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      Organomegaly in Mali before and after praziquantel treatment. A possible association with Schistosoma haematobium

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          Continuous exposure to schistosome-infested water results in acute and chronic morbidity in all ages. We analysed occurence of organomegaly via ultrasonography and investigated a possible additive effect of dual-dose drug administration in 401 Schistosoma haematobium infected individuals from a highly endemic area in Mali. Mean intensity of infection at baseline (22.0 eggs per 10 ml) was reduced to 0.22 eggs per 10 ml 9 weeks after treatment (both treatments combined). Odds of persistent infection among those given dual-dose treatment was 41% of that in people given single dose (b = 0.41; p = 0.05; 95% CI 0.17–1.00), but after two years, 70.7% of the 157 participants, who completed the survey, were re-infected with no significant difference in prevalence and intensity of infection between treatment groups. Resolution of organomegaly occurred in all age groups after treatment. A novel association between Schistosoma haematobium infection and moderate portal vein enlargement was found in 35% (n: 55). Severe portal vein diameter enlargement was found in 3.2%. After two years, moderate hepatomegaly was present in 50.6%, moderate splenomegaly in 45.6% and moderate portal vein diameter enlargement in 19%. A subsequent dose of PZQ did not provide any additional long-term advantages.

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          Improved sensitivity of the urine CAA lateral-flow assay for diagnosing active Schistosoma infections by using larger sample volumes

          Background Accurate determination of Schistosoma infection rates in low endemic regions to examine progress towards interruption of transmission and elimination requires highly sensitive diagnostic tools. An existing lateral flow (LF) based test demonstrating ongoing infections through detection of worm circulating anodic antigen (CAA), was improved for sensitivity through implementation of a protocol allowing increased sample input. Urine is the preferred sample as collection is non-invasive and sample volume is generally not a restriction. Methods Centrifugal filtration devices provided a method to concentrate supernatant of urine samples extracted with trichloroacetic acid (TCA). For field trials a practical sample volume of 2 mL urine allowed detection of CAA down to 0.3 pg/mL. The method was evaluated on a set of urine samples (n = 113) from an S. mansoni endemic region (Kisumu, Kenya) and compared to stool microscopy (Kato Katz, KK). In this analysis true positivity was defined as a sample with either a positive KK or UCAA test. Results Implementation of the concentration method increased clinical sensitivity (Sn) from 44 to 98% when moving from the standard 10 μL (UCAA10 assay) to 2000 μL (UCAA2000 assay) urine sample input. Sn for KK varied between 23 and 35% for a duplicate KK (single stool, two slides) to 52% for a six-fold KK (three consecutive day stools, two slides). The UCAA2000 assay indicated 47 positive samples with CAA concentration above 0.3 pg/mL. The six-fold KK detected 25 egg positives; 1 sample with 2 eggs detected in the 6-fold KK was not identified with the UCAA2000 assay. Conclusions Larger sample input increased Sn of the UCAA assay to a level indicating ‘true’ infection. Only a single 2 mL urine sample is needed, but analysing larger sample volumes could still increase test accuracy. The UCAA2000 test is an appropriate candidate for accurate identification of all infected individuals in low-endemic regions. Assay materials do not require refrigeration and collected urine samples may be stored and transported to central test laboratories without the need to be frozen.
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            A Comparative Study of the Spatial Distribution of Schistosomiasis in Mali in 1984–1989 and 2004–2006

            Introduction Mali was one of the first countries in sub-Saharan Africa to initiate a national schistosomiasis control programme. Control efforts started regionally in 1978 in Dogon Country (region of Mopti) after the construction of small dams for growing vegetables, and became a national programme in 1982. During the first 10 years, the programme was run by the Malian Ministry of Health in partnership with the World Health Organization and the German Technical Cooperation (Deutsche Gesellschaft für Technische Zusammenarbeit, GTZ) [1]. Parasitological surveys followed by mass treatment of the entire population in target areas were conducted by a central team from Bamako. Additionally, in selected areas, identification of infected individuals and case treatment was implemented. The control programme was intensively focused on two major endemic areas: Office du Niger (irrigation area) and in the area around Bandiagara in the Plateau Dogon (small dams area). Initial evaluation (1–3 years after intervention) showed reductions in both prevalence of infection and prevalence of heavy-intensity infections (>50 eggs/10 ml urine for Schistosoma haematobium and >100 eggs/gram stool for S. mansoni). For S. haematobium, prevalence of infection was reduced from 58.9 to 26.8% and that of heavy infections from 18.4 to 3.8%, whereas for S. mansoni, prevalence of infection was only reduced from 49.0 to 48.1% and that of heavy infections from 10.6 to 8.9% [2]. Estimated impact of the intervention varied by intervention approach, ecological zone and time to follow up (1–3 years). GTZ support for the programme ceased in 1992, with the government taking over financial responsibility. However, lack of resources led to control activities being considerably reduced and the implications of this for infection levels were not assessed in the immediate post treatment period. From 1998, a new, decentralised control programme was approved by the Ministry of Health but, due to lacking continuous financial support from the government, many planned activities were not implemented. In 2004, a new initiative to recommence national control activities was established with support from the Schistosomiasis Control Initiative (SCI; http://www.schisto.org). Again the main intervention strategy was mass treatment with praziquantel, with a particular focus on treating school-age children [3]. The potential of using risk mapping to describe the spatial patterns of infections is now well-established, and has been demonstrated for a range of diseases including malaria [4],[5], schistosomiasis [6], Loa loa filariasis [7] and lymphatic filariasis [8]. The combination of geographical information systems (GIS), remote sensing and geostatistics has led to an increase in the understanding of the spatial epidemiology of infectious diseases, the prediction of occurrence, and the targeting of large-scale control programmes. For example, Bayesian geostatistical modelling is being used increasingly to predict spatial patterns of human schistosomiasis in Africa [9],[10],[11],[12],[13]. Much of this work to date has used data from a single geographical area at a single point in time to develop predictions for similar locations. Preliminary work has investigated the spatial extent to which risk models can be reliably extrapolated [14] but it remains unclear the extent to which models based on data from one area can be extrapolated temporally. This is particularly important in determining whether control programmes can be spatially targeted on the basis of historic data, or whether it is necessary to conduct new surveys (which are expensive and time consuming) to define the spatial distribution of disease. This issue is especially relevant in the context of the dramatic up-scaling of disease control interventions and the need for survey