Equations 5 and 6 emphasize that environmental and socioeconomic aspects contribute to the presence of the *B. straminea* species. It is observed that the smaller the *SanRiverLake*, *DEM* and *PCW* variables, the larger is the presence of *B. straminea*, while the *WithoutSan* and *PCS*

The models selected, for each one of the snail species, were analyzed to verify if they presented a good adjustment, through Receiver Operating Characteristic (ROC) or ROC curve. ROC curve is a measure that confronts mistakes with successes and successes with

The result of ROC curve of the models selected for each one of the snail species is presented

Based on Figure 4, sensitivity and specificity are statistical measures of the performance used for the logistic regression test. Sensitivity measures the proportion of actual positives which are correctly identified (the percentage of municipalities where the *Biomphalaria* species exist and are identified as *Biomphalaria*), and specificity measures the proportion of negatives which are correctly identified (the percentage of municipalities where the *Biomphalaria* species exist and are not identified as *Biomphalaria*). According to the Figure 4 it

Fig. 4. ROC curve of the models selected for each one of the *Biomphalaria* species: (a) ROC curve of the *B. glabrat*a species, (b) ROC curve of the *B. tenagophila* species, (c) ROC curve of

mistakes. The larger the area below the curve, the better is the model adjustment.

^ exp Prob

consequence, regions where several classes may occur, more transitions have been found and, therefore, higher uncertainties.

#### **3.2 Logistic regression**

Unlike indicator kriging, logistic regression estimates the occurrence probability for each species separately. Variables that represent vegetation, temperature, precipitation and topography, were found in the logistic regression models, giving an indication of important conditions for the mollusc's development.

The first model generated by logistic regression, for *B. glabrata* species, has five variables and is presented in Equations 1 and 2.

$$\mathbf{Prob}\_{RG}^{\wedge} = \frac{\exp^{\chi b}}{1 + \exp^{\chi b}} = \frac{G}{1 + G} \tag{1}$$

where ^ Prob*BG* is the estimated probability of existence of the *B. glabrata* species in Minas Gerais state, *b* are estimated parameters, *X* are the explanatory variables and *G* is given by Equation 2.

$$G = \exp(1.837 \text{ - } 0.003 \text{(DEM)} + 11.175 \text{(NDVI}\_W) \text{ - } 0.121 \text{(PC}\_W) + 0.015 \text{(PC}\_S) \text{ - } 0.390 \text{(TN}\_W)) \text{ (2)}$$

where *DEM* is the digital elevation model, *NDVIW* is the winter normalized vegetation index, *PCW* is the mean winter precipitation and *TNW* is the winter minimum temperature.

It is observed that the *DEM* variable possesses an inverse relationship with the presence of the *B. glabrata* species; in other words, the lower the digital elevation model, the larger is the presence of *B. glabrata*. The *PCW* and *TNW* (winter minimum temperature) variables also presented an inverse relationship with the presence of the *B. glabrata* species. It is noticed that the larger the vegetation index (*NDVI*) and precipitation in summer, the larger is the presence of the *B. glabrata* species. This can be explained by the natural conditions of the habitat of the mollusc that lives close to vegetation and water, in search of protection against solar radiation and high temperatures.

The second model generated by logistic regression, for *B. tenagophila* species, has four variables and it is presented in Equations 3 and 4

$$\mathbf{Prob}\_{\mathcal{B}T}^{\wedge} = \frac{\exp^{\mathcal{X}b}}{1 + \exp^{\mathcal{X}b}} = \frac{T}{1 + T} \tag{3}$$

where ^ Prob*BT* is the probability estimated of the existence of the *B. tenagophila* species in Minas Gerais state, b are parameters estimated, X are the explanatory variables and *T* is presented in Equation 4.

$$\begin{aligned} T &= \exp\{21.887 \text{ - } 0.003 \text{(DEM) - } 0.041 \text{(Shad}\_{\text{S}}\text{)}\\ &- 23.723 \text{(MIR}\_{\text{W}}\text{) - } 0.543 \text{(TX}\_{\text{S}}\text{)}\} \end{aligned} \tag{4}$$

According to Equations 3 and 4, an indirect relationship in all the variables can be noticed. This coincides with the ideal conditions for the snail's development, as the topography of low elevated defined by *DEM*, the water concentration or low topographical variation that

consequence, regions where several classes may occur, more transitions have been found

Unlike indicator kriging, logistic regression estimates the occurrence probability for each species separately. Variables that represent vegetation, temperature, precipitation and topography, were found in the logistic regression models, giving an indication of important

The first model generated by logistic regression, for *B. glabrata* species, has five variables and

*BG Xb*

Gerais state, *b* are estimated parameters, *X* are the explanatory variables and *G* is given by

*G* exp(1.837 - 0.003( ) + 11.173( ) - 0.121( ) + 0.015( ) - 0.390( )) *DEM NDVI PC PC TN WW SW* (2)

where *DEM* is the digital elevation model, *NDVIW* is the winter normalized vegetation index, *PCW* is the mean winter precipitation and *TNW* is the winter minimum temperature. It is observed that the *DEM* variable possesses an inverse relationship with the presence of the *B. glabrata* species; in other words, the lower the digital elevation model, the larger is the presence of *B. glabrata*. The *PCW* and *TNW* (winter minimum temperature) variables also presented an inverse relationship with the presence of the *B. glabrata* species. It is noticed that the larger the vegetation index (*NDVI*) and precipitation in summer, the larger is the presence of the *B. glabrata* species. This can be explained by the natural conditions of the habitat of the mollusc that lives close to vegetation and water, in search of protection against

The second model generated by logistic regression, for *B. tenagophila* species, has four

*BT Xb*

Minas Gerais state, b are parameters estimated, X are the explanatory variables and *T* is

exp(21.887 - 0.003( ) - 0.041( ) - 23.723( ) - 0.543( ))

According to Equations 3 and 4, an indirect relationship in all the variables can be noticed. This coincides with the ideal conditions for the snail's development, as the topography of low elevated defined by *DEM*, the water concentration or low topographical variation that

*MIR TX*

*T DEM Shad*

1 exp 1 *Xb*

Prob*BT* is the probability estimated of the existence of the *B. tenagophila* species in

*W S*

*T*

*T* (3)

*S*

(4)

^ exp Prob

1 exp 1 *Xb*

Prob*BG* is the estimated probability of existence of the *B. glabrata* species in Minas

*G*

*G* (1)

^ exp Prob

and, therefore, higher uncertainties.

conditions for the mollusc's development.

is presented in Equations 1 and 2.

solar radiation and high temperatures.

variables and it is presented in Equations 3 and 4

**3.2 Logistic regression**

where ^

Equation 2.

where ^

presented in Equation 4.

can be associated to the *ShadS* variable, the humidity of the plants defined by the *MIRW* and the maximum temperature average for summer seasons associated to the *TXS* variable. Equations 5 and 6 present the model generated by logistic regression for *B. straminea* species and five variables

$$\text{Prob}\_{RS}^{\wedge} = \frac{\exp^{Xb}}{1 + \exp^{Xb}} = \frac{S}{1 + S} \tag{5}$$

where ^ Prob*BS* is the probability estimated of the existence of the *B. straminea* species in Minas Gerais state, b are parameters estimated, *X* are the explanatory variables and *S* is presented in Equation 6

$$\begin{aligned} S &= \exp(0.351 \text{ - } 0.081 \text{(SanRiverLake)} + 0.020 \text{(Vithout} \text{San)} \\ &- 0.002 \text{(DEM)} \text{ - } 0.076 \text{(PC}\_{\text{IV}}) + 0.013 \text{(PC}\_{\text{S}})) \end{aligned} \tag{6}$$

Equations 5 and 6 emphasize that environmental and socioeconomic aspects contribute to the presence of the *B. straminea* species. It is observed that the smaller the *SanRiverLake*, *DEM* and *PCW* variables, the larger is the presence of *B. straminea*, while the *WithoutSan* and *PCS* variables possess a direct relationship.

The models selected, for each one of the snail species, were analyzed to verify if they presented a good adjustment, through Receiver Operating Characteristic (ROC) or ROC curve. ROC curve is a measure that confronts mistakes with successes and successes with mistakes. The larger the area below the curve, the better is the model adjustment.

The result of ROC curve of the models selected for each one of the snail species is presented in Figure 4.

Based on Figure 4, sensitivity and specificity are statistical measures of the performance used for the logistic regression test. Sensitivity measures the proportion of actual positives which are correctly identified (the percentage of municipalities where the *Biomphalaria* species exist and are identified as *Biomphalaria*), and specificity measures the proportion of negatives which are correctly identified (the percentage of municipalities where the *Biomphalaria* species exist and are not identified as *Biomphalaria*). According to the Figure 4 it

Fig. 4. ROC curve of the models selected for each one of the *Biomphalaria* species: (a) ROC curve of the *B. glabrat*a species, (b) ROC curve of the *B. tenagophila* species, (c) ROC curve of the *B. straminea* species.

A Study of Schistosomiasis Prevalence and

Risk of Snail Presence Spatial Distributions Using Geo-Statistical Tools 269

Fig. 5. Probability estimated of *B. glabrata* (a), *B. tenagophila* (c) and *B. straminea* (e) using logistic regression; and the estimate of *B. glabrata* (b), *B. tenagophila* (d) and *B. straminea* (f)

using indicator kriging.

is observed that the model of the *B. glabrata* species presented the best adjustment, with 98% of area below the ROC curve. The *B. tenagophila* and *B. straminea* species obtained 73% and 82%, respectively. Based on the models selected, the probabilities of presence of *Biomphalaria* species were estimated for the whole Minas Gerais state.

Figure 5 presents the probability of the presence of *B. glabrata* (Figure 5a), *B. tenagophila* (Figure 5c) and *B. straminea* (Figure 5e) species using logistic regression. Additionally, Figure 5 shows the estimated presence of *B. glabrata* (Figure 5b), *B. tenagophila* (Figure 5d) and *B. straminea* (Figure 5f) species using indicator kriging which is obtained from section 3.1 results by merging the probabilities of the joint classes.

The estimative of the presence of each species was correlated with the historical data of schistosomiasis prevalence (*Pv*), with the intention of determining which of the *Biomphalaria* species is more related with the disease prevalence (Table 1). The values in highlights (\*) are the estimates of the species that possess significant correlation on a 95% confidence level (p<0.05).

Table 1. Correlation of the estimative of *Biomphalaria* species with the historical data of schistosomiasis prevalence.

Based on Table 1, *B. glabrata* is the species of *Biomphalaria* that has more correlation with the disease prevalence. This result is confirmed by the study of Lutz (1917), which affirms the distribution of *B. glabrata* is more associated to the occurrence of the schistosomiasis in Brazil.

#### **3.3 Multiple regression**

The purpose of this section is to use the *Biomphalaria* presence probabilities, as estimated by logistic regressions, as explanatory variables together with the environmental variables.

The analysis of the correlation matrix showed that some variables had non-significative correlations with the prevalence of schistosomiasis (*Pv*) at a 95% confidence level and some variables were highly correlated among themselves, indicating that the model could be further simplified. Variable selection was performed by the R2 criterion using all possible regressions (Neter et al., 1996).

#### **3.3.1 Global model**

The four variables selected were: probability estimated of *B. glabrata* using logistic regression (*LRG*), winter precipitation (*PCW*), summer minimum temperature (*TNS*) and human development index (*HDI91*) for the year 1991.

