2. Bärnighausen T
    3. Cooke GS
    4. Newell ML

    (2009) Localized spatial clustering of HIV infections in a widely disseminated rural South African epidemic

    International Journal of Epidemiology 38:1008–1016.

    https://doi.org/10.1093/ije/dyp148

    • PubMed
    • Google Scholar
52. 1. Tanser F
    2. Bärnighausen T
    3. Grapsa E
    4. Zaidi J
    5. Newell ML

    (2013) High coverage of ART associated with decline in risk of HIV acquisition in rural KwaZulu-Natal, South Africa

    Science 339:966–971.

    https://doi.org/10.1126/science.1228160

    • PubMed
    • Google Scholar
53. 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
54. 1. Tanser F
    2. Hosegood V
    3. Benzler J
    4. Solarsh G

    (2001) New approaches to spatially analyse primary health care usage patterns in rural South Africa

    Tropical Medicine and International Health 6:826–838.

    https://doi.org/10.1046/j.1365-3156.2001.00794.x

    • PubMed
    • Google Scholar
55. 1. Tanser F

    (2006) Methodology for optimising location of new primary health care facilities in rural communities: a case study in KwaZulu-Natal, South Africa

    Journal of Epidemiology & Community Health 60:846–850.

    https://doi.org/10.1136/jech.2005.043265

    • PubMed
    • Google Scholar
56. 1. Taylor M
    2. Jinabhai CC
    3. Couper I
    4. Kleinschmidt I
    5. Jogessar VB

    (2001) The effect of different anthelmintic treatment regimens combined with iron supplementation on the nutritional status of schoolchildren in KwaZulu-Natal, South Africa: a randomized controlled trial

    Transactions of the Royal Society of Tropical Medicine and Hygiene 95:211–216.

    https://doi.org/10.1016/S0035-9203(01)90171-3

    • PubMed
    • Google Scholar
57. 1. Thomassen Morgas DE
    2. Kvalsvig JD
    3. Taylor M
    4. Gundersen SG
    5. Kjetland EF

    (2010) Schistosomiasis and water-related practices in school girls in rural KwaZulu-Natal, South Africa

    Southern African Journal of Epidemiology and Infection 25:30–33.

    https://doi.org/10.1080/10158782.2010.11441406

    • Google Scholar
58. Website

    1. Uniting to Combat Neglected Tropical Diseases

    (2012) The London Declaration

    Accessed December 30, 2017.

    http://unitingtocombatntds.org/resource/london-declaration
59. Report

    1. Uniting to Combat Neglected Tropical Diseases

    (2016)

    Reaching the Unreached: Fourth Progress Report of the London Declaration

    Uniting to Combat Neglected Tropical Diseases.

    • Google Scholar
60. Report

    1. UWP Engineers

    (2002)

    Umkhanyakude District Municipality Water Services Development Plan

    Umkhanyakude District Municipality .

    • Google Scholar
61. 1. Vandormael A
    2. de Oliveira T
    3. Tanser F
    4. Bärnighausen T
    5. Herbeck J

    (2018) A high percentage of undiagnosed HIV cases in a rural and hyper-endemic South African community

    Journal Of Epidemiology & Community Health 72:168–172.

    https://doi.org/10.1136/jech-2017-209713

    • Google Scholar
62. 1. WHO Expert Committee

    (2002)

    Prevention and control of schistosomiasis and soil-transmitted helminthiasis

    World Health Organization Technical Report Series 912:.

    • PubMed
    • Google Scholar
63. Report

    1. WHO

    (1991)

    Basic Laboratory Methods in Medical Parasitology

    Geneva: World Health Organization.

    • Google Scholar
64. Report

    1. WHO

    (2010)

    Informal Consultation on Expanding Schistosomiasis Control in Africa

    Geneva: World Health Organisation.

    • Google Scholar
65. Report

    1. WHO

    (2013)

    Sustaining the Drive to Overcome the Global Impact of Neglected Tropical Diseases: Second WHO Report on Neglected Tropical Diseases

    World Health Organisation.

    • Google Scholar
66. Report

    1. WHO

    (2015)

    Water Sanitation and Hygiene for Accelerating and Sustaining Progress on Neglected Tropical Diseases a Global Strategy 2015–2020

    Geneva: World Health Organisation.

    • Google Scholar
67. 1. Yang J
    2. Zhao Z
    3. Li Y
    4. Krewski D
    5. Wen SW

    (2009) A multi-level analysis of risk factors for Schistosoma japonicum infection in China

    International Journal of Infectious Diseases 13:e407–e412.

    https://doi.org/10.1016/j.ijid.2009.02.005

    • PubMed
    • Google Scholar

Author details

1. Frank Tanser

   1. Africa Health Research Institute, KwaZulu-Natal, South Africa
   2. School of Nursing and Public Health, University of KwaZulu-Natal, Durban, South Africa
   3. Centre for the AIDS Programme of Research in South Africa (CAPRISA), University of KwaZulu-Natal, Durban, South Africa
   4. Institute of Epidemiology and Health Care, University College London, London, United Kingdom

   Contribution

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

   For correspondence

   ftanser@gmail.com

   Competing interests

   No competing interests declared

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

2. Daniel K Azongo

   Navrongo Health Research Centre, Ghana Health Service, Navrongo, Upper East Region, Ghana

   Contribution

   Investigation, Methodology, Writing—original draft, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

3. Alain Vandormael

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

   Contribution

   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

4. Till Bärnighausen

   1. Africa Health Research Institute, KwaZulu-Natal, South Africa
   2. Institute of Epidemiology and Health Care, University College London, London, United Kingdom
   3. Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, United States
   4. Institute for Public Health, University of Heidelberg, Heidelberg, Germany

   Contribution

   Methodology, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

5. Christopher Appleton

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

   Contribution

   Conceptualization, Funding acquisition, Investigation, Methodology, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

• 1,908

  views

• 191

  downloads

• 15

  citations

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