data to target suites of alternative interventions. In this paper, we use unique data on schistosome infections, available from two nationwide surveys conducted in Mali, the first undertaken during the 1980s prior to the implementation of the GTZ-supported national control programme and the second between 2004–2006, 12 years after this programme had ceased and prior to implementation of the SCI-supported programme. We aim to determine whether the overall prevalence and spatial distribution of schistosomiasis in Mali is different in 2004–2006 compared to the 1980s and to determine whether the spatial distribution, including covariate relationships with environmental variables and parameters that describe the spatial dependence structure (i.e. clustering), have changed in Mali over the last two decades. Materials and Methods Data A nationwide survey was carried out between May 1984 and May 1989 prior to implementation of the GTZ-supported programme (see Traoré et al. [15] for further details). In brief, villages were selected using a three-stage sampling approach: two to three districts were randomly selected in each province, then three to five arrondissements (sub-districts) were randomly selected in each district, and five villages were randomly selected in each arrondissement. In each village, individuals were randomly selected to provide urine (200 individuals) and stool samples (150 individuals). For each individual, a single urine slide (for diagnosis of S. haematobium infection by filtration method), and two Kato-Katz slides prepared from a single faecal sample (for diagnosis of S. mansoni) were examined microscopically using standard methods. While egg counts were done, only data on the number tested and proportion positive (i.e. with one or more eggs) in a given location were available for the current study. Longitude and latitude co-ordinates of each village were identified during the current study from a national village GIS database (http://www.who.int/health_mapping/tools/healthmapper/en/); of the 323 villages surveyed we were able to geo-reference 300 villages, from which data were available on 52,104 individuals. A more recent nationwide survey was conducted in 194 schools (including 15,051 school-aged children) between December 2004 and May 2006. Ethical approval for these surveys was obtained from St. Mary's Hospital Research Ethics Committee UK and the National Public Health Research Institute's (INRSP) scientific committee in Mali. All data collection activities were carefully explained to, and oral consent was obtained from traditional authorities in the village (the village head and the elders), the schoolmaster, the representative of the pupils' parents and the local health authorities. Child participants were given an explanation of the data collection activities and were free not to participate if they so chose. Written consent was not obtained and oral consent was not specifically documented because the survey was considered by the UK and Malian ethical committees as part of the monitoring and evaluation of routine health activities carried out by the Malian Ministry of Health's national schistosomiasis control programme. Survey protocols (available on request) instructed survey teams to select 30 boys and 30 girls per school using systematic random sampling. Schools were selected to maximise geographical coverage of the study area; all parts of Mali excluding the northern desert and far eastern regions, where transmission is known not to occur [16], were included in the survey. This was done in a GIS (ArcView 9.2, ESRI, Redlands, CA) by overlaying a 1 decimal degree squared grid over the country. The locations of communities in Mali were obtained from the aforementioned national village database. Communities were selected using simple random selection from each grid cell and, if more than one school was present in a town or village, a school was sampled on arrival using simple random selection. The selected children were assembled and asked to provide a urine and stool sample. For each child, a single urine slide and two Kato-Katz slides prepared from a single faecal sample were examined microscopically as described above. Numbers of eggs of S. haematobium and S. mansoni in each child's sample were recorded on paper forms, in addition to the geographic location of the school (determined using a hand-held global positioning system). All school and individual data were transferred to a Microsoft Access database. For the current study, numbers tested and positive (defined as one or more eggs for each species of schistosome) were calculated for each survey location. School or community-level raw prevalence was then plotted in the GIS. Electronic data for land surface temperature (LST) and normalised difference vegetation index (NDVI) were obtained from the National Oceanographic and Atmospheric Administration's (NOAA) Advanced Very High Radiometer (AVHRR; see Hay et al. [17] for details on these datasets) and the location of large perennial waterbodies was obtained from the Food and Agriculture Organization of the United Nations (FAO-GIS). Values for LST, NDVI and distance to the nearest perennial water body (DPWB) were calculated in the GIS for each survey location. Spatial risk prediction Multivariable logistic regression models were developed for each species of schistosome and each of the two survey periods in a frequentist statistical software package (Stata version 10.1, Stata corporation, College Station, TX). Prelimary results were similar for each species of schistosome and each study period. A quadratic association between LST and prevalence was assessed and was found to be significant and DPWB was also significantly and negatively associated with prevalence. NDVI was not found to be significantly associated with prevalence in the preliminary multivariable models and was excluded from further analysis. Therefore, it was decided to enter LST (in quadratic form) and DPWB as covariates into the final spatial models. Bayesian geostatistical models, developed in WinBUGS 1.4 (Medical Research Council, Cambridge, UK and Imperial College London, UK), were identically structured for each species of schistosome and each study period. Statistical notation is presented in Text S1. Three chains of the models were run consecutively. A burn-in of 1,000 iterations was allowed, followed by 10,000 iterations where values for the intercept and coefficients were stored. Diagnostic tests for convergence of the stored variables were undertaken, including visual examination of history and density plots of the three chains. Convergence was successfully achieved after 10,000 iterations in each model and the posterior distributions of model parameters were combined across the three chains and summarized using descriptive statistics. Geostatistical prediction across Mali was done in WinBUGS using the spatial.unipred command [18]. To compare predictions accross time periods, the 1984–1989 model was used to predict infection prevalence at the 2004–2006 survey locations and vice versa, for both S. haematobium and S. mansoni. The predicted prevalence was compared to the observed prevalence, dichotomised at 50, 20, 10 and 0% (to assess predictive performance relative to different observed prevalence thresholds, including the World Health Organisation-recommended thresholds for annual and biannual mass chemotherapy of 50% and 10% respectively). The diagnostic test evaluation statistic, area under the curve (AUC) of the receiver operating characteristic, was used for the comparison. An AUC value of >0.7 was taken to indicate acceptable predictive performance [19]. Investigation of stationarity of spatial dependence across time periods A stationary model is one where the parameters that define the spatial dependence structure are the same for the two time