The final model, with R2 = 0.28, was

$$\stackrel{\wedge}{P}\mathcal{V} = e^{(-4.22 + 1.76LR\_{\odot} + 0.03PC\_{W} + 0.35TN\_{S} - 3.28HDI\_{\mathfrak{H}})} - \mathbf{1} \tag{7}$$

is observed that the model of the *B. glabrata* species presented the best adjustment, with 98% of area below the ROC curve. The *B. tenagophila* and *B. straminea* species obtained 73% and 82%, respectively. Based on the models selected, the probabilities of presence of *Biomphalaria*

Figure 5 presents the probability of the presence of *B. glabrata* (Figure 5a), *B. tenagophila* (Figure 5c) and *B. straminea* (Figure 5e) species using logistic regression. Additionally, Figure 5 shows the estimated presence of *B. glabrata* (Figure 5b), *B. tenagophila* (Figure 5d) and *B. straminea* (Figure 5f) species using indicator kriging which is obtained from section

The estimative of the presence of each species was correlated with the historical data of schistosomiasis prevalence (*Pv*), with the intention of determining which of the *Biomphalaria* species is more related with the disease prevalence (Table 1). The values in highlights (\*) are the estimates of the species that possess significant correlation on a 95% confidence level

**Estimated species Logistic regression Kriging**

*B. glabrata* 0.18\* 0.15\*

*B. tenagophila* 0.07 -0.05 *B. straminea* -0.04 -0.10

Based on Table 1, *B. glabrata* is the species of *Biomphalaria* that has more correlation with the disease prevalence. This result is confirmed by the study of Lutz (1917), which affirms the distribution of *B. glabrata* is more associated to the occurrence of the schistosomiasis in

The purpose of this section is to use the *Biomphalaria* presence probabilities, as estimated by logistic regressions, as explanatory variables together with the environmental variables. The analysis of the correlation matrix showed that some variables had non-significative correlations with the prevalence of schistosomiasis (*Pv*) at a 95% confidence level and some variables were highly correlated among themselves, indicating that the model could be further simplified. Variable selection was performed by the R2 criterion using all possible

The four variables selected were: probability estimated of *B. glabrata* using logistic regression (*LRG*), winter precipitation (*PCW*), summer minimum temperature (*TNS*) and

> 91 ^ ( 4.22 1.76 0.03 0.35 3.28 ) *GW S* 1 *LR PC TN HDI Pv e* (7)

Table 1. Correlation of the estimative of *Biomphalaria* species with the historical data of

species were estimated for the whole Minas Gerais state.

3.1 results by merging the probabilities of the joint classes.

(p<0.05).

Brazil.

schistosomiasis prevalence.

**3.3 Multiple regression**

regressions (Neter et al., 1996).

The final model, with R2 = 0.28, was

human development index (*HDI91*) for the year 1991.

**3.3.1 Global model**

Fig. 5. Probability estimated of *B. glabrata* (a), *B. tenagophila* (c) and *B. straminea* (e) using logistic regression; and the estimate of *B. glabrata* (b), *B. tenagophila* (d) and *B. straminea* (f) using indicator kriging.

A Study of Schistosomiasis Prevalence and

education for the year of 2000).

Risk of Snail Presence Spatial Distributions Using Geo-Statistical Tools 271

2 *WSWW* 1 0.45 *NDVI TN TN TX Pv e R* (9)

3 *S S* 1 0.35 *DEM TN TX NHI Pv e R* (10)

4 *W* 1 0.51 *DEM TX HDIE C Pv e R* (11)

00

^ ( 6.86 4.08 1.31 0.57 0.36 ) 2

^ (11.44 0.002 0.47 0.64 1.79 ) 2

^ ( 8.54 0.003 0.24 3.64 0.03 ) 2

where: *LRT* (probability estimated of *B. tenagophila* using logistic regression), *DEC* (slope from SRTM), *NDVIS* (summer normalized difference vegetation index), *C* (percentage of housing with bathroom or toilet connected to a ditch), *NDVIW* (winter normalized difference vegetation index), *TNS* (summer minimum temperature), *TNW* (winter minimum temperature), *TXW* (winter maximum temperature), *DEM* (digital elevation model from SRTM), *TXS* (summer maximum temperature), *NHI* (need of health index), *HDIE00* (HDI

Fig. 7. (a) Regionalization; (b) Regional models - prevalence (%) 0.001–5.000 (green), 5.001–

15.000 (yellow) and above 15.001 (red); (c) Residuals.

Figure 6a shows the estimated *Pv* for all municipalities in the Minas Gerais state using the estimated regression equation (7). Figure 6b presents the plot of the residuals, resulting from the difference between observed (Figure 2a) and estimated *Pv* from 255 municipalities. In Figure 6b, dark colours (red and blue) represent overestimated values, light colours (red and blue) underestimated ones, and in white are the municipalities where the estimated prevalence differs very little from the true values.

Fig. 6. (a) Global model - prevalence (%) 0.001–5.000 (green), 5.001–15.000 (yellow) and above 15.001 (red); (b) Residuals models.

The precipitation, minimum temperature and estimate of *B. glabrata* were positively correlated with *Pv*. The human development index was negatively correlated with *Pv*. This is consistent with the adequate environmental conditions for the transmission of schistosomiasis. The transmission depends on the presence of *B. glabrata*, places with temperature above 15oC, hydric collections and low economic status.

The result of this model has the same variables (precipitation, temperature and HDI) obtained by Guimarães et al. (2006). The difference is that the variable obtained by Guimarães et al. (2006) related to the type of vegetation (forest, savannah and caatinga) and in this study to the presence of the *B. glabrata*. However, this variable (presence of the *B. glabrata*) had already been obtained by Guimarães et al. (2010b) using indicator kriging.

#### **3.3.2 Regional model**

The state of Minas Gerais was divided into four regions using the Skater algorithm. The result of the regionalization is shown in Figure 7a. Regression models were developed for each of the four regions with the same 255 variables used in the global model and the same selection procedure (123 cases for variables selection and model definition, and another with 132 cases for model validation). Different numbers of variables were selected in each region to determine the best regression model.

The final models generated for each region and their R2 were

$$\stackrel{\wedge}{P} \mathcal{D}\_1 = \mathcal{e}^{(8.29+13.14LR\_T - 0.12DEC - 10.95NDVI\_S - 0.16C)} - 1 \Longrightarrow \mathcal{R}^2 = 0.91 \tag{8}$$

Figure 6a shows the estimated *Pv* for all municipalities in the Minas Gerais state using the estimated regression equation (7). Figure 6b presents the plot of the residuals, resulting from the difference between observed (Figure 2a) and estimated *Pv* from 255 municipalities. In Figure 6b, dark colours (red and blue) represent overestimated values, light colours (red and blue) underestimated ones, and in white are the municipalities where the estimated

Fig. 6. (a) Global model - prevalence (%) 0.001–5.000 (green), 5.001–15.000 (yellow) and

temperature above 15oC, hydric collections and low economic status.

The precipitation, minimum temperature and estimate of *B. glabrata* were positively correlated with *Pv*. The human development index was negatively correlated with *Pv*. This is consistent with the adequate environmental conditions for the transmission of schistosomiasis. The transmission depends on the presence of *B. glabrata*, places with

The result of this model has the same variables (precipitation, temperature and HDI) obtained by Guimarães et al. (2006). The difference is that the variable obtained by Guimarães et al. (2006) related to the type of vegetation (forest, savannah and caatinga) and in this study to the presence of the *B. glabrata*. However, this variable (presence of the *B. glabrata*) had already been obtained by Guimarães et al. (2010b) using indicator kriging.

The state of Minas Gerais was divided into four regions using the Skater algorithm. The result of the regionalization is shown in Figure 7a. Regression models were developed for each of the four regions with the same 255 variables used in the global model and the same selection procedure (123 cases for variables selection and model definition, and another with 132 cases for model validation). Different numbers of variables were selected in each region

^ (8.29 13.14 0.12 10.95 0.16 ) 2

1 *T S* 1 0.91 *LR DEC NDVI C Pv e R* (8)

prevalence differs very little from the true values.

above 15.001 (red); (b) Residuals models.

to determine the best regression model.

The final models generated for each region and their R2 were

**3.3.2 Regional model**

$$\stackrel{\wedge}{P} \text{\textquotedblleft}\_2 = \text{\textquotedblleft}^{(-6.86 + 4.08\,\text{NDVI}\_{\text{IV}} + 1.31\,\text{TN}\_{\text{S}} - 0.57\,\text{TN}\_{\text{IV}} - 0.36\,\text{TX}\_{\text{W}})} - \mathbf{1} \Longrightarrow \text{R}^2 = \mathbf{0}.45\tag{9}$$

$$\stackrel{\wedge}{P}\text{\prime\prime}\_{3} = \epsilon^{\{11.44-0.002DEM+0.47TN\_S - 0.64TX\_S + 1.79NHI\}} - \mathbf{1} \Longrightarrow \mathbb{R}^2 = \mathbf{0.35} \tag{10}$$

$$\stackrel{\frown}{P\upsilon\_4} = e^{\{-8.54 + 0.003DEM + 0.24TX\_W + 3.64HD\bar{L}\_{00} + 0.03C\}} - \mathbf{1} \Rightarrow \mathbb{R}^2 = \mathbf{0.51} \tag{11}$$

where: *LRT* (probability estimated of *B. tenagophila* using logistic regression), *DEC* (slope from SRTM), *NDVIS* (summer normalized difference vegetation index), *C* (percentage of housing with bathroom or toilet connected to a ditch), *NDVIW* (winter normalized difference vegetation index), *TNS* (summer minimum temperature), *TNW* (winter minimum temperature), *TXW* (winter maximum temperature), *DEM* (digital elevation model from SRTM), *TXS* (summer maximum temperature), *NHI* (need of health index), *HDIE00* (HDI education for the year of 2000).

Fig. 7. (a) Regionalization; (b) Regional models - prevalence (%) 0.001–5.000 (green), 5.001– 15.000 (yellow) and above 15.001 (red); (c) Residuals.

A Study of Schistosomiasis Prevalence and

predictive models can also be improved.

occur.

disease.

**5. Acknowledgment**

**6. References**

Risk of Snail Presence Spatial Distributions Using Geo-Statistical Tools 273

The results of the regression models show that regionalization improves the estimation of the schistosomiasis prevalence in Minas Gerais state. Based on this model, a schistosomiasis risk map was built for Minas Gerais using an interpolator. Martins-Bedê et al. (2009) and

 To conclude, this study can contribute significantly to the choice of actions by the health decision makers, allowing them, on the one hand to narrow the set of municipalities in the state of Minas Gerais for which treatment and sanitation should be a priority, and on the other hand, to focus on preventive measures in the municipalities where transmission can

The use of GPS is recommended for field surveys to obtain the coordinates of the foci of *Biomphalaria*. Thus the results can be compared to the logistic regression and the accuracy of

The methodology used in this study can be utilized to control and to take preventive measures to prevent schistosomiasis transmission in the areas with occurrence of the

Sandra C Drummond (Secretary of Health in the state of Minas Gerais) and CNPq (grants

Almeida Machado P (1982). The Brazilian Program for Schistosomiasis Control. *Am J Trop*

Amaral RS, Tauil PL, Lima DD, Engels D (2006). An analysis of the impact of the

Araújo KCGM, Resendes APC, Souza-Santos R, Silveira Júnior JC, Barbosa CS (2007).

Baptista DF, Jurberg P (1993). Factors conditioning the habitat and the density of

Barbosa FS (1970). Epidemiologia. In: *Esquistossomose mansoni,* A. S. Cunha, pp. (31-60),

2000. *Cadernos de Saúde Pública*, Vol. 23, No. 2, pp. 409-417, 0102-311X. Assunção RM, Neves MC, Camara G, Freitas CC (2006). Efficient regionalization techniques

Baker FC (1945). *The molluscan family Planorbidae* University of Illinois, Illinois.

*Cruz*, Vol. 99, No. Suppl. I, pp. 79-83, 0074-0276.

Schistosomiasis Control Programme in Brazil. *Mem Inst Oswaldo Cruz*, Vol. 101, No.

Análise espacial dos focos de *Biomphalaria glabrata* e de casos humanos de esquistossomose mansônica em Porto de Galinhas, Pernambuco, Brasil, no ano

for socio-economic geographical units using minimum spanning trees. *International Journal of Geographical Information Science*, Vol. 20, No. 7, pp. 797-811, 1365-8816.

*Biomphalaria tenagophila* (Orbigny, 1835) in an isolated schistosomiasis focus in Rio de Janeiro city. *Mem Inst Oswaldo Cruz*, Vol. 88, No. 3, pp. 457-464, 0074-0276. Barbosa CS, Araújo KC, Antunes L, Favre T, Pieri OS (2004). Spatial distribution of

schistosomiasis foci on Itamaracá Island, Pernambuco, Brazil. *Mem Inst Oswaldo*

#300679/2011-4, 384571/2010-7, 302966/2009-9, 308253/2008-6).

*Med Hyg*, Vol. 31, No. 1, pp. 76-86, 0002-9637.

Suppl. I, pp. 79-85, 0074-0276.

Editora da U.S.P., São Paulo.

Guimarães et al. (2010b) also obtained a better model with the use of regionalization.

Figure 7b shows the estimated values of *Pv* for the whole state of Minas Gerais using equations (8, 9, 10 and 11). In addition, Figure 7c shows the residuals from 255 municipalities. In this figure, gray represents "no prevalence", dark colours represent overestimated values, light colours represent the underestimated values and in the white municipalities with good estimates.

The regional model for Region 1 (R1) reflects the effect of sanitation (housing with bathroom or toilet connected to a ditch), topography (*DEC*), vegetation (*NDVIS*) and the influence of molluscs (*B. tenagophila*). Region 1 achieved a R2 value of 0.91. The result obtained by Guimarães (2010) suggested it would be interesting to do a detailed study in the Sul Meso region, which is part of Region 1, to determine the prevalence and the transmitter of schistosomiasis.

