Lower Richness of Small Wild Mammal Species and Chagas Disease Risk

A new epidemiological scenario involving the oral transmission of Chagas disease, mainly in the Amazon basin, requires innovative control measures. Geospatial analyses of the Trypanosoma cruzi transmission cycle in the wild mammals have been scarce. We applied interpolation and map algebra methods to evaluate mammalian fauna variables related to small wild mammals and the T. cruzi infection pattern in dogs to identify hotspot areas of transmission. We also evaluated the use of dogs as sentinels of epidemiological risk of Chagas disease. Dogs (n = 649) were examined by two parasitological and three distinct serological assays. kDNA amplification was performed in patent infections, although the infection was mainly sub-patent in dogs. The distribution of T. cruzi infection in dogs was not homogeneous, ranging from