periods and a non-stationary model is one where the parameters are different (note we refer to stationarity across time periods, not different parts of the study area). Models were developed using the combined datasets, including with different intercepts for each time period and: 1) different coefficients, spatial dependence parameters and random effects (i.e. assuming separate sub-models for each time period); 2) the same coefficients but different spatial dependence parameters and random effects (i.e. allowing the sub-models to have common covariate effects); 3) the same coefficients and spatial dependence parameters but different random effects (i.e. allowing common covariate effects and stationary spatial dependence structures, but separate predicted risk surfaces); and 4) the same coefficients, spatial dependence parameters and random effects (i.e. a single model giving an overall predicted risk surface across the two time periods). Models 1 and 2 were non-stationary models and models 3 and 4 were stationary models. Statistical notation is presented in Text S2. The best-fitting model (of 1–4) was selected using the deviance information criterion (DIC). An additional comparison of the spatial distribution of schistosomiasis accross time periods was done by subtracting predicted prevalence from the best-fitting S. haematobium and S. mansoni models in 2004–2006 from predicted prevalence in 1984–1989. Results The national prevalence of infection with S. haematobium in 1984–1989 was 25.7% (range, 0.0–93.0%; 95% CI 25.3, 26.0%) and in 2004–2006 was 38.3% (range, 0.0–99.0%; 95% CI 37.5, 39.1%), whereas for S. mansoni, prevalence in 1984–1989 was 7.4% (range, 0.0–77.8%; 95% CI 7.1, 7.6%) and in 2004–2006 was 6.7% (range, 0.0–94.9%; 95% CI 6.3, 7.1%; note, CIs are binomial exact CIs which do not account for the clustered survey design or spatial autocorrelation – see the section on comparative models for significance testing of prevalence in 1984–1989 versus 2004–2006). Maps of community (1984–1989) and school (2004–2006) level prevalence (Figures 1 and 2) show that the data from 1984–1989 had a less uniform geographical distribution than the data from 2004–2006. High prevalence of infection with S. haematobium was widespread in Mali in both survey periods, whereas for S. mansoni, both surveys indicated small clusters of high infection prevalence in central Mali (Macina and Niono districts in the Office du Niger irrigation area) and southwestern areas (e.g. Kati district on the Niger River and Kita and Bafoulabé districts on the Senegal River), but zero or very low prevalence of infection throughout the rest of the country. 10.1371/journal.pntd.0000431.g001 Figure 1 Raw prevalence of Schistosoma haematobium infection (A) in 1984–1989, in 300 villages and (B) in 2004–2006, in 194 schools, Mali, West Africa. 10.1371/journal.pntd.0000431.g002 Figure 2 Raw prevalence of Schistosoma mansoni infection (A) in 1984–1989, in 300 villages and (B) in 2004–2006, in 194 schools, Mali, West Africa. Period-specific models The Bayesian geostatistical models for each time period are presented in Table 1. Note that the odds ratios are on the same scale for each variable, which were standardised to have a mean of zero and standard deviation of one. DPWB was significantly and negatively associated with each outcome, with very similar odds ratios for all four models. The quadratic term for LST was not significant in any of the models, where significance is defined by a 95% posterior interval that excludes one (note, outputs of Bayesian models are distributions termed posterior distributions that describe the probability associated with each of a range of plausible values for the variable being estimated). Phi ( ), which indicates the rate of decay of spatial correlation (with a bigger indicative of smaller clusters) varied from 1.68 to 9.02 for S. haematobium and S. mansoni in 2004–2006. S. haematobium clusters were, therefore, generally larger than S. mansoni clusters. For both types of infection, the sill was lower in 1984–1989 than in 2004–2006, indicating a stronger tendency towards spatial clustering in the latter time period. 10.1371/journal.pntd.0000431.t001 Table 1 Bayesian geostatistical models of Schistosoma haematobium and S. mansoni infection prevalence in 1984–1989 and 2004–2006 in Mali, West Africa. Variable S. haematobium S. mansoni 1984–1989 2004–2006 1984–1989 2004–2006 OR: DPWB 0.50 (0.32,0.71) 0.50 (0.25,0.91) 0.59 (0.34, 0.94) 0.49 (0.20,0.95) OR: LST 1.41 (1.02,1.85) 0.62 (0.33,1.02) 0.48 (0.28, 0.84) 0.40 (0.19,0.71) OR: LST2 0.96 (0.79,1.14) 1.06 (0.81,1.36) 0.88 (0.66, 1.13) 1.02 (0.69,1.48) Intercept −1.73 (−2.15,−1.35) −1.42 (−2.33,0.23) −5.40 (−6.29, −4.60) −6.13 (−7.18,−5.25) Phi ( ) 5.38 (3.69,7.60) 1.68 (0.96,2.60) 6.09 (2.94, 12.04) 9.02 (2.01,54.25) Sill 3.20 (2.43,4.26) 8.24 (5.44,12.79) 6.67 (4.55, 9.98) 9.42 (5.66,15.79) DPWB = distance to perennial water body; LST = land surface temperature; OR = odds ratio; phi = rate of decay of spatial correlation; sill = variance of the spatial random effect. Ninety-five percent posterior intervals are shown in brackets. Models developed on 1984–1989 and 2004–2006 data were generally able to discriminate infection prevalence for the other dataset to an acceptable level (Table 2). For S. haematobium, models tended to perform better when discriminating at lower prevalence thresholds (present versus absent, 0%) in 1984–1989. 10.1371/journal.pntd.0000431.t002 Table 2 Discriminatory performance of Bayesian geostatistical models based on 1984–1989 data for predicting prevalence in 2004–2006 and vice versa, for Schistosoma haematobium and S. mansoni in Mali, West Africa. Observed prevalence threshold S. haematobium S. mansoni Using 1984–1989 data to predict 2004–2006 status Using 2004–2006 data to predict 1984–1989 status Using 1984–1989 data to predict 2004–2006 status Using 2004–2006 data to predict 1984–1989 status ≥50% 0.70 (0.62, 0.78) 0.73 (0.66, 0.79) 0.81 (0.71, 0.92) 0.93 (0.84, 1.00) ≥20% 0.73 (0.65, 0.80) 0.72 (0.66, 0.78) 0.78 (0.64, 0.91) 0.86 (0.78, 0.95) ≥10% 0.78 (0.71, 0.84) 0.74 (0.68, 0.80) 0.82 (0.72, 0.91) 0.79 (0.70, 0.87) >0% 0.82 (0.73, 0.91) 0.82 (0.70, 0.95) 0.70 (0.62, 0.78) 0.67 (0.60, 0.73) The evaluation statistic is area under the receiver operating characteristic curve and it is estimated relative to different observed prevalence thresholds. Ninety-five percent confidence intervals are shown in brackets. Comparative models The deviance information criterion for models 1–4, for S. haematobium and S. mansoni, are presented in Table 3. For S. haematobium, the model with the lowest DIC (indicating the model with the best compromise between model fit and parsimony) was model 2 (Table 4), with common covariate effects but a non-stationary spatial dependence structure across time periods. For S. mansoni, the model with the lowest DIC was model 3 (Table 5), with common covariate effects and a stationary spatial dependence structure across time periods. As for the period-specific models, prevalence of both infections was negatively associated with increasing DPWB and was not significantly associated with LST. In the non-stationary model for S. haematobium (Table 4), the sill was lower for 1984–1989 than for 2004–2006, again indicating greater clustering in the latter time period, and the rates of decay of spatial correlation, phi, were similar for the two time periods. The overlapping 95% posterior interval limits for the 1984–1989 and 2004–2006 intercepts in both the S. haematobium and S. mansoni models suggest that overall (mean) prevalence was not significantly different across time periods for either species of schistosome. 