The model for Region 2 (R2) shows the effect of vegetation (*NDVIw*) and temperature. The same relationship between temperature and vegetation was also obtained by Bavia et al. (2001) in Bahia state and Guimarães et al. (2006, 2010b) in Minas Gerais state. The R2 found for this model was 0.45.

The models for Regions 3 and 4 (R3 and R4) show that *Pv* was associated with topography (*DEM*), weather (temperature) and socioeconomic effects. Among the regional models, Region 3 had the lowest R2 (0.35). Region 4 had R2 of 0.51. The variables obtained in Regions 3 and 4 were the same variables found by Guimarães et al. (2010b) also in the same regions.

In all models (global and regional) the presence of *Biomphalaria*, socioeconomic effects, vegetation index and temperature were the most important variables. These characteristics are the same as environmental conditions for the presence and development of molluscs (infection of the intermediate host) and sanitation (water contamination - presence of *S. mansoni* cercariae) obtained by Guimarães et al. (2010b).

Bavia et al. (2001), in Bahia, showed that the distribution of schistosomiasis is related to the vegetation index and temperature using data from NOAA/AVHRR.

#### **4. Conclusion and further work**

The use of GIS, remote sensing, geostatistical and statistical techniques together proved to be quite suitable for the study of schistosomiasis.

This study explored the relationship between the schistosomiasis prevalence and the existing snail species in Minas Gerais state, Brazil (*B. glabrata*, *B. tenagophila*, *B.straminea*). The results showed that the snail species that has more correlation with the disease prevalence is *B. glabrata*.

The generated results showed that logistic regression or indicator kriging are consistent tools and the obtained map can be used as an auxiliary tool to formulate proper public health strategies and to guide fieldwork, considering the places with higher occurrence probability for occurrence of the most important species. Although not shown here, it is interesting to notice that among the models generated for regression logistic for the three *Biomphalaria* species, there was coincidence in the selection of variables. Variables that represent the vegetation, temperature, precipitation and topography were presented in the models, giving an indication of the important conditions for the mollusc's development.

Most of the selected explanatory variables in the global and regional models were related to environmental conditions for the presence and development of the mollusc, even as to the transmission of schistosomiasis.

The results of the regression models show that regionalization improves the estimation of the schistosomiasis prevalence in Minas Gerais state. Based on this model, a schistosomiasis risk map was built for Minas Gerais using an interpolator. Martins-Bedê et al. (2009) and Guimarães et al. (2010b) also obtained a better model with the use of regionalization.

 To conclude, this study can contribute significantly to the choice of actions by the health decision makers, allowing them, on the one hand to narrow the set of municipalities in the state of Minas Gerais for which treatment and sanitation should be a priority, and on the other hand, to focus on preventive measures in the municipalities where transmission can occur.

The use of GPS is recommended for field surveys to obtain the coordinates of the foci of *Biomphalaria*. Thus the results can be compared to the logistic regression and the accuracy of predictive models can also be improved.

The methodology used in this study can be utilized to control and to take preventive measures to prevent schistosomiasis transmission in the areas with occurrence of the disease.

#### **5. Acknowledgment**

Sandra C Drummond (Secretary of Health in the state of Minas Gerais) and CNPq (grants #300679/2011-4, 384571/2010-7, 302966/2009-9, 308253/2008-6).

#### **6. References**

272 Schistosomiasis

Figure 7b shows the estimated values of *Pv* for the whole state of Minas Gerais using equations (8, 9, 10 and 11). In addition, Figure 7c shows the residuals from 255 municipalities. In this figure, gray represents "no prevalence", dark colours represent overestimated values, light colours represent the underestimated values and in the white

The regional model for Region 1 (R1) reflects the effect of sanitation (housing with bathroom or toilet connected to a ditch), topography (*DEC*), vegetation (*NDVIS*) and the influence of molluscs (*B. tenagophila*). Region 1 achieved a R2 value of 0.91. The result obtained by Guimarães (2010) suggested it would be interesting to do a detailed study in the Sul Meso region, which is part of Region 1, to determine the prevalence and the transmitter of

The model for Region 2 (R2) shows the effect of vegetation (*NDVIw*) and temperature. The same relationship between temperature and vegetation was also obtained by Bavia et al. (2001) in Bahia state and Guimarães et al. (2006, 2010b) in Minas Gerais state. The R2 found

The models for Regions 3 and 4 (R3 and R4) show that *Pv* was associated with topography (*DEM*), weather (temperature) and socioeconomic effects. Among the regional models, Region 3 had the lowest R2 (0.35). Region 4 had R2 of 0.51. The variables obtained in Regions 3 and 4 were the same variables found by Guimarães et al. (2010b) also in the same regions. In all models (global and regional) the presence of *Biomphalaria*, socioeconomic effects, vegetation index and temperature were the most important variables. These characteristics are the same as environmental conditions for the presence and development of molluscs (infection of the intermediate host) and sanitation (water contamination - presence of *S.*

Bavia et al. (2001), in Bahia, showed that the distribution of schistosomiasis is related to the

The use of GIS, remote sensing, geostatistical and statistical techniques together proved to

This study explored the relationship between the schistosomiasis prevalence and the existing snail species in Minas Gerais state, Brazil (*B. glabrata*, *B. tenagophila*, *B.straminea*). The results showed that the snail species that has more correlation with the disease prevalence is

The generated results showed that logistic regression or indicator kriging are consistent tools and the obtained map can be used as an auxiliary tool to formulate proper public health strategies and to guide fieldwork, considering the places with higher occurrence probability for occurrence of the most important species. Although not shown here, it is interesting to notice that among the models generated for regression logistic for the three *Biomphalaria* species, there was coincidence in the selection of variables. Variables that represent the vegetation, temperature, precipitation and topography were presented in the models, giving an indication of the important conditions for the mollusc's development. Most of the selected explanatory variables in the global and regional models were related to environmental conditions for the presence and development of the mollusc, even as to the

municipalities with good estimates.

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**4. Conclusion and further work**

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< http://www.dpi.inpe.br/geoschisto/publicacoes/Martins\_dissertacao.pdf> Martins-Bedê FT, Freitas CC, Dutra LV, Sandri SA, Fonseca FR, Drummond IN,

Guimaraes RJPS, Amaral RS, Carvalho OS (2009a). Risk Mapping of schistosomiasis in Minas Gerais, Brazil, Using MODIS and Socioeconomic Spatial Data. *Geoscience and Remote Sensing, IEEE Transactions on*, Vol. 47, No. 11, pp.

Amaral RS (2009b). Classification of schistosomiasis prevalence using fuzzy casebased reasoning. In *Bio-Inspired Systems: Computational and Ambient Intelligence* 5517,

Carvalho OS (2010). Schistosomiasis risk mapping in the state of Minas Gerais, Brazil, using a decision tree approach, remote sensing data and sociological

*em área endêmica de Minas Gerais - Brasil*, Biologia parasitária PhD Thesis, Instituto

Esquistossomose em área de ecoturismo do Estado de Minas Gerais, Brasil.

*Taphius glabratus* from *Taphius tenagophilus*. *Am J Trop Med Hyg*, Vol. 8, No. 4, pp.

Ecuador to two strains of *Schistosoma mansoni*. *Rev Inst Med Trop São Paulo*, Vol. 15,

Basommatophora Pulmonata). *Mem Inst Oswaldo Cruz*, Vol. 76, No. 2, pp. 199-211,

<http://www.cidades.gov.br/secretarias-nacionais/saneamentoambiental/indicadores/Sniu.zip/view>

**14**

*Nigeria*

**The Merits of Urine Color Observation**

Robert Houmsou1, Elizabeth Amuta2 and Edward Omudu3

*3Department of Biological Sciences, Benue State University, Makurdi*

*1Department of Biological Sciences, Taraba State University*

**Estimate** *Schistosoma Haematobium* **Infection in Two Endemic Areas of Benue State, Nigeria**

*2Department of Biological Sciences, University of Agriculture Makurdi, Benue State*

Schistosomiasis is the most prevalent parasitic infection in the world after malaria, with nearly 207 people infected, and 779 million currently at risk in 76 countries of the tropics

In sub-Saharan Africa, about 192 million are found to be infected with schistosomiasis (Hotez and Kamath, 2009).The highest prevalence and intensities of human schistosomiasis occur in school-aged children, adolescents, and young adults who also suffer from the

Urinary Schistosomiasis due *Schistosoma haematobium* is a significant cause of clinical morbidity and disability in the endemic countries of Africa and the Middle East, where

In sub-Saharan Africa two-thirds of the schistosomiasis cases are due to *Schistosoma haematobium,* which represents an important cause of severe urinary tract disease (van der Werf *et al*., 2000). They also estimated that 70 million and 32 million individuals out of 682 million people in sub-Saharan Africa had experienced hematuria and dysuria, respectively, within the last two weeks of their reports. *S. haematobium* produces bladder wall pathology in approximately 18 million people in sub-Saharan Africa, and 10 million people suffer from hydronephrosis (van der Werf *et al*., 2000). Renal failure accounts for a large percentage of the estimated 150,000 deaths from urinary tract schistosomiasis in sub-Saharan Africa, and significant association was observed between major bladder wall pathology and squamous cell carcinoma (Maxwell, 2008). A significant percentage of women and men with urinary schistosomiasis acquire genital ulcers and other lesions (King and Dangerfield-cha, 2008). Identification of cases or communities for treatment with *Schistosoma haematobium* infection is usually based on microscopic detection of eggs in urine. Haematuria and proteinuria are recognized clinical features of *S.haematobium* infection (Wilkins, 1977). Many epidemiological studies have been conducted to investigate the characteristics of these methods to measure urinary schistosomiasis; this usually involved comparing the outcomes

more than 110 million people are infected (van der Werf and de Vlas, 2001).

**1. Introduction**

highest morbidity and mortality.

with intensity of infection.

where the disease is endemic (Steinmann *et al*., 2006).

**as a Rapid Diagnostic Technique to**

## **The Merits of Urine Color Observation as a Rapid Diagnostic Technique to Estimate** *Schistosoma Haematobium* **Infection in Two Endemic Areas of Benue State, Nigeria**

Robert Houmsou1, Elizabeth Amuta2 and Edward Omudu3

*1Department of Biological Sciences, Taraba State University 2Department of Biological Sciences, University of Agriculture Makurdi, Benue State 3Department of Biological Sciences, Benue State University, Makurdi Nigeria*

#### **1. Introduction**

280 Schistosomiasis

Vidigal THDA, Caldeira RL, Simpson AJG, Carvalho OS (2000). Further studies on the

WHO (1957). Study Group on the Ecology of Intermediate Snail Hosts of Bilharziasis*, Technical Report Series 120,* World Health Organization*,* Geneva, pp 120*.*

Vol. 95, No. 1, pp. 57-66, 0074-0276.

molecular systematics of *Biomphalaria* snails from Brazil. *Mem Inst Oswaldo Cruz*,

Schistosomiasis is the most prevalent parasitic infection in the world after malaria, with nearly 207 people infected, and 779 million currently at risk in 76 countries of the tropics where the disease is endemic (Steinmann *et al*., 2006).

In sub-Saharan Africa, about 192 million are found to be infected with schistosomiasis (Hotez and Kamath, 2009).The highest prevalence and intensities of human schistosomiasis occur in school-aged children, adolescents, and young adults who also suffer from the highest morbidity and mortality.

Urinary Schistosomiasis due *Schistosoma haematobium* is a significant cause of clinical morbidity and disability in the endemic countries of Africa and the Middle East, where more than 110 million people are infected (van der Werf and de Vlas, 2001).

In sub-Saharan Africa two-thirds of the schistosomiasis cases are due to *Schistosoma haematobium,* which represents an important cause of severe urinary tract disease (van der Werf *et al*., 2000). They also estimated that 70 million and 32 million individuals out of 682 million people in sub-Saharan Africa had experienced hematuria and dysuria, respectively, within the last two weeks of their reports. *S. haematobium* produces bladder wall pathology in approximately 18 million people in sub-Saharan Africa, and 10 million people suffer from hydronephrosis (van der Werf *et al*., 2000). Renal failure accounts for a large percentage of the estimated 150,000 deaths from urinary tract schistosomiasis in sub-Saharan Africa, and significant association was observed between major bladder wall pathology and squamous cell carcinoma (Maxwell, 2008). A significant percentage of women and men with urinary schistosomiasis acquire genital ulcers and other lesions (King and Dangerfield-cha, 2008).