10.1371/journal.pntd.0000431.t003 Table 3 Deviance Information Criterion values for Bayesian geostatistical models of Schistosoma haematobium and S. mansoni infection prevalence in 2004–2006 and 1984–1989 in Mali, West Africa. Model S. haematobium S. mansoni 1) Different coefficients and spatial structure 2952.4 1352.2 2) Same coefficients, different spatial structure 2947.5 1351.9 3) Same coefficients and spatial structure 2949.9 1346.5 4) Data grouped, with single overall prediction 2950.1 1347.6 10.1371/journal.pntd.0000431.t004 Table 4 Bayesian geostatistical model of Schistosoma haematobium infection prevalence in 2004–2006 and 1984–1989 in Mali, West Africa. Variable Posterior mean (95% posterior interval) OR: DPWB 0.51 (0.39, 0.67) OR: LST 1.33 (1.02, 1.77) OR: LST2 0.95 (0.74, 1.12) Intercept: 1984–1989 −1.72 (−2.11, −1.34) Intercept: 2004–2006 −1.37 (−2.17, −0.71) Phi ( ): 1984–1989 5.60 (3.59, 8.24) Phi ( ): 2004–2006 6.82 (1.77, 45.75) Sill: 1984–1989 3.17 (2.42, 4.27) Sill: 2004–2006 6.35 (4.26, 9.70) DPWB = distance to perennial water body; LST = land surface temperature; OR = odds ratio; phi = rate of decay of spatial correlation; sill = variance of the spatial random effect. Ninety-five percent posterior intervals are shown in brackets. 10.1371/journal.pntd.0000431.t005 Table 5 Bayesian geostatistical model of S. mansoni infection prevalence in 2004–2006 and 1984–1989 in Mali, West Africa. Variable Posterior mean (95% posterior interval) OR: DPWB 0.57 (0.35, 0.82) OR: LST 0.45 (0.31, 0.65) OR: LST2 0.92 (0.71, 1.15) Intercept: 1984–1989 −5.39 (−5.99, −4.71) Intercept: 2004–2006 −5.84 (−6.59, −5.18) Phi ( ) 6.47 (3.28, 16.57) Sill 7.15 (5.17, 9.86) DPWB = distance to perennial water body; LST = land surface temperature; OR = odds ratio; phi = rate of decay of spatial correlation; sill = variance of the spatial random effect. Ninety-five percent posterior intervals are shown in brackets. Spatial predictions (showing the mean of the posterior distributions for predicted prevalence) based on the best model for each type of schistosome infection are presented in Figures 3 and 4. In 2004–2006, S. haematobium occurred in large clusters in a mid-latitudinal band from western to central Mali and low predicted prevalence was apparent in both southern and northern latitudinal bands (Figure 3B). In 1984–1989 (Figure 3A), the pattern was similar but more fragmented. The prediction maps for S. mansoni (Figure 4) were remarkably similar to each other, with infection limited to small high-prevalence clusters in central and southwestern regions, althought the clusters occurred in slightly different locations. 10.1371/journal.pntd.0000431.g003 Figure 3 Predicted prevalence of Schistosoma haematobium (A) in 1984–1989 and (B) in 2004–2006, Mali, West Africa. Predictions are based on a non-stationary Bayesian geostatistical model. 10.1371/journal.pntd.0000431.g004 Figure 4 Predicted prevalence of Schistosoma mansoni (A) in 1984–1989 and (B) in 2004–2006, Mali, West Africa. Predictions are based on a stationary Bayesian geostatistical model. Comparative maps show predicted prevalence in 1984–1989 subtracted from predicted prevalence in 2004–2006, using the best-fitting models (Figure 5). Most areas of both maps had an estimated difference of 20% in predicted prevalence; for S. haematobium, higher predicted prevalence in 2004–2006 mainly occurred in central and western regions and lower predicted prevalence was mainly along the Niger river and in southwestern regions; for S. mansoni, differences coincided with the locations of the small high-prevalence foci in central and southwestern regions because the precise location of these clusters varied somewhat between the study periods. 10.1371/journal.pntd.0000431.g005 Figure 5 Difference in predicted prevalence of infection with (A) Schistosoma haematobium and (B) S. mansoni in 1984–1989 and 2004–2006, Mali, West Africa. Predictions for S. haematobium are based on a non-stationary Bayesian geostatistical model and for S. mansoni on a stationary Bayesian geostatistical model, and calculations involved subtracting 1984–1989 predicted values from 2004–2006 predicted values. Discussion Despite differences in survey design and study population between the time periods, this study demonstrated remarkable similarities in the spatial distribution of prevalence of infection with S. haematobium and S. mansoni in Mali between 1984–1989 and 2004–2006. While clusters of infection occurred in generally the same area of the country, the precise location did vary slightly between the two time periods. Nonetheless, our analysis of predictive performance of models across time periods suggests it may be possible, in the first instance, to use historical data to predict contemporary distributions at national scales (assuming a stable climate and an absence of new, large water resource development projects, both of which should be investigated). It is perhaps not surprising that the statistical associations between prevalence and DPWB did not vary between the study periods as the essential biology of schistosome infections is unlikely to have changed, but it is interesting that the spatial dependence structure was different (i.e. non-stationary) for S. haematobium between the time periods. Possible reasons for non-stationary spatial variation of S. haematobium can be broadly categorised into those related to the different sampling strategies used, and those related to changing epidemiology between the two study periods. Regarding the sampling strategies, the data were based on different sample locations, collected for different purposes and from different populations. The data from 1984–1989 were collected from the general population including adults, whilst the 2004–2006 data were from school-aged children. Age-stratified prevalence and intensity of S. haematobium infections in Mali have been reported [15] but individual or location-specific, age-stratified prevalence data were not available in the current study, which can be seen as its major limitation. However, previous analyses (including an analysis of the same 1984–1989 dataset used in this report) have shown that, while prevalence in school-aged children is generally higher than in the adult population, there is a consistent relationship between the prevalence in the two populations such that prevalence in one can be used to predict prevalence in the other [15],[20]. The overall prevalence of S. haematobium in 1984–1989, 25.7%, corresponds to an age-adjusted prevalence of approximately 36% in children aged 7–14 years [15], which is very similar to the prevalence in school-aged children (38.3%) in 2004–2006. The 1984–1989 surveys had a less uniform geographical distribution than the 2004–2006 surveys, which is not surprising given that the 1984–1989 surveys were not explicitly designed with subsequent spatial analysis in mind, whereas uniform geographical coverage was an aim of the survey design for the 2004–2006 study to facilitate spatial analysis. Investigation of the impact of different sampling strategies on observed spatial correlation is an area of future research. Factors potentially related to changing epidemiology include desertification, urban growth and rural-urban migration [21],[22], changing demographic and socioeconomic characteristics of the population, long-term impacts of interventions on transmission and implementation of water resource development projects such as irrigation schemes, large dams and reservoirs [23],[24],[25]. These factors can influence not only stationarity of spatial variation but any differences observed in the location of spatial disease clusters. The earlier, GTZ-supported control programme focussed on specific, perceived high-risk areas of the country, with treatment coverage highest in Bandiagara, Office du Niger, Baguinéda and Sélingué. It might be suggested that spatial variation in changes in prevalence (Figure 5) could relate to uneven geographical coverage of the intervention, but the main intervention areas do not correspond consistently to those where prevalence was lower in 2004–06 than 1984–89. In addition to the limitation of different survey designs between periods, we were not able to compare spatial variation in intensity of infection between time periods because location-specific mean egg counts were not available from the 1984–1989 surveys. Maps of intensity would be useful for determining any changes in transmission across the periods. Examination of a single urine slide or single stool sample as a diagnostic approach results in sub-optimal sensitivity and this will also have affected the accuracy of our maps. We also did not incorporate anisotropy (where the spatial correlation structure varies by direction) or non-stationary spatial variation between different parts of the country, within each time period; these are future potential refinements of the models. We should also point out that the model predictions are distributions and here we have only presented the posterior mean. Examination of the full posterior distribution of predicted prevalence enables assessment of uncertainties arising from sampling and measurement error (including in the model covariates). We have recently described how an understading of these uncertainties can assist decion making in schistosomiasis control programme planning [9]. Our results show that, while there were differences in the raw data, the overall prevalence of neither S. haematobium nor S. mansoni was significantly different between the time periods, despite ten years of donor-funded schistosomiasis control throughout the 1980s and early 1990s. The most likely explanation is that, in the absence of ongoing exposure reduction measures, re-infection with schistosomes following chemotherapy inevitably occurred. In endemic settings this is often apparent within 24 months [26],[27]. Rates of infection and re-infection are generally similar among different age groups, although older people typically reacquire schistosome infection at slower rates than younger people [28]. Problems of re-infection were acknowledged by the managers of the 1980s control programme and this was reflected in the goal to reduce morbidity associated with infection in the treated communities (which was successfully demonstrated in some areas [29]) rather than transmission. The result was a predictable failure of the national programme to have a lasting impact on the burden of schistosomiasis in subsequent generations of Malians. One of the most important conclusions arising from the current work is that it is essential to develop a sustainability strategy to ensure ongoing benefits from the current national control programme. Recognising this fact, SCI has developed a sustainability plan which is outlined in Fenwick et al. [30]. Briefly, sustainability is based on initially using annual mass chemotherapy in areas with prevalence ≥50%, or biannual mass chemotherapy where prevalence is ≥10% and <50%, to rapidly reduce prevalence and intensity of infection. Then, when prevalence reaches <10% (after up to four rounds of treatment, depending on levels of transmission), the Malian government plans to make treatments available in health facilities, carry out regular surveys and target treatment in schools if the prevalence rises above 10%. Sustainability also depends on developing the Malian health system and integrating schistosomiasis control with routine health care delivery [31]. Improved water sanitation and health education could be promoted for sustainable control [32], snail control could be revisited and schistosomiasis vaccines might also have a future role [33]. The maps presented here can be used to target what are likely to be more limited national resources in the longer term to the highest-risk areas, where they will have the greatest impact on infection, morbidity, and (hopefully) transmission. The current move towards integration of control of neglected tropical diseases means that the government may have the opportunity to implement a cost effective control programme encompassing schistosomiasis, soil transmitted helminth infections, lymphatic filariasis, river blindness and trachoma. It is clear that a commitment from the Malian government and international donors for substantial resources is required long into the future, or alternative strategies need to be found, if control of schistosomiasis transmission in Mali is to be achieved. Supporting Information Checklist S1 STROBE Checklist (0.08 MB DOC) Click here for additional data file. Text S1 Statistical notation of Bayesian geostatistical models for prevalence of Schistosoma haematobium and S. mansoni in 1984–1989 and 2004–2006. (0.03 MB DOC) Click here for additional data file. Text S2 Statistical notation of Bayesian geostatistical models of prevalence of Schistosoma haematobium and S. mansoni for investigating stationarity of spatial dependence and consistency of covariate effects across 1984–1989 and 2004–2006. (0.07 MB DOC) Click here for additional data file.
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              S. haematobium as a Common Cause of Genital Morbidity in Girls: A Cross-sectional Study of Children in South Africa

              Introduction Urogenital schistosomiasis causes gynecological morbidity in adult women [1], [2]. Schistosoma (S.) haematobium is primarily known for its effect on the urinary tract, but in endemic areas schistosomiasis may be the most common cause of genital morbidity and mucosal lesions [3]. An estimated 390 million females are at risk of schistosomiasis infection [4], [5]. It is second only to malaria in terms of public health impact of the parasitic diseases, with more than 100 million females infected, 85% of them live in rural parts of Africa. Previous studies on urogenital schistosomiasis have been conducted in adult women of childbearing age. S. haematobium ova when deposited in the female reproductive tract seem to be equally distributed in the different genital parts, but are most commonly identified in the cervix and the vagina [6], [7], [8], [9]. Both viable and dead ova may cause tissue reactions, morbidity and symptoms long after contact with infested waters [10], [11]. The disease may manifest itself in both the genital and urinary tract and may be found exclusively in the genitals [6], [12]. In young girls there have only been a few case reports, hypothesizing that the pre-pubertal predilection site is in the vulva [7], [8], [9], [13], [14]. This may partly be because gynecological inspections are not prioritized in rural areas, controversial in virgins, but also because the causal relationship between schistosomiasis and genital lesions in young females has not been explored on a large scale [14], [15]. It has been hypothesized that female genital schistosomiasis poses an increased risk to secondary infections such as human papillomavirus and other STDs. Most importantly, these women have been found to have significantly more HIV [16], [17], [18], [19]. In the wake of the HIV epidemic and a realistic prospect of successful anti-schistosomal mass-treatment programs this study sought to explore if girls before sexual debut had signs of genital disease [20]. In Ugu District, South Africa the study aimed to explore the association between gynecologic symptoms and urinary S. haematobium in young girls. Methods Study design and participants We carried out a cross-sectional study in 18 primary schools, which were randomized for inclusion from 309 primary schools in the area. We invited all girls aged 10–12 years in the included schools. Girls who were absent on the days of the invitations were excluded, as were girls with serious illnesses, or if their guardians or they refused. Setting The schools were visited between September 2009 and November 2010 in the predominantly rural Ugu District, KwaZulu Natal, South Africa, an S. haematobium endemic area, which covers 5866 km2 (Figure 1). It has an estimated population of 700 000 almost exclusively isiZulu speaking people, 84% reside in the rural areas, 51% are below the age of 20 years and 55% are female [21]. 