Identification of cases or communities for treatment with *Schistosoma haematobium* infection is usually based on microscopic detection of eggs in urine. Haematuria and proteinuria are recognized clinical features of *S.haematobium* infection (Wilkins, 1977). Many epidemiological studies have been conducted to investigate the characteristics of these methods to measure urinary schistosomiasis; this usually involved comparing the outcomes with intensity of infection.

The Merits of Urine Color Observation as a Rapid Diagnostic Technique

**2.3 Determination of microhaematuria and proteinuria**

and Ca.500 (+++) respective.

**2.5 Statistical analysis**

was considered at *p* < 0.01.

using the following formulae.

**2.6 Evaluation of diagnostic performance**

**2.4 Egg counts**

to Estimate *Schistosoma Haematobium* Infection in Two Endemic Areas of Benue State, Nigeria 283

A total of 1,292 urine specimens were collected from apparently healthy individuals aged 1- 30 years, whom were carefully instructed to collect the last part of their urine. About 20 ml of clean-catch, midstream urine samples were collected in a 20 ml capacity autoclaved wide mouthed, leak, proof universal containers. Samples were obtained between 10:00 hrs and 14:00 hrs of the day (Cheesbrough, 2000). Urine specimens were visually inspected for colour and graded, from (1) as urine free of any trace of microhaematuria or proteinuria (light-yellow) to (3) as red urine (blood urine).The specimens were appropriately labeled with identification numbers and placed in a cold box. Where delay in transportation of specimens to laboratory was inevitable, ordinary household bleach was added to the urine samples to preserve any schistosome ova present (Cheesbrough, 2000; W.H.O., 2003).

Reagent strips (Medi-Test Combi 9, Macherey-Nagel GmbH & Co.KG, Germany) were dipped into the urine inside the universal containers. Microhaematuria and proteinuria were evaluated and results were ranked as negative (-ve), trace(+), positive(++), (+++) according to the manufacturer's instructions. Concentrations (Ca) of microhaematuria and proteinuria were measured as erythrocytes/µl (ery/µl) and mg/dl of albumin respectively. The degree of microhaematuria and proteinuria concentrations were graded as follows: 0 (negative), Ca.5-10 (+), Ca.50 (++) and Ca.250 (+++) and 0 (negative), Ca.30 (+), Ca.100 (++)

Eggs were recovered from urine by the filtration technique. Filtration is the most sensitive, rapid, and reproducible technique for detecting and quantifying *S. haematobium* eggs in urine. Using blunt-ended (untoothed) forceps, a polycarbonate membrane filter (13 mm diameter and 13µm porosity) was placed carefully on the filter support of the filter holder (13 mm diameter) and attached to the end of a 10ml Luer syringe. The plunger was removed from the syringe before the syringe is filled to the 10ml mark with well-mixed urine after which the plunger was replaced. Holding the syringe over a beaker the urine was slowly passed through the filter. The filter holder is removed and unscrewed before a blunt-ended forceps is carefully used to remove the membrane filter. This was transferred face upwards (eggs on surface) to a slide before addition of a drop of Lugol's iodine with subsequent covering using a cover glass. Using the 10X objective with the condenser iris closed sufficiently to give good contrast, the entire filter was examined systematically for eggs of *S. haematobium*. The number of eggs are counted and reported as egg per 10ml of urine, 1-49/10 ml urine was considered as light

Microsoft Excel 2007 and PASW (Predictive Analysis software) version 18.0.were used to perform data analysis. Associations between urine colour, microhaematuria, proteinuria and intensity of infection were tested using Spearman correlation (*rho*). The significance level

The diagnostic performances of urine colour observation, microhaematuria and proteinuria were assessed by calculating sensitivity, specificity, positive and negative predictive values

infection and ≥50 eggs/10 ml of urine as heavy infection (W.H.O., 2003).

As part of early and successful control with chemotherapy, rapid, cost-effective and reliable diagnostic tools and assay that will play an important role in assessing cases of infection are needed in the present time. Thus, the study was designed to determine if urine colour can be used as a potential diagnostic tool for rapid screening of urinary schistosomiasis in endemic areas and to compare it with other diagnostic methods such as filtration technique and reagent strip tests.

#### **2. Materials and methods**

#### **2.1 Study area**

The study was conducted in two contiguous local government areas (Buruku and Katsina-Ala) of Benue State endemic for urinary schistosomiasis (Houmsou *et al*., 2009). The selection of the areas was based on previous reports from local hospitals, clinics and health centers where cases of urinary schistosomiasis were common particularly among school children. Other parasitic infections like onchocerciasis, malaria and parasites of the gastro-intestinal tract were also reported by health officials. The relative position of the two Local Government Areas in Benue State is about the Middle Eastern part of the State. The areas are drained by streams and rivers among which river Katsina-Ala is the biggest; ponds are also found all over the areas (Figure 1). The areas have a monthly temperature ranging from 27-38ºC and 900-1000 mm of rain fall annually with two distinct seasons: the dry season starts in late October and usually ends by March, while the rainy season lasts from mid-April to early October.

Fig. 1. Physical Map of Buruku and Katsina-Ala LGAs of Benue State, Nigeria (Encarta 2008)

#### **2.2 Study population and samples collection**

Prior to the commencement of the research, ethical approval was sought from the Ministry of Health, Benue State and the Local Government Education Authorities of both areas. Parents of the school children were duly informed on the significance of the study.

A total of 1,292 urine specimens were collected from apparently healthy individuals aged 1- 30 years, whom were carefully instructed to collect the last part of their urine. About 20 ml of clean-catch, midstream urine samples were collected in a 20 ml capacity autoclaved wide mouthed, leak, proof universal containers. Samples were obtained between 10:00 hrs and 14:00 hrs of the day (Cheesbrough, 2000). Urine specimens were visually inspected for colour and graded, from (1) as urine free of any trace of microhaematuria or proteinuria (light-yellow) to (3) as red urine (blood urine).The specimens were appropriately labeled with identification numbers and placed in a cold box. Where delay in transportation of specimens to laboratory was inevitable, ordinary household bleach was added to the urine samples to preserve any schistosome ova present (Cheesbrough, 2000; W.H.O., 2003).

#### **2.3 Determination of microhaematuria and proteinuria**

Reagent strips (Medi-Test Combi 9, Macherey-Nagel GmbH & Co.KG, Germany) were dipped into the urine inside the universal containers. Microhaematuria and proteinuria were evaluated and results were ranked as negative (-ve), trace(+), positive(++), (+++) according to the manufacturer's instructions. Concentrations (Ca) of microhaematuria and proteinuria were measured as erythrocytes/µl (ery/µl) and mg/dl of albumin respectively. The degree of microhaematuria and proteinuria concentrations were graded as follows: 0 (negative), Ca.5-10 (+), Ca.50 (++) and Ca.250 (+++) and 0 (negative), Ca.30 (+), Ca.100 (++) and Ca.500 (+++) respective.

#### **2.4 Egg counts**

282 Schistosomiasis

As part of early and successful control with chemotherapy, rapid, cost-effective and reliable diagnostic tools and assay that will play an important role in assessing cases of infection are needed in the present time. Thus, the study was designed to determine if urine colour can be used as a potential diagnostic tool for rapid screening of urinary schistosomiasis in endemic areas and to compare it with other diagnostic methods such as filtration technique and

The study was conducted in two contiguous local government areas (Buruku and Katsina-Ala) of Benue State endemic for urinary schistosomiasis (Houmsou *et al*., 2009). The selection of the areas was based on previous reports from local hospitals, clinics and health centers where cases of urinary schistosomiasis were common particularly among school children. Other parasitic infections like onchocerciasis, malaria and parasites of the gastro-intestinal tract were also reported by health officials. The relative position of the two Local Government Areas in Benue State is about the Middle Eastern part of the State. The areas are drained by streams and rivers among which river Katsina-Ala is the biggest; ponds are also found all over the areas (Figure 1). The areas have a monthly temperature ranging from 27-38ºC and 900-1000 mm of rain fall annually with two distinct seasons: the dry season starts in late October and usually

Fig. 1. Physical Map of Buruku and Katsina-Ala LGAs of Benue State, Nigeria (Encarta 2008)

Prior to the commencement of the research, ethical approval was sought from the Ministry of Health, Benue State and the Local Government Education Authorities of both areas.

Parents of the school children were duly informed on the significance of the study.

**2.2 Study population and samples collection**

ends by March, while the rainy season lasts from mid-April to early October.

reagent strip tests.

**2.1 Study area**

**2. Materials and methods**

Eggs were recovered from urine by the filtration technique. Filtration is the most sensitive, rapid, and reproducible technique for detecting and quantifying *S. haematobium* eggs in urine. Using blunt-ended (untoothed) forceps, a polycarbonate membrane filter (13 mm diameter and 13µm porosity) was placed carefully on the filter support of the filter holder (13 mm diameter) and attached to the end of a 10ml Luer syringe. The plunger was removed from the syringe before the syringe is filled to the 10ml mark with well-mixed urine after which the plunger was replaced. Holding the syringe over a beaker the urine was slowly passed through the filter. The filter holder is removed and unscrewed before a blunt-ended forceps is carefully used to remove the membrane filter. This was transferred face upwards (eggs on surface) to a slide before addition of a drop of Lugol's iodine with subsequent covering using a cover glass. Using the 10X objective with the condenser iris closed sufficiently to give good contrast, the entire filter was examined systematically for eggs of *S. haematobium*. The number of eggs are counted and reported as egg per 10ml of urine, 1-49/10 ml urine was considered as light infection and ≥50 eggs/10 ml of urine as heavy infection (W.H.O., 2003).

#### **2.5 Statistical analysis**

Microsoft Excel 2007 and PASW (Predictive Analysis software) version 18.0.were used to perform data analysis. Associations between urine colour, microhaematuria, proteinuria and intensity of infection were tested using Spearman correlation (*rho*). The significance level was considered at *p* < 0.01.

#### **2.6 Evaluation of diagnostic performance**

The diagnostic performances of urine colour observation, microhaematuria and proteinuria were assessed by calculating sensitivity, specificity, positive and negative predictive values using the following formulae.

The Merits of Urine Color Observation as a Rapid Diagnostic Technique

to Estimate *Schistosoma Haematobium* Infection in Two Endemic Areas of Benue State, Nigeria 285

The relationship between urine colour observation and microhaematuria among subjects examined in Buruku and Katsina-Ala LGAs is shown in Fig.3. Of the 666 subjects screened having brown colour, 322(48.3%) had microhaematuria with a breakdown of 21.8%, 10.7% and 15.9% for microhaematuria at Ca.5-10, Ca.50 and Ca.250 respectively. Out of the 48 screened having blood urine, 45(93.8%) had microhaematuria at Ca.250, while 3(6.3%) having light yellow colour of urine had microhaematuria at Ca.5-10. A significant relationship was

Fig. 2. Relationship between urine colour observation and microhameturia among subjects

The relationship between urine colour and proteinuria among subjects examined in Buruku and Katsina-Ala LGAs of Benue is shown in Fig.3. Of the 666 screened having brown urine, 604(90.7%) had proteinuria with a breakdown of 301(45.1%), 208(31.2%), 95(14.3%) for proteinuria at Ca.30, Ca.100 and Ca.500 respectively. Out of the 48 screened having blood urine, 33(68.8%) had proteinuria at Ca.500, while 78(15.5%) of the 578 having light yellow colour urine had proteinuria at Ca.30. A significant relationship was observed between

Figure 4 shows the relationship between urine colour observation and intensity of *Schistosoma haematobium* eggs amongst subjects examined in Buruku and Katsina-Ala LGAs of Benue State. Of the 1,292 subjects screened for urine colour, 578 had light yellow (normal colour) among which 46(8.0%) and 3(0.5%) had 1-49eggs/10ml of urine and ≥50

examined in Buruku and Katsina-Ala LGAs of Benue State

urine colour and proteinuria (*rho*=0.7, *p<0.01*).

observed between urine colour observation and microhaematuria (*rho*=0.5, *p<0.01*).

 Sensitivity = *a a b* with a = True positive

b = False negative

 Specificity = *c c d* with c = True negative

d = False positive

 Positive predictive value (PPV) = *a a d*

$$\bullet \quad \text{Negative predictive value (NPV)} = \frac{c}{c+b}$$

#### **2.7 Results**

Urine was visually inspected and assigned a number. Figure 2.0 illustrates the urine colour chart related to the presence of proteinuria and microhaematuria. The urine colour chart ranges from 1 to 3, with 1 indicating urine free of any trace of microhaematuria or proteinuria (light-yellow) and 3 corresponding to red urine (blood urine). Whereas number 2 grouped as brown colour corresponds to visually discernable microhaematuria and proteinuria present in the urine.

Fig. 1. Urine colour observation

with a = True positive

*c d* with c = True negative

*a d*

*c b*

Urine was visually inspected and assigned a number. Figure 2.0 illustrates the urine colour chart related to the presence of proteinuria and microhaematuria. The urine colour chart ranges from 1 to 3, with 1 indicating urine free of any trace of microhaematuria or proteinuria (light-yellow) and 3 corresponding to red urine (blood urine). Whereas number 2 grouped as brown colour corresponds to visually discernable microhaematuria and

Sensitivity = *a*

Specificity = *c*

**2.7 Results**

*a b*

Positive predictive value (PPV) = *a*

Negative predictive value (NPV) = *c*

proteinuria present in the urine.

b = False negative

d = False positive

Key: 1 = urine free of any trace of haematuria and proteinuria 2 = urine with presence of haematuria and proteinuria

 3 = urine with visible blood Fig. 1. Urine colour observation The relationship between urine colour observation and microhaematuria among subjects examined in Buruku and Katsina-Ala LGAs is shown in Fig.3. Of the 666 subjects screened having brown colour, 322(48.3%) had microhaematuria with a breakdown of 21.8%, 10.7% and 15.9% for microhaematuria at Ca.5-10, Ca.50 and Ca.250 respectively. Out of the 48 screened having blood urine, 45(93.8%) had microhaematuria at Ca.250, while 3(6.3%) having light yellow colour of urine had microhaematuria at Ca.5-10. A significant relationship was observed between urine colour observation and microhaematuria (*rho*=0.5, *p<0.01*).

Fig. 2. Relationship between urine colour observation and microhameturia among subjects examined in Buruku and Katsina-Ala LGAs of Benue State

The relationship between urine colour and proteinuria among subjects examined in Buruku and Katsina-Ala LGAs of Benue is shown in Fig.3. Of the 666 screened having brown urine, 604(90.7%) had proteinuria with a breakdown of 301(45.1%), 208(31.2%), 95(14.3%) for proteinuria at Ca.30, Ca.100 and Ca.500 respectively. Out of the 48 screened having blood urine, 33(68.8%) had proteinuria at Ca.500, while 78(15.5%) of the 578 having light yellow colour urine had proteinuria at Ca.30. A significant relationship was observed between urine colour and proteinuria (*rho*=0.7, *p<0.01*).

Figure 4 shows the relationship between urine colour observation and intensity of *Schistosoma haematobium* eggs amongst subjects examined in Buruku and Katsina-Ala LGAs of Benue State. Of the 1,292 subjects screened for urine colour, 578 had light yellow (normal colour) among which 46(8.0%) and 3(0.5%) had 1-49eggs/10ml of urine and ≥50

The Merits of Urine Color Observation as a Rapid Diagnostic Technique

to Estimate *Schistosoma Haematobium* Infection in Two Endemic Areas of Benue State, Nigeria 287

Fig. 4. Relationship between urine colour observation and intensity of *Schistosoma*

*haematobium* eggs among subjects examined in Buruku and Katsina-Ala LGAs of Benue State

Filtration technique (gold standard method) (%)

Table 1. Comparison of urine colour observation as rapid screening test and the true disease status as determined by filtration technique in Buruku and Katsina-Ala LGAs of Benue State

Urine colour Negative Positive **Total** Negative (light-yellow) 529(91.5)**c** 49(8.4)**b 578** Positive (Brown+Red) 210(29.4)**d** 504(70.6)**a 714 Total<PHONE_NUMBER>**

Key: c= True negative b= False negative d= False positive a= True positive

eggs/10ml respectively. Of the 666 having brown colour urine, 378(56.8%) and 80(12.0%) had 1-49eggs/10ml and ≥50 eggs/10ml of urine respectively. Out of the 48 having blood urine, 38(79.2%) had ≥50eggs/10ml of urine. A significant relationship was found between urine colour and intensity of *Schistosoma haematobium* eggs among participants examined (*rho*=0.6, *p<0.01*).

Table 1.0 compares urine colour observed as an indirect test and the true disease status as determined by filtration technique in Buruku and Katsina-Ala LGAs of Benue State. When Compared to the true disease status, light yellow (normal colour) of urine is considered as negative and this was observed in 529(91.5%) participants having no *S. haematobium* eggs (true negative) and 49(8.5%) having *Schistosoma haematobium* eggs (false negative). Of the 714 participants considered positive (brown + blood urine), 504(70.6%) had *S. haematobium* eggs (true positive), while 210(29.4 %) had no *S. haematobium* eggs (false positive).

Table 2 validates observed urine colour as an indirect test in the screening of urinary schistosomiasis in Buruku and Katsina-Ala LGAs of Benue State. The ability of the observed urine colour to accurately identify all those with the disease (sensitivity) was 91.1%, while its ability to correctly sort out all those without the disease (specificity) was 71.6 %. The positive predictive value (PPV) was (70.6 %), while the Negative Predictive Value (NPV) was 91.5%.

Fig. 3. Relationship between urine colour observation and proteinuria among subjects examined in Buruku and Katsina-Ala LGAs of Benue State, Nigeria

Fig. 4. Relationship between urine colour observation and intensity of *Schistosoma haematobium* eggs among subjects examined in Buruku and Katsina-Ala LGAs of Benue State

Key: c= True negative

286 Schistosomiasis

eggs/10ml respectively. Of the 666 having brown colour urine, 378(56.8%) and 80(12.0%) had 1-49eggs/10ml and ≥50 eggs/10ml of urine respectively. Out of the 48 having blood urine, 38(79.2%) had ≥50eggs/10ml of urine. A significant relationship was found between urine colour and intensity of *Schistosoma haematobium* eggs among participants

Table 1.0 compares urine colour observed as an indirect test and the true disease status as determined by filtration technique in Buruku and Katsina-Ala LGAs of Benue State. When Compared to the true disease status, light yellow (normal colour) of urine is considered as negative and this was observed in 529(91.5%) participants having no *S. haematobium* eggs (true negative) and 49(8.5%) having *Schistosoma haematobium* eggs (false negative). Of the 714 participants considered positive (brown + blood urine), 504(70.6%) had *S. haematobium* eggs

Table 2 validates observed urine colour as an indirect test in the screening of urinary schistosomiasis in Buruku and Katsina-Ala LGAs of Benue State. The ability of the observed urine colour to accurately identify all those with the disease (sensitivity) was 91.1%, while its ability to correctly sort out all those without the disease (specificity) was 71.6 %. The positive predictive value (PPV) was (70.6 %), while the Negative Predictive

Fig. 3. Relationship between urine colour observation and proteinuria among subjects

examined in Buruku and Katsina-Ala LGAs of Benue State, Nigeria

(true positive), while 210(29.4 %) had no *S. haematobium* eggs (false positive).

examined (*rho*=0.6, *p<0.01*).

Value (NPV) was 91.5%.

b= False negative

d= False positive

a= True positive

Table 1. Comparison of urine colour observation as rapid screening test and the true disease status as determined by filtration technique in Buruku and Katsina-Ala LGAs of Benue State

The Merits of Urine Color Observation as a Rapid Diagnostic Technique

Ugbomoiko *et al*., 2009).

**4. References**

feasibility in other endemic areas.

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Studies, Havard, School of Public Health, 41pp.

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schistosomiasis. Chronic Illness 4(1): 65–79.

*International Health*, 14(12): 1484-1487.

*Urology and Nephrology*. 1–9. 10.1080/03008880802285032.

*Tropical Medicine and Hygiene*, 68: 315-318.

to Estimate *Schistosoma Haematobium* Infection in Two Endemic Areas of Benue State, Nigeria 289

Takougang *et al*., 2004; Brooker and Utzinger, 2007; Robinson *et al*., 2009; French *et al*., 2009;

The results obtained, however, agree with preliminary reports undertaken earlier to find its possibility as a rapid screening method in endemic areas (Houmsou *et al.*, 2009). Futhermore it is recommended that additional researches should be conducted in order to elucidate its

Anosike, J.C., Nwoke, B.E.B. & Njoku, A.J. (2001). Validity of haematuria in the

Brooker, S. & Utzinger, J. (2007). Integrated disease mapping in polyparasitic world.

Cheesbrough, M.(2000). District Laboratory Practice in Tropical Countries, part 1 Cambridge

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Hotez, P.J. & Kamath, A. (2009). Neglected tropical diseases in sub-Saharan Africa: review of

Houmsou, R.S., Kela, S.L., Suleiman, M.M. & Ogidi, J.A. (2009). Urine colour as a rapid

Maxwell, P.D. (2008). The global burden of urinary bladder cancer. *Scandinav Journal of*

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Nigeria. *The Internet Journal of Tropical Medicine*, 6(1). www.ispub.com. King C.H. & Dangerfield-Cha, M. (2008). The unacknowledged impact of chronic

intensity of *Schistosoma haematobium* infection in zanzibari school children. *Working Paper Series*, 14(5). Havard Center for Population and Development

*haematobium* infection and heavy proteinuria. *Transactions of the Royal Society of*

glomerulonephritis associated with *Schistosoma haematobium* infection. *Proceedings of*

(2009) School-based control of urinary schistosomiasis on Zanzibar, Tanzania: monitoring microhaematuria with reagent strips as a rapid urological

their prevalence, distribution and disease burden. *PLoS Neglected Tropical Diseases*,

indicator of urine schistosomiasis in Katsina-Ala and Buruku LGAs of Benue State,

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community diagnosis of urinary schistosomiasis infection. *Journal of Helminthology*,

Table 2. Validation of urine colour as rapid screening test for urinary schistosomiasis in Buruku and Katsina-Ala LGAs of Benue State

#### **3. Discussion**

It was demonstrated that assessing urine colour through simple observation significantly estimated the prevalence of infection and correlated with infection intensity as measured by egg counts (Filtration technique) known as the gold standard test. This shows that urine colour observation, if used as rapid screening tool in an endemic area is capable of assessing the endemic community for urinary schistosomiasis prevalence.

In addition to being a useful rapid field diagnostic for *Schistosoma haematobium* infection, urine colour as assessed by observation may also prove to be an indicator of morbidity through proteinuria and microhaematuria. Both proteinuria and microhaematuria correlated well with urine colour, *rho* = 0.7 and *rho* = 0.5 respectively at 0.01 significance level. However, the brown colour of urine should be the result of excreted protein and red blood cells into the urine from the damage of urinary tract and kidney. However, inconclusive evidence suggests that *Schistosoma haematobium* affects the glomeruli, the units of the kidney that function to separate out wastes and extra fluid from the blood. When the glomeruli are damaged, protein and often red blood cells leak into the urine as this might be the case in this study. Although, at the present time the precise origin and clinical significance of the proteinuria observed in *S. haematobium* infection remains unknown (Elissa, 2004). Nonetheless Sabour *et al*. (1972), Ezzat *et al*. (1974 and Ezzat *et al*. (1978) claimed an association between glomerulonephritis, the inflammation of the membrane tissue of the kidney and *S. haematobium* in human. Chugh and Sakuja (1990) reported that glomurelonephritis is highly prevalent in areas of the tropic where urinary schistosomiasis is also common, however its relationship to *S. haematobium* remain unclear. Sobh *et al*. (1991) showed that infected hamsters and not control animals developed significant glomerular damage related to the presence and intensity of *Schistosoma haematobium* infections.

The sensitivity of urine colour for screening test was high (91.1%) and with a specificity of 71.6%. This high sensitivity may be an indication of the predisposition of these rural dwellers to renal complications associated with urinary schistosomiasis. When compared to other indirect tests used in other studies like reagent strips, sensitivity of urine colour observation in this study was higher, but a specificity lower than results obtained by Ugbomoiko *et al*. (2009) who reported sensitivity of 68.3% and specificity of 83.2% using only microhaematuria, and sensitivity of 67.7% and specificity of 79.6% using only proteinuria respectively as indirect tests. However, variation in sensitivity and specificity of indirect tests during *Schistosoma haematobium* infection has been reported in several studies conducted in different African settings. They have been reported to vary from 41.0 % to 93 % and from 67 % to 99 % for sensitivity and specificity respectively (Anosike *et al*, 2001; Takougang *et al*., 2004; Brooker and Utzinger, 2007; Robinson *et al*., 2009; French *et al*., 2009; Ugbomoiko *et al*., 2009).

The results obtained, however, agree with preliminary reports undertaken earlier to find its possibility as a rapid screening method in endemic areas (Houmsou *et al.*, 2009). Futhermore it is recommended that additional researches should be conducted in order to elucidate its feasibility in other endemic areas.