10.1371/journal.pntd.0002104.g001 Figure 1 Map of Ugu district in South Africa. The coastal areas are inhabited by the more affluent and the schools here were excluded. Ethical considerations The study was approved by The Biomedical Research Ethics Administration, UKZN 2009, Ref BF029/07; by the Department of Health, Pietermaritzburg, 2009, Ref HRKM010 - 08; by the Norwegian ethics committee, Ref 469 - 07066a1.2007.535, 2007, and the Departments of Health (2008) and Education (2009) in Ugu District. The Helsinki Declaration was followed. All members of the group, including students and research assistants had passed exams in Good Clinical Practice and signed Declarations of Confidentiality. The interviewers did not know the study subjects beforehand. Prior to the study there were information meetings for the parents, principals, school governing bodies and/or teachers of each school. Informed consent was given by each girl, and the parents/guardians signed consent forms. Identifying information was stored separate from the interview information (in separate towns). All were informed about their right to withdraw and to abstain from answering questions without negative consequences. In order to protect girls from stigmatization the disease was discussed in general terms as urinary schistosomiasis. Treatment for schistosomiasis was offered to all, and all were informed about possible side effects. A private psychologist was hired by the project to take care of referred cases as felt necessary; for psychological, practical and legal issues. When other medical help was required, the girls were referred to a government clinical facility, or offered private care if government services were unavailable. For ethical and community liaison reasons the project staff was not involved in any physical or psychological examinations after referral. The interview The interviews (30 minutes duration) were conducted face to face in isiZulu (the local language) by trained female fieldworkers. Questions were asked about recent (the last week) or previous urogenital symptoms of itch, burn, ulcers or tumors (swelling, lumps) in the genitals, malodorous discharge, color of discharge and feeling of a burning sensation in the genitals, as well as red urine, dysuria, urge and stress incontinence [1], [2]. They were also asked questions about confounders for bloody discharge (menstruation and red urine), malodorous discharge (sexual intercourse and sexual abuse) and for burning sensations in the genitals (sexual intercourse and dysuria). The girls in a pilot study (same age) had no concept of the local anatomy and we therefore decided to not include questions on exact localization of e.g. tumors. In case the child did not seem to understand, terms were explained and if she seemed too shy/uncomfortable to answer the interviewers were instructed to move to the next question. The discharge color was defined using a custom-made color chart. The study population was not familiar with the details of the questionnaire on beforehand. A water body was defined as a river, dam, lake, stream or pond. Each child was asked if she carried out any of seven specific water-related activities known in the study area (playing/swimming, washing/bathing, laundry, washing blankets, collecting water, fishing and crossing) [22]. Furthermore, there were validated demographic, social and psychological components in the questionnaire that will not be analyzed here. Parasitology The researchers aimed to visit each school at least three times. We obtained urine samples from each girl on three consecutive days between 10 am and 2 pm. After gentle tilting we deposited 10 ml of urine into a container with 1 ml methylate-formalin solution and the same week we investigated the specimens by microscopy [23]. After centrifuging, we transferred all of the precipitate onto microscopy slides; the last amount was washed with water before transferred. If the mean number of eggs of the three specimens was higher than 50 per 10 ml urine the infection was classified as high-intensity [24]. One stool sample per child was collected for Kato Katz and analyzed for Ascaris lumbricoides, Ancylostoma duodenale, Taenia solium, Trichuris trichiura and S. mansoni. If there was at least one ovum in a specimen it was defined as positive. Data management and statistical analyses Based on data from studies in adults we estimated the prevalence of genital schistosomiasis to be 30% and urinary schistosomiasis 40% [3]. We hypothesized that the expected prevalence of genital ulcers in the schistosomiasis exposed to be 9% and in the unexposed 4%. To detect a difference with a significance level of 5% and a power of 80%, the sample size would have to be 511 unexposed and 341 schistosomiasis exposed young women, in all 852 subjects. The information was recorded on paper; the personal information sheet was separated from the other information as soon as the record number had been secured. Data was entered into EpiData (interview) or Excel (urine) and subsequently exported into IBM SPSS version 19 (Chicago, Illinois, USA). Chi-square and odds ratios (OR) with 95% confidence interval (CI) were used to compare impacts of water contact or current urinary schistosomiasis infection on genital symptoms. In order to study the impact of other variables (for example menstruation or red urine), logistic regression analysis was applied with a 5% significance level; variables were included if the P-value in the crude association was less than 0.2 and if the Spearman rank correlation coefficient was below 0.7. When there were less than 10 cases, the variable was not included in regression analysis. The statistical analysis was computed using SPSS. Results Characteristics of the study group Schools that were randomized for inclusion were visited in no particular order. We invited all pupils aged 10 to 12 years. All schools were visited several times in order to collect guardian consent forms and to find as many students as possible. The parents of 1241/1948 (64%) pupils in 18 schools provided consent. On the days we were in the schools we were able to include 1057 assenting girls. In the first 13 schools, where there was adequate time before exams, the consent forms were returned and signed by 92% (1109/1201) of the parents. The pupils were recruited from grades 1 to 7, median grade 5. Menstruation, HIV testing and sexual history Answers were recorded as missing if the child chose not to answer or did not understand the topics. Seven percent of the girls (71/1019) had started menstruating. Only 5% (51/981) said they had been tested for HIV. Three out of 980 (0.3%) knew they had HIV, as many as 495 said they did not know and 77 girls did not reply to this question. Less than one percent (7/1017) reported to have had intra-vaginal sex. However, two percent (22/953) reported to have been sexually abused. They were referred to psychosocial follow-up. All in all 24 of 1019 young girls had experienced voluntary or involuntary vaginal sex. Parasitology Out of the 1057 girls who were interviewed, 970 submitted at least one urine for examination, and out of these 791 submitted three urine samples. S. haematobium eggs were found in 32% (312/970) of the girls. High-intensity urinary infection was found in 28% (88/312) of these. In those who had ova in the urine, the mean intensity of infection was 52 eggs/10 ml urine (range 1–624/10 ml). Among the 658 girls with negative urine specimens, 79% (522) submitted three negative urine samples. There was neither any difference in urinary schistosomiasis infection intensity nor presence of symptoms between those who had submitted three urine samples versus one sample. Symptoms Thirty five percent reported to have had genital symptoms (356/1018), and as many as 17% (172/1008) reported genital symptoms the last week. Eighteen girls reported having a genital tumor or an ulcer this last week. Table 1 shows the association between urinary schistosomiasis and symptoms in girls. Controlled for confounders in multivariate analyses the table shows that urinary schistosomiasis remained associated with bloody discharge, a burning sensation in the genitals, genital ulcers, tumors and incontinence. Having had vaginal sex was not significantly associated with any of the symptoms; however the variable ‘vaginal sex’ was forced into the multivariate analyses. It did not influence the association between the symptoms and urinary schistosomiasis or water contact. Likewise, having soil-transmitted helminths did not influence the associations (data not shown) and only one person had S. mansoni. The discharge color was white in 51% (84/164) of the cases; cream color in 35% (58/164) and yellow in 9% (14/164). The discharge had streaks/traces of red in 13% (11/87) of the cases, light red in 66% (57/87) of the cases and an even lighter shade of red (light pink) in 9% (8/87). Patients with symptoms were referred but not investigated by the project. 10.1371/journal.pntd.0002104.t001 Table 1 Association between the urogenital symptoms in rural 10–12 year old girls and urinary schistosomiasis. Symptoms and frequencies In 298 urinary schistosomiasis positivea (%) In 628 urinary schistosomiasis negative (%) OR (95% CI)b P Adj. OR (95%CI)c P Bloody discharge Never 247 (83) 608 (97) 1.0 1.0 Sometimes 32 (11) 13 (2) 6.1(3.1–11.7) <0.001 4.2 (2.1–8.5) <0.001 Always 19 (6) 7 (1) 6.7 (2.8–16.1) <0.001 3.3 (1.3–8.5) 0.01 Red urined Never 207 (69) 568 (90) 1.0 1 Sometimes 37 (12) 34 (5) 2.9 (1.8–4.9) <0.001 2.4 (1.5–4.1) 0.001 This week 54 (18) 26 (4) 5.7 (3.5–9.3) <0.001 4.3 (2.5–7.2) <0.001 Malodorous discharge Never 254 (85) 565 (90) 1.0 1.0 Sometimes 20 (7) 39 (6) 1.1 (0.7–2.0) 0.64 1.1 (0.6–2.0) 0.69** Always 24 (8) 24 (4) 2.2 (1.2–4.0) 0.007 2.2 (1.2–4.0) 0.008 Genital itch Never 237 (80) 524 (83) 1.0 1.0 Sometimes 39 (13) 62 (10) 1.4 (0.9–2.1) 0.13 1.4 (0.9–2.2) 0.12** This week 22 (7) 42 (7) 1.2 (0.7–2.0) 0.59 1.2 (0.7–2.0) 0.57** Burning sensation in the genitals Never 243 (82) 561 (89) 1.0 1.0 Sometimes 34 (11) 43 (7) 1.8 (1.1–2.9) 0.01 1.6 (1.0–2.6) 0.05 This week 21 (7) 24 (4) 2.0 (1.1–3.7) 0.02 1.9 (1.0–3.5) 0.05 Dysuria Never 220 (74) 516 (82) 1.0 1.0 Sometimes 55 (18) 75 (12) 1.7 (1.2–2.5) 0.005 1.6 (1.1–2.3) 0.02 This week 23 (8) 37 (6) 1.5 (0.9–2.5) 0.17 1.3 (0.7–2.2) 0.42** Genital ulcer Never 266 (89) 598 (95) 1.0 1.0 Sometimes 27 (9) 25 (4) 2.4 (1.4–4.3) 0.002 2.4 (1.4–4.3) 0.002 This week 5 (2) 5 (1) 2.2 (0.6–7.8) 0.20 2.2 (0.6–7.7) 0.21** Genital tumor Never 283 (95) 613 (98) 1.0 1.0 Sometimes 12 (4) 13 (2) 2.0 (0.9–4.4) 0.09 2.0 (0.9–4.4) 0.09 This week 3 (1) 2 (0) 3.2 (0.5–19.6) 0.19 3.3 (0.5–19.6) 0.19** Urge incontinence Never 184 (62) 443 (71) 1.0 1.0 Sometimes 77 (26) 109 (17) 1.7 (1.2–2.4) 0.002 1.7 (1.2–2.4) 0.002 This week 37 (12) 76 (12) 1.2 (0.8–1.8) 0.47 1.2 (0.8–1.8) 0.44** Stress incontinence Never 212 (71) 530 (84) 1.0 1.0 Sometimes 44 (15) 66 (11) 1.7 (1.1–2.5) 0.02 1.7 (1.1–2.5) 0.02 This week 42 (14) 32 (5) 3.3 (2.0–5.3) <0.001 3.3 (2.0–5.3) <0.001 Eight separate multivariate analyses. Age was forced into each model and did not influence the results (data not shown). a The presence of at least one schistosome ova in any of the urine examined specimens. b Odds ratio (OR) with 95% confidence interval (CI). c Adjusted odds ratio, different confounding variables were included in each multivariate analysis for the specific genital symptom. d Red urine as seen by the child. ** If recalculated as ‘ever had the symptom’ it is significantly associated with urinary schistosomiasis. Nuances in urinary schistosomiasis negative and positive groups In order to explore the urinary negative girls in more detail they were first divided into two groups (Figure 2), those in high-endemic school versus those in low endemic. The pupils from the low-endemic schools were further split into two, those who admitted water body contact and those who did not. Figure 2 shows that symptoms are significantly more common in those that have a high intensity of infection. The figure also highlights that low-endemic schools (the ‘most negative’ in the district) have a low prevalence of genital symptoms. Amongst the girls with high-intensity schistosomiasis almost 50% had genital symptoms, compared to less than 5% in the negative girls who lived in non-endemic areas and had no water contact. These girls denied having genital tumors, ulcers, bloody or smelly discharge. Bloody discharge was found in the high-endemic schools only, notably also in those individuals of these schools who were negative for schistosomiasis in three urines (Figure 1, category III). Urge incontinence and genital itch were both relatively constant in the high-endemic schools, but significantly higher than in the low-endemic schools. 10.1371/journal.pntd.0002104.g002 Figure 2 Genital and urinary symptoms in girls of two S. haematobium positive groups and three negative risk groups. aLikelihood ratio. bThree urines investigated for S. haematobium ova, all were negative. cMore than 50 S. haematobium ova per 10 ml urine. d1–49 ova per 10 ml urine. eThese girls have water body contact (e.g. river, dam or lake). fThese girls deny water body contact. History of water contact We found a significant association between water contact and all the listed symptoms (data not shown). However, among the 364 who denied water contact, 21% (76) had urinary schistosomiasis. Sixty three percent of the girls reported water contact (667/1057), among these 606 submitted urines and 39% (236/606) had S. haematobium ova in urine. Among the girls with three negative urines, 54% (281/522) reported water contact. These reported significantly more genital symptoms the last week than their peers without water contact (p = 0.001). Prior urinary infection with S. haematobium and symptoms Twenty two percent of the young girls (226/1020) reported having had urinary schistosomiasis previously. This was significantly associated with current genital symptoms such as bloody discharge (Chi square, p<0.001), malodorous discharge (p<0.001), genital itch (p<0.017) and genital ulcer (p = 0.001). Furthermore 29% (251/853) knew of a family member who had had urinary schistosomiasis or red urine and these factors too were associated with all the queried symptoms (p≤0.002), except genital tumor (p = 0.08). Twelve percent (129/1057) reported that they had been treated for schistosomiasis previously. Girls who said that they had not been treated had significantly more urinary schistosomiasis than those who had been treated (p<0.001), but not more symptoms (sample size small and p-values range from p = 0.43 to p = 1.0). Discussion In an S. haematobium endemic area girls aged 10 to 12 years with schistosomiasis had significantly more often unpleasant symptoms such as genital ulcers, bloody discharge, malodorous white to yellow cultured discharge, genital itch or tumors than those without this infection. Even before sexual debut and independent of menstruation more than 40% of girls with S. haematobium ova in urine reported having had gynecological symptoms previously, one third reported having it the last week or ‘always’. Girls living in endemic areas without urinary schistosomiasis also had significantly more genital symptoms than their peers in low-endemic schools. This study shows that urinary schistosomiasis, water contact, history of red urine and family history of schistosomiasis (‘Isichenene’ in Zulu) are also associated with the full range of symptoms. As shown in adults previously, the history of water contact was an excellent predictor for genital symptoms also in girls [3], [7], [8], [9], [25]. The girls who had been treated for schistosomiasis previously had the same symptoms as those who denied having received treatment. In adults the grainy sandy patches have been found to be diagnostic of S. haematobium infection and are significantly associated with discharge [1], [2], [3]. However, the findings in this young population could not be corroborated by a clinical examination. These results are therefore circumstantial, since the gynecological symptoms are not specific for genital schistosomiasis. Without the physical examination and intravaginal tests we cannot confirm schistosomiasis as the etiological factor. Furthermore, vaginal discharge, ulcers and genital itch may have other causes that were not controlled for in this study, such as the sexually transmitted diseases, atopic, irrigative dermatitis or other dermatoses like psoriasis or lichen sclerosis, lice, scabies, or non-specific etiology [26], [27]. Furthermore, one cannot preclude that the current symptoms, although caused by infection with S. haematobium in the lower genital tract may make the genital mucosa more susceptible to super-infections by other agents such as bacterial infections, which in turn may cause the reported symptoms. Likewise, the association between water contact and symptoms may be influenced by social and other practices. Poor perinea hygiene may be more common in a group that has limited access to water; and cultural cleansing rituals may also be hypothetical reasons for the association between water contact and symptoms [28]. In this study three urines were collected and the presence of schistosome ova defined the urinary schistosomiasis positive group. However, this study confirms that even urinary negative cases in endemic areas have gynecological morbidity. Hence the prevalence is most likely higher in this population. A more sensitive diagnostic method, such as antigen detection or PCR, would likely have made the reported finding more apparent, though this was not possible in our study [29]. Some girls denied having had water contact, but were found to still have S. haematobium ova in urine. Girls may be shy, worried about repercussions or not be able to differentiate between urinary and genital symptoms. Some girls were ignorant of some phenomena in the questionnaire such as menstruation or discharge. The interviewers – all female – were trained to explain the differences, however information sessions using dolls followed by more thorough questioning of genital symptoms could perhaps have produced more reliable answers and less under-reporting. This was not done in the present study. Further, the most reliable method to determine water contact is by direct observation, although many schistosomiasis studies have used self-reported water body data [30], [31]. It is well documented that many adult women may have genital schistosomiasis even without having detectable schistosome ova in the urine [3], [12], [32]. Urine investigations may therefore be of limited use in the diagnosis of genital schistosomiasis. Studies have shown that female genital schistosomiasis may cause pathologic blood vessel morphology and fragile blood vessels that may lead to mucosal bleeding [3], [33], [34]. Bloody discharge may be a result of this. Inter-menstrual bleeding, post-coital bleeding, malodorous and abnormally colored discharge and genital itch have been found to be associated with S. haematobium ova in the genitals of adults, even after correcting for sexually transmitted diseases [1], [19], [25], [34]. The girls in this study report the same symptoms as adult women in previous studies [1], [2]. One may fear that young children's genital mucosa are already imbued with calcified S. haematobium ova [16], [25], [35]. Childhood water contact may start very early and these girls may have had S. haematobium infection for several years [36]. One study found that such lesions were refractory to treatment in adults, whereas treatment received before the age of 20 years seemed to offer some protection against genital mucosal pathology [25]. Even so, the morbidity prevalence levels were unacceptably high even in those who had received treatment once in childhood and treatment may have to be given in infanthood in order to prevent genital damage [36]. Furthermore, siblings and people sharing the same water bodies should be given simultaneous treatment in order to reduce re-infection rate and intensity; treatment should be given in low-transmission seasons, and the effect should be secured by several rounds [37]. At the present time there are no suitable tools for the diagnosis of genital schistosomiasis in girls. Abnormal malodorous or bloody genital discharge are mucosal symptoms [2]. In our young study population gynecological investigations were not possible for cultural and technical reasons [3]. Further studies are needed to triangulate the analyses of (1) symptoms and (2) water contact/family history with (3) the objective mucosal findings in genital schistosomiasis. For the rural clinician history taking and urine analyses are simpler sources of information than gynecological examinations, and especially so in virgins. The findings in this study suggest that young girls in S. haematobium endemic areas have gynecological symptoms as a result of schistosoma infection. Mucosal damages may be present as these young girls enter into their first sexual relationships, making them particularly susceptible to HIV or human papillomavirus infection [16], [17], [36]. Further studies are needed to explore the effects of treatment on the prolific symptomatic manifestations and on decreasing the susceptibility to super-infections before sexual debut. Supporting Information Checklist S1 Enclosed is a Strobe checklist for cross-sectional studies. (DOC) Click here for additional data file.
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                Author and article information

                Contributors
                Journal
                Heliyon
                Heliyon
                Heliyon
                Elsevier
                2405-8440
                14 November 2017
                November 2017
                14 November 2017
                : 3
                : 11
                : e00440
                Affiliations
                [a ]Department of Infectious Diseases, Aarhus University Hospital, Denmark
                [b ]Center for Global Health (GloHAU), Department of Public Health, Aarhus University, Denmark
                [c ]Section for Parasitology and Aquatic Diseases, SUND, University of Copenhagen, Denmark
                [d ]Department of Pathology, University of Cambridge, United Kingdom
                [e ]Laboratory of Parasitology, Institut National de Recerche en Sante Publique, Bamako, Mali
                [f ]University of Sciences, Techniques and Technology, Bamako, Mali
                [g ]Department of Infectious Diseases, The Royal Hospital, P.O. Box 1331, Muscat, Oman
                [h ]Institute for Clinical Medicine, University of Aarhus, Denmark
                Author notes
                [* ]Corresponding author. chalstec@ 123456rm.dk
                Article
                S2405-8440(17)30096-8 e00440
                10.1016/j.heliyon.2017.e00440
                5727379
                f9ad33a3-f3e3-4818-b53b-caad212d5ec0
                © 2017 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 16 January 2017
                : 2 September 2017
                : 25 October 2017
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                infectious disease
                infectious disease

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