#### **4. References**

288 Schistosomiasis

Table 2. Validation of urine colour as rapid screening test for urinary schistosomiasis in

It was demonstrated that assessing urine colour through simple observation significantly estimated the prevalence of infection and correlated with infection intensity as measured by egg counts (Filtration technique) known as the gold standard test. This shows that urine colour observation, if used as rapid screening tool in an endemic area is capable of assessing

In addition to being a useful rapid field diagnostic for *Schistosoma haematobium* infection, urine colour as assessed by observation may also prove to be an indicator of morbidity through proteinuria and microhaematuria. Both proteinuria and microhaematuria correlated well with urine colour, *rho* = 0.7 and *rho* = 0.5 respectively at 0.01 significance level. However, the brown colour of urine should be the result of excreted protein and red blood cells into the urine from the damage of urinary tract and kidney. However, inconclusive evidence suggests that *Schistosoma haematobium* affects the glomeruli, the units of the kidney that function to separate out wastes and extra fluid from the blood. When the glomeruli are damaged, protein and often red blood cells leak into the urine as this might be the case in this study. Although, at the present time the precise origin and clinical significance of the proteinuria observed in *S. haematobium* infection remains unknown (Elissa, 2004). Nonetheless Sabour *et al*. (1972), Ezzat *et al*. (1974 and Ezzat *et al*. (1978) claimed an association between glomerulonephritis, the inflammation of the membrane tissue of the kidney and *S. haematobium* in human. Chugh and Sakuja (1990) reported that glomurelonephritis is highly prevalent in areas of the tropic where urinary schistosomiasis is also common, however its relationship to *S. haematobium* remain unclear. Sobh *et al*. (1991) showed that infected hamsters and not control animals developed significant glomerular

damage related to the presence and intensity of *Schistosoma haematobium* infections.

The sensitivity of urine colour for screening test was high (91.1%) and with a specificity of 71.6%. This high sensitivity may be an indication of the predisposition of these rural dwellers to renal complications associated with urinary schistosomiasis. When compared to other indirect tests used in other studies like reagent strips, sensitivity of urine colour observation in this study was higher, but a specificity lower than results obtained by Ugbomoiko *et al*. (2009) who reported sensitivity of 68.3% and specificity of 83.2% using only microhaematuria, and sensitivity of 67.7% and specificity of 79.6% using only proteinuria respectively as indirect tests. However, variation in sensitivity and specificity of indirect tests during *Schistosoma haematobium* infection has been reported in several studies conducted in different African settings. They have been reported to vary from 41.0 % to 93 % and from 67 % to 99 % for sensitivity and specificity respectively (Anosike *et al*, 2001;

**Parameters Percentage (%)**

Sensitivity 91.1 Specificity 71.6 Positive predictive value (PPV) 70.6 Negative predictive value (NPV) 91.5

the endemic community for urinary schistosomiasis prevalence.

Buruku and Katsina-Ala LGAs of Benue State

**3. Discussion**

**Part 6**

**Malacology**

**Part 6**

**Malacology**

290 Schistosomiasis

Sobh, M.A., Moustapha, F.E., Ramzy, R.M., Deelder, A.M. & Ghoreim, M.A. (1991).

Steinmann, P., Keiser, J., Bos, R., Tanner, M. & Utzinger, J. (2006). Schistosomiasis and water

Takougang, I., Meli, J., Fotso, S., Angwafo III, F., Kamajeu, R. & Ndumbe, P.M. (2004).

Ugmoboiko, U.S., Dalumo, V., Ariaza, L., Bezerra, F.S.M. & Heukelbach, J. (2009). A simple

van der Werf, M.J., de Vlas SJ, Brooker S, Looman CW, Nagelkerke NJ, *et al*. (2003)

van der Werf, M.J. & de Vlas, S.J. (2001). Morbidity and infection with schistosome or soil

Wilkins, H.A. (1977). *Schistosoma haematobium* in a Gambian community. The intensity and prevalence of infection. *Annals of Tropical Medicine and Parasitology*, 71(1): 53-58. World Health Organization. (2003). Manual of basic techniques for a health laboratory, 2nd

golden hamster. *Nephron*, 57(2): 216-224.

121-127.

*Cruz,* 104(3):456-461.

risk. The *Lancet Infectious Diseases*, 6(7): 411-425.

Saharan Africa. *Acta Tropica*, 86(2–3): 125–139.

transmitted helminths. *Rotterdam Erasmus University*, 1-103.

edition. World Health Organization, Geneva, 126pp.

*Schistosoma haematobium*-induced glomerular disease: and experimental study in the

resources development: systematic review meta-analysis and estimates of people at

Haematuria and dysuria in the self diagnosis of urinary schistosomiasis among school children in Northern Cameroon. *African Journal of Health Sciences*, 11(3&4):

approach improving the performance of urine reagent strips for rapid diagnosis of urinary schistosomiasis in Nigerian school children. *Memorias Instituto Oswaldo*

Quantification of clinical morbidity associated with schistosome infection in sub-

**15**

*Brazil*

**Pattern of Genetic Divergence of Mitochondrial**

**DNA Sequences in** *Biomphalaria tenagophila*

Roseli Tuan, Fernanda Pires Ohlweiler, Raquel Gardini Sanches Palasio,

Neotropical region has a high diversity of *Biomphalaria* (Pulmonata: Basommatophora: Planorbidae) fauna with at least twenty-six of the world's estimated thirty- seven species recorded (Carvalho,2008). Nearly one –third of these species occur in freshwater ecosystems related to the main drainage river basins in Brazil (Estrada et al.,2006; Teodoro,2010),where *Biomphalaria glabrata* (Say,1818)*,Biomphalaria tenagophila (*Orbigny,1835) and *Biomphalaria straminea (*Dunker,1848) have serious impact on human health for these species host

Fig. 1. Map (according Palasio, R.G.S., 2011) showing the worldwide distribution of the 37 *Biomphalaria* species, the 26 neotropical *Biomphalaria* species and the 11 species and 1

subspecies naturally described in Brazil (•) (Brasil,Ministério da Saúde 2007; Carvalho et al., 2008; Teodoro et al.,2010). An asterisk indicates *Schistosoma mansoni* host species (Palasio,

**1. Introduction**

RGS, 2011).

*Schistosoma mansoni* Sambon 1907 (Figure 1).

**Complex Species Based on Barcode and**

Ricardo Dalla Zanna and Marisa Cristina De Almeida Guimarães

**Morphological Analysis**

*Superintendência de Controle de Endemias, São Paulo*

## **Pattern of Genetic Divergence of Mitochondrial DNA Sequences in** *Biomphalaria tenagophila*  **Complex Species Based on Barcode and Morphological Analysis**

Roseli Tuan, Fernanda Pires Ohlweiler, Raquel Gardini Sanches Palasio, Ricardo Dalla Zanna and Marisa Cristina De Almeida Guimarães *Superintendência de Controle de Endemias, São Paulo Brazil*

#### **1. Introduction**

Neotropical region has a high diversity of *Biomphalaria* (Pulmonata: Basommatophora: Planorbidae) fauna with at least twenty-six of the world's estimated thirty- seven species recorded (Carvalho,2008). Nearly one –third of these species occur in freshwater ecosystems related to the main drainage river basins in Brazil (Estrada et al.,2006; Teodoro,2010),where *Biomphalaria glabrata* (Say,1818)*,Biomphalaria tenagophila (*Orbigny,1835) and *Biomphalaria straminea (*Dunker,1848) have serious impact on human health for these species host *Schistosoma mansoni* Sambon 1907 (Figure 1).

Fig. 1. Map (according Palasio, R.G.S., 2011) showing the worldwide distribution of the 37 *Biomphalaria* species, the 26 neotropical *Biomphalaria* species and the 11 species and 1 subspecies naturally described in Brazil (•) (Brasil,Ministério da Saúde 2007; Carvalho et al., 2008; Teodoro et al.,2010). An asterisk indicates *Schistosoma mansoni* host species (Palasio, RGS, 2011).

Pattern of Genetic Divergence of Mitochondrial DNA Sequences in

difficult to diagnose in a malacological work routine.

*B.t.guaibensis* is not an intermediary host of *S. mansoni.*

*Biomphalaria* species.

identification.

caused great concern to public health authorities.

organism with the environment.

*Biomphalaria tenagophila* Complex Species Based on Barcode and Morphological Analysis 295

The morphological characters of the genital organs employed by the malacologist WL Paraense permits all of us to correctly identify the taxonomic units of the *tenagophila* complex. But,in fact,identifying *Biomphalaria* species solely on morphological characters leads to a less precise identification of these taxonomical groups,which are found to be

The following examples may illustrate how morphological or technical variations can lead

*Example 1:* Within the species of planorbid described in Brazil,a *B. glabrata* is the only species to feature pigmented renal ridge (Paraense and Deslandes 1959). This character is known to be the only to set the difference between *B. glabrata* (the most important host species of *S. mansoni* in the Americas) and *B. tenagophila*,responsible for the transmission of schistosomiasis in the southeastern region of Brazil. Pepe et al. (2009) reports that a pigmentation-like may appear associated to the renal ridge in *B. t. guaibensis,*as a result from an intraspecies variation. Such diagnosis would trigger an unnecessary alert once

Variation in the pattern of the pigmentation of the renal ridge may also reflect the influences of the freshwater substrate types leading us to an erroneous identification of all

Here we exemplified the two major aggravating factors in a morphological identification system: morph variation within the species and variation caused by interaction of the

*Example 2:* According to Paraense (1984) an essential condition for a clear distinction of *B.tenagophila* from the subspecies *B. t. guaibensis* "*is that the animal body is preserved for dissection in a well relaxed condition"*,that is,for the identification of the subspecies,more rigorous laboratorial methods of looseness need to be used instead of the methods utilized in the current identification of mollusks. If the *B.t. guaibensis* subspecies were dispersed,it

The intrinsic difficulties to the system of morphological identification of organisms limit the description of taxonomic discreet units,although they lead to the development of auxiliary molecular techniques to overcome these difficulties (Estrada et al.,2006; Carvalho,2008). Species of planorbid snails belonging to the genus *Biomphalaria* have received increasing attention in molecular systematic and phylogeographic studies in recent years (Vidigal et al. 2000,2004; Carvalho et al. 2001; DeJong et al. 2001,2003; Mavárez et al. 2002a,b; Morgan et al. 2002; Pointier et al. 2005; Tuan and Santos,2007). The development of genetic markers and the databases generated in such studies can be further used for multiple purposes,of both theoretical and applied interest: species identification,analysis of population genetic structure and speciation patterns,inference of common/independent origins of susceptibility to *Schistosoma*,detection of natural hybridization,tracking of geographical expansions and biological invasions,evaluation of genetic differentiation between phenotypic variants and/or geographic isolates,amongst others. Genetic markers used in *Biomphalaria* studies include mitochondrial DNA (Spatz et al. 1999; DeJong et al. 2001,2003; Mavárez et al. 2002b; Pointier et al. 2005). The latter is especially useful for species

The dispersal and colonizing ability of *B. tenagophila* highlights the need to monitor the spread of this species within and outside the neotropical region*.* The identification of *B. tenagophila* in Kinshasa,Africa (Pointier,2005) and Romania (Majoros et al. 2008) certainly

would be difficult to have a morphological differential diagnosis of these subspecies.

to erroneous species identification with serious epidemiological consequences.

*B. tenagophila* is the nominal member of a species complex of freshwater snails distributed widespread in the neotropical region (Figure 2). *B. tenagophila* is the main host of *S. mansoni* in the southeastern Brazil. (Paraense & Corrêa,1978).As currently defined,the *B. tenagophila* complex includes a sub-species *B. tenagophila guaibensis* (Paraense,1984) and a species *B. occidentalis* (Paraense,1981).

Fig. 2. Geographic distribution of *Biomphalaria tenagophila* complex in the southeastern Brazil. (R.G.S. Palasio & R. Tuan based on illustrations in Lima et al.,1993; Paraense 1984, 2001; Carvalho & Caldeira,2004).

*B. tenagophila* is the nominal member of a species complex of freshwater snails distributed widespread in the neotropical region (Figure 2). *B. tenagophila* is the main host of *S. mansoni* in the southeastern Brazil. (Paraense & Corrêa,1978).As currently defined,the *B. tenagophila* complex includes a sub-species *B. tenagophila guaibensis* (Paraense,1984) and a species *B.*

Fig. 2. Geographic distribution of *Biomphalaria tenagophila* complex in the southeastern Brazil. (R.G.S. Palasio & R. Tuan based on illustrations in Lima et al.,1993; Paraense 1984,

*occidentalis* (Paraense,1981).

2001; Carvalho & Caldeira,2004).

The morphological characters of the genital organs employed by the malacologist WL Paraense permits all of us to correctly identify the taxonomic units of the *tenagophila* complex. But,in fact,identifying *Biomphalaria* species solely on morphological characters leads to a less precise identification of these taxonomical groups,which are found to be difficult to diagnose in a malacological work routine.

The following examples may illustrate how morphological or technical variations can lead to erroneous species identification with serious epidemiological consequences.

*Example 1:* Within the species of planorbid described in Brazil,a *B. glabrata* is the only species to feature pigmented renal ridge (Paraense and Deslandes 1959). This character is known to be the only to set the difference between *B. glabrata* (the most important host species of *S. mansoni* in the Americas) and *B. tenagophila*,responsible for the transmission of schistosomiasis in the southeastern region of Brazil. Pepe et al. (2009) reports that a pigmentation-like may appear associated to the renal ridge in *B. t. guaibensis,*as a result from an intraspecies variation. Such diagnosis would trigger an unnecessary alert once *B.t.guaibensis* is not an intermediary host of *S. mansoni.*

Variation in the pattern of the pigmentation of the renal ridge may also reflect the influences of the freshwater substrate types leading us to an erroneous identification of all *Biomphalaria* species.

Here we exemplified the two major aggravating factors in a morphological identification system: morph variation within the species and variation caused by interaction of the organism with the environment.

*Example 2:* According to Paraense (1984) an essential condition for a clear distinction of *B.tenagophila* from the subspecies *B. t. guaibensis* "*is that the animal body is preserved for dissection in a well relaxed condition"*,that is,for the identification of the subspecies,more rigorous laboratorial methods of looseness need to be used instead of the methods utilized in the current identification of mollusks. If the *B.t. guaibensis* subspecies were dispersed,it would be difficult to have a morphological differential diagnosis of these subspecies.

The intrinsic difficulties to the system of morphological identification of organisms limit the description of taxonomic discreet units,although they lead to the development of auxiliary molecular techniques to overcome these difficulties (Estrada et al.,2006; Carvalho,2008).

Species of planorbid snails belonging to the genus *Biomphalaria* have received increasing attention in molecular systematic and phylogeographic studies in recent years (Vidigal et al. 2000,2004; Carvalho et al. 2001; DeJong et al. 2001,2003; Mavárez et al. 2002a,b; Morgan et al. 2002; Pointier et al. 2005; Tuan and Santos,2007). The development of genetic markers and the databases generated in such studies can be further used for multiple purposes,of both theoretical and applied interest: species identification,analysis of population genetic structure and speciation patterns,inference of common/independent origins of susceptibility to *Schistosoma*,detection of natural hybridization,tracking of geographical expansions and biological invasions,evaluation of genetic differentiation between phenotypic variants and/or geographic isolates,amongst others. Genetic markers used in *Biomphalaria* studies include mitochondrial DNA (Spatz et al. 1999; DeJong et al. 2001,2003; Mavárez et al. 2002b; Pointier et al. 2005). The latter is especially useful for species identification.

The dispersal and colonizing ability of *B. tenagophila* highlights the need to monitor the spread of this species within and outside the neotropical region*.* The identification of *B. tenagophila* in Kinshasa,Africa (Pointier,2005) and Romania (Majoros et al. 2008) certainly caused great concern to public health authorities.

Pattern of Genetic Divergence of Mitochondrial DNA Sequences in

*B. tenagophila*

*B. t. guaibensis*

*B. occidentalis*

NA,Not Available.

molecular study.

**2.2 Morphology analysis**

used for dissection and anatomic study.

*Biomphalaria tenagophila* Complex Species Based on Barcode and Morphological Analysis 297

Ourinhos (SP),Brazil *Btt\_Our1 -6* 22°58'02"S 49°52"25" W Ipauçu (SP),Brazil *Btt\_Juq1-8* 23°02'54"S 49°34'41"W Taubaté (SP),Brazil *Btt\_Tau1-3* 23°01'35"S 45°33'21"W

Porto Alegre (RS),Brazil *Btg\_POA1-4* 29°59'52. 3''S 51°15'38.6''W

Ourinhos (SP),Brazil *Boc\_Our 1-5* 22°58'02"S 49°52'25"W Martinópolis (SP),Brazil *Boc\_Mar1-2* 22°14'04"S 51°09'36"W

Presidente Prudente (SP),Brazil *Boc\_PPr1* 22°10'12"S 51°22'34"W Candido Mota (SP),Brazil *Boc\_CMo1* 22°44'52"S 50°23'08"W

Table 1. Collecting data,code and geographical identification of the specimen used in

Sequences were corrected using Chromas (Technelysium Pty Ltd.),aligned in ClustalX 1.8 (Thompson et al. 1997). The alignments of COI datasets comprised sequences with no gaps or indels. The alignments were subsequently adjusted by eye using BioEdit 7.0 (Hall, 1999). Phylogenetic and molecular evolutionary analyses were conducted using *MEGA* version 5 (Tamura,Peterson,Stecher,Nei,Kumar 2011). Sequence divergence between species was estimated with Kimura 2 parameter model (K2P) (Kimura, 1980). The Neighbor-Joining (Saitou and Nei 1987) trees were obtained using K2P model and the support for the nodes was calculated using 1000 bootstrap replicates (Felsenstein, 1985). Nucleotide diversity (pi,Nei,1987,equations 10.5 or 10.6 and 10.7); and nucleotide divergence between sequence groups (Dxy,Nei,1987,equation 10.20 using Jukes and Cantor correction) were calculated using DnaSP v.5 (Librado and Rozas,2009). F*st* was estimated following Hudson et al. 1992.

The morphological analysis was based on 10 dissected specimens *B. tenagophila* from Arambaré,Rio Grande do Sul (lot DPE 9011); 13 dissected specimens *B. t. guaibensis* from Porto Alegre,Rio Grande do Sul (lot DPE 9008,Figure 3) and 31 dissected specimens *B. occidentalis* from Tremembé,São Paulo (lot DPE 9028). Only large and adult specimens were

**Species/Locality Code Geographical coordinates**

Pindamonhangaba (SP),Brazil *Btt\_Pin1-2* 22º53'30''S 45º28'09''W Caraguatatuba (SP),Brazil *Btt\_Car1-7* 23°37'31"S 45°24'44"W São Sebastião (SP),Brazil *Btt\_SSe1* 23º47'08"S 45º33'22"W Campinas (SP),Brazil *Btt\_Cam1-4* 23º02'13''S 47º06'21''W Sorocaba (SP),Brazil *Btt\_Sor1-5* 23°29'56"S 47°27'30"W Registro (SP),Brazil *Btt\_Reg1-3* 24º29'20''S 47º51'06"W Juquiá (SP),Brazil *Btt\_Juq1-8* 24º19'33"S 47º38'22''W Araraquara (SP),Brazil *Btt\_Ara1* 23°01'35"S 45°33'21"W

Rio da Prata,Argentina *Btt\_Arg1-2* NA

For *Biomphlalaria* species two important characteristics play an important role to successfully establish snail populations in a environment outside the native range of the species: self-fertilization and desiccation. *B.tenagophila* species are simultaneous hermaphrodites snails with the possibility of both self and cross-fertilization (Paraense,1955; Tuan & Simões,1989; Guimarães,2003). Such a complex mode of reproduction might probably increase colonization since new populations can arise from just one snail by self-fertilization.

On the other hand reproduction alone is not enough to establish a species because snails still must be able to survive conditions outside their native range. One important aspect to *B. tenagophila* survival it is the ability to withstand desiccation in response to water shortage (Tuan & Simões,1989; Ohlweiler & Kawano,2002) that greatly increases the possibility of recolonization after a population crash or colonization of new environments. These must be a major source of genetic diversity amongst the same species creating patchily distributed populations that can be easily distinguished by molecular markers.

In this study,we obtained mitochondrial COI and 16SrRNA sequences from *B. tenagophila*,*B. t. guaibensis*,and *B. occidentalis* specimens collected in the São Paulo state,and Rio Grande do Sul state,Brazil to estimate the divergence of each species amongst the three members of the *tenagophila* complex and evaluate the potential of COI and 16S rRNA mitochondrial genes for identification of closely related species.

#### **2. Materials and methods**

#### **2.1 Molecular analysis**

*B. tenagophila* and *B. occidentallis* snails were collected along the banks of different streams of Paranapanema,Ribeira do Iguape,Paraíba do Sul,Tietê,and Litoral Basins at São Paulo,Brazil. *B.t. guaibensis* specimens were collected at Rio Grande do Sul,Brazil (Table 1). Two samples from Argentina were also analyzed. A total of 66 specimens were used in this study. Prior to molecular analysis snails were identified through morphological analysis,according to Deslandes (1959) and Paraense (1975,1981,and 1984).

Total genomic DNA was extracted from foot tissue of individual snails using DNeasy Tissue Kits (Qiagen®) and was preserved as DNA vouchers specimens in the DNA voucher collection of the Laboratory of Molecular Biology and Biochemical at SUCEN. Amplification of COI gene was attempted using LCO-1490 and HCO-2198 (Folmer et al.,1994). Amplifications (in 50µl of total volume) consisted of 10-100 ng of DNA,0.2 mM of each dNTP,0.10 µM of each primer and 1 U of *Taq* DNA polymerase (BioLabs) with the supplied buffer. The following PCR temperature profile was used: a initial 3 min step at 95ºC for denaturation,25 iterations of 1 min at 95ºC,1 min at 47ºC and 1 min 30 sec at 72ºC,and a final extension step for 7 min at 72ºC. The amplification profile of 16SrRNA fragment included an initial denaturation step at 94°C for 2 min. and 35 cycles of 30 sec at 94°C,30 sec at 54°C and 1 minute at 72°C,and a final step at 72°C for 5 minutes. Amplification of 16S gene was attempted using a Palumbi forward and reverse primer (Palumbi, 1996).

PCR products were checked by agarose gel electrophoresis,cleaned using QIAquick PCR Purification Kits (Qiagen®). Cycle-sequencing of PCR products were carried out using terminal primers given above and the BigDye Terminator v3.1 kit of Applied Biosystems (ABI),purified using DyeEx-Kit (Qiagen®) according to the modified protocol and sequenced on an ABI 310 automated sequencer.

NA,Not Available.

296 Schistosomiasis

For *Biomphlalaria* species two important characteristics play an important role to successfully establish snail populations in a environment outside the native range of the species: self-fertilization and desiccation. *B.tenagophila* species are simultaneous hermaphrodites snails with the possibility of both self and cross-fertilization (Paraense,1955; Tuan & Simões,1989; Guimarães,2003). Such a complex mode of reproduction might probably increase colonization since new populations can arise from

On the other hand reproduction alone is not enough to establish a species because snails still must be able to survive conditions outside their native range. One important aspect to *B. tenagophila* survival it is the ability to withstand desiccation in response to water shortage (Tuan & Simões,1989; Ohlweiler & Kawano,2002) that greatly increases the possibility of recolonization after a population crash or colonization of new environments. These must be a major source of genetic diversity amongst the same species creating patchily distributed

In this study,we obtained mitochondrial COI and 16SrRNA sequences from *B. tenagophila*,*B. t. guaibensis*,and *B. occidentalis* specimens collected in the São Paulo state,and Rio Grande do Sul state,Brazil to estimate the divergence of each species amongst the three members of the *tenagophila* complex and evaluate the potential of COI and 16S rRNA mitochondrial genes

*B. tenagophila* and *B. occidentallis* snails were collected along the banks of different streams of Paranapanema,Ribeira do Iguape,Paraíba do Sul,Tietê,and Litoral Basins at São Paulo,Brazil. *B.t. guaibensis* specimens were collected at Rio Grande do Sul,Brazil (Table 1). Two samples from Argentina were also analyzed. A total of 66 specimens were used in this study. Prior to molecular analysis snails were identified through morphological analysis,according to

Total genomic DNA was extracted from foot tissue of individual snails using DNeasy Tissue Kits (Qiagen®) and was preserved as DNA vouchers specimens in the DNA voucher collection of the Laboratory of Molecular Biology and Biochemical at SUCEN. Amplification of COI gene was attempted using LCO-1490 and HCO-2198 (Folmer et al.,1994). Amplifications (in 50µl of total volume) consisted of 10-100 ng of DNA,0.2 mM of each dNTP,0.10 µM of each primer and 1 U of *Taq* DNA polymerase (BioLabs) with the supplied buffer. The following PCR temperature profile was used: a initial 3 min step at 95ºC for denaturation,25 iterations of 1 min at 95ºC,1 min at 47ºC and 1 min 30 sec at 72ºC,and a final extension step for 7 min at 72ºC. The amplification profile of 16SrRNA fragment included an initial denaturation step at 94°C for 2 min. and 35 cycles of 30 sec at 94°C,30 sec at 54°C and 1 minute at 72°C,and a final step at 72°C for 5 minutes. Amplification of 16S gene was attempted using a Palumbi forward and reverse primer

PCR products were checked by agarose gel electrophoresis,cleaned using QIAquick PCR Purification Kits (Qiagen®). Cycle-sequencing of PCR products were carried out using terminal primers given above and the BigDye Terminator v3.1 kit of Applied Biosystems (ABI),purified using DyeEx-Kit (Qiagen®) according to the modified protocol and

populations that can be easily distinguished by molecular markers.

just one snail by self-fertilization.

for identification of closely related species.

Deslandes (1959) and Paraense (1975,1981,and 1984).

sequenced on an ABI 310 automated sequencer.

**2. Materials and methods**

**2.1 Molecular analysis**

(Palumbi, 1996).

Table 1. Collecting data,code and geographical identification of the specimen used in molecular study.

Sequences were corrected using Chromas (Technelysium Pty Ltd.),aligned in ClustalX 1.8 (Thompson et al. 1997). The alignments of COI datasets comprised sequences with no gaps or indels. The alignments were subsequently adjusted by eye using BioEdit 7.0 (Hall, 1999). Phylogenetic and molecular evolutionary analyses were conducted using *MEGA* version 5 (Tamura,Peterson,Stecher,Nei,Kumar 2011). Sequence divergence between species was estimated with Kimura 2 parameter model (K2P) (Kimura, 1980). The Neighbor-Joining (Saitou and Nei 1987) trees were obtained using K2P model and the support for the nodes was calculated using 1000 bootstrap replicates (Felsenstein, 1985). Nucleotide diversity (pi,Nei,1987,equations 10.5 or 10.6 and 10.7); and nucleotide divergence between sequence groups (Dxy,Nei,1987,equation 10.20 using Jukes and Cantor correction) were calculated using DnaSP v.5 (Librado and Rozas,2009). F*st* was estimated following Hudson et al. 1992.

#### **2.2 Morphology analysis**

The morphological analysis was based on 10 dissected specimens *B. tenagophila* from Arambaré,Rio Grande do Sul (lot DPE 9011); 13 dissected specimens *B. t. guaibensis* from Porto Alegre,Rio Grande do Sul (lot DPE 9008,Figure 3) and 31 dissected specimens *B. occidentalis* from Tremembé,São Paulo (lot DPE 9028). Only large and adult specimens were used for dissection and anatomic study.

Pattern of Genetic Divergence of Mitochondrial DNA Sequences in

*Biomphalaria tenagophila* Complex Species Based on Barcode and Morphological Analysis 299

Fig. 4. Optimal trees inferred for a 550 fragment COI mtDNA(A),sum of branch length= 0.09775397,and for a 314 nucleotides 16SrRNA,sum of branch length= 0.07712277. Bootstrap

Table 2. Mean Pairwise distance values calculated under Kimura´s 2 –parameter model

Species 1 Species 2 COI 16S *B.tenagophila B.t.guaibensis* 0.0436 0.0668 *B.tenagophila B.occidentalis* 0.0546 0.0478 *B.t.guaibensis B.occidentalis* 0.0362 0.0178

values (1000 replicates) are shown next to the branches.

(K2P) for 49 COI sequences and 46 16SrRNA sequences.

Fig. 3. A natural freshwater habitat at Porto Alegre where *B. tenagophila guaibensis* were collected. 29° 59' 52 3''S, 51° 15' 38. 6' W.

Snails were identified according to Paraense (1975). Soft parts were preserved under fixation as vouchers. The identification of the mollusks was done under the stereomicroscopy (Leica MZ 95) and was photographed with the image Program IMS 50. We took into consideration the morphology of the shell and soft parts,especially the reproductive system.

Three radulae and three jaws of the *B. occidentalis,B. tenagophila* and *B. t. guaibensis* were extracted and examined under a Scanning Electron Microscope LEO 440 at the Museum of the Zoology,São Paulo University (MZUSP).

### **3. Results**

#### **3.1 Molecular analysis**

The evolutionary relationships of *tenagophila* complex were inferred using Neighbor-Joining method for COI and 16SrRNA mitochondrial genes (Figure 4). The evolutionary distances depicted as branch lengths were computed using the Kimura 2p method using 550 nucleotides positions for the COI dataset and 314 nucleotide positions for the 16S rRNA dataset. The *tenagophila* complex sequences were retrieved in two well distinct and bootstrap supported branches: one with *B. occidentalis* clustering with *B. t. guaibensis*,and another distinct branch with the nominal *B.tenagophila* sequences.

For each component that belongs to *tenagophila* complex we obtained intraspecific identical sequences that support morphological taxonomy.

Two distinct groups of *B. tenagophila* were recovered in each analysis (COI and rRNA16S).

The estimates of genetic divergence (Table2) under Kimura 2p model over the COI and 16S between sequences pairs of the taxa analyzed show that *B. t. guaibensis* are more closely related with *B. occidentallis* than with *B. tenagophila*.

Fig. 3. A natural freshwater habitat at Porto Alegre where *B. tenagophila guaibensis* were

the morphology of the shell and soft parts,especially the reproductive system.

Snails were identified according to Paraense (1975). Soft parts were preserved under fixation as vouchers. The identification of the mollusks was done under the stereomicroscopy (Leica MZ 95) and was photographed with the image Program IMS 50. We took into consideration

Three radulae and three jaws of the *B. occidentalis,B. tenagophila* and *B. t. guaibensis* were extracted and examined under a Scanning Electron Microscope LEO 440 at the Museum of

The evolutionary relationships of *tenagophila* complex were inferred using Neighbor-Joining method for COI and 16SrRNA mitochondrial genes (Figure 4). The evolutionary distances depicted as branch lengths were computed using the Kimura 2p method using 550 nucleotides positions for the COI dataset and 314 nucleotide positions for the 16S rRNA dataset. The *tenagophila* complex sequences were retrieved in two well distinct and bootstrap supported branches: one with *B. occidentalis* clustering with *B. t. guaibensis*,and another

For each component that belongs to *tenagophila* complex we obtained intraspecific identical

Two distinct groups of *B. tenagophila* were recovered in each analysis (COI and rRNA16S). The estimates of genetic divergence (Table2) under Kimura 2p model over the COI and 16S between sequences pairs of the taxa analyzed show that *B. t. guaibensis* are more closely

collected. 29° 59' 52 3''S, 51° 15' 38. 6' W.

the Zoology,São Paulo University (MZUSP).

distinct branch with the nominal *B.tenagophila* sequences.

sequences that support morphological taxonomy.

related with *B. occidentallis* than with *B. tenagophila*.

**3. Results**

**3.1 Molecular analysis**

Fig. 4. Optimal trees inferred for a 550 fragment COI mtDNA(A),sum of branch length= 0.09775397,and for a 314 nucleotides 16SrRNA,sum of branch length= 0.07712277. Bootstrap values (1000 replicates) are shown next to the branches.

Table 2. Mean Pairwise distance values calculated under Kimura´s 2 –parameter model (K2P) for 49 COI sequences and 46 16SrRNA sequences.

Pattern of Genetic Divergence of Mitochondrial DNA Sequences in

genetic variation (Figure 6).

*Biomphalaria tenagophila* Complex Species Based on Barcode and Morphological Analysis 301

In order to examine the ability of the barcoding methodology to identify the three *Biomphalaria* taxa we plotted the intra and interspecific divergence of COI and 16S using a threshold of 3% minimum interspecific genetic variation and 3% maximum intraspecific

According the 3% threshold value for within and between species variation four quadrants are defined. Species on quadrant I (dark grey) are well-defined species. Groups on quadrant II (white colored) are composite species. Species on quadrant III (medium grey) are recent

Fig. 6. Minimum Interspecific distances versus Maximum intraspecific distances of COI and 16S sequences for *B. tenagophila*,*B. occidentalis* and *B. t. guaibensis* (according Ballard et al.,2009).

*B. tenagophila,B.t. guaibensis and B. occidentalis* had no significant differences among the number of whorls. The species had typically six to eight whorls. Otherwise,the shell aperture would be shorter and narrower in *B.t guaibensis* than in the other two species

**3.2 Comparative analysis of shell,reproductive system and radula**

analyzed (Figure 7).

divergent species,and species on quadrant IV (light grey) are misidentified species.

The Figure 5 shows that no overlap exists between intraspecific and interspecific distribution distance calculated under Kimura 2P. Indeed,the distributions of the COI and 16S genetic distances are very similar (Figure 5).

Fig. 5. Distribution of intraspecific variation genetic distance (in blue bars),and interspecific distance (in green bars) estimated by Kimura´s 2 –parameter model (K2P) for all the species studied

The Figure 5 shows that no overlap exists between intraspecific and interspecific distribution distance calculated under Kimura 2P. Indeed,the distributions of the COI and

Fig. 5. Distribution of intraspecific variation genetic distance (in blue bars),and interspecific distance (in green bars) estimated by Kimura´s 2 –parameter model (K2P) for all the species

studied

16S genetic distances are very similar (Figure 5).

In order to examine the ability of the barcoding methodology to identify the three *Biomphalaria* taxa we plotted the intra and interspecific divergence of COI and 16S using a threshold of 3% minimum interspecific genetic variation and 3% maximum intraspecific genetic variation (Figure 6).

According the 3% threshold value for within and between species variation four quadrants are defined. Species on quadrant I (dark grey) are well-defined species. Groups on quadrant II (white colored) are composite species. Species on quadrant III (medium grey) are recent divergent species,and species on quadrant IV (light grey) are misidentified species.

Fig. 6. Minimum Interspecific distances versus Maximum intraspecific distances of COI and 16S sequences for *B. tenagophila*,*B. occidentalis* and *B. t. guaibensis* (according Ballard et al.,2009).

#### **3.2 Comparative analysis of shell,reproductive system and radula**

*B. tenagophila,B.t. guaibensis and B. occidentalis* had no significant differences among the number of whorls. The species had typically six to eight whorls. Otherwise,the shell aperture would be shorter and narrower in *B.t guaibensis* than in the other two species analyzed (Figure 7).

Pattern of Genetic Divergence of Mitochondrial DNA Sequences in

*Biomphalaria tenagophila* Complex Species Based on Barcode and Morphological Analysis 303

Fig. 8. Reproductive System from *Biomphalaria tenagophila*. GA: albumen gland,GN: nidamental gland,OV: ovotestis,OVI: oviduct,OVIS: ovispermiduct,PP: prepuce,PR:

and size of the prepuce and the volume of prostata (Table 5,Figure 9).

*Biomphalaria tenagophila Biomphalaria tenagophila*

deferens,VP: vaginal pouch, VS: seminal vesicle.

Wider at the free end of the prepuce

than the prepuce

number of diverticula

Penis sheath Shorter and slenderer

Próstata Robust with a great

*B. occidentalis*.

Vaginal

prepuce

prostate,PS: penial sheath,SP: spermatheca,SPE: spermiduct,UT: uterus,VA: vagina,VD: vas

The hermaphrodite part did not present differences between *B.tenagophila,B.t. guaibensis* and

The differences between the species were mainly found in the male and female systems. The anatomical characters that clearly groups *B.tenagophila* and *B. t. guaibensis* in a same taxon is a well developed vaginal pouch located in the vaginal wall of this species,and also the shape

Vagina short Long and slender Long and slender

penis thick Long and slender Long and slender

Table 5. Differences among *B. tenagophila* species complex based on reproductive parameters

Wider at the free end of the prepuce

Shorter and slenderer than the prepuce

Robust with a great number of diverticula

pouch Well developed Well developed absent

spermatheca large small small

*guaibensis Biomphalaria occidentalis*

Same diameter along the prepuce

Shorter and slenderer than the prepuce

Refined with a small number of diverticula

Fig. 7. Shell. A: *B. t. guaibensis Biomphalaria tenagophila,* C: *B. occidentalis*

*Biomphalaria* snails can be distinguished based on differences in the shape and size of their male and female internal organs as described in Figure 8.

Fig. 7. Shell. A: *B. t. guaibensis Biomphalaria tenagophila,* C: *B. occidentalis*

male and female internal organs as described in Figure 8.

*Biomphalaria* snails can be distinguished based on differences in the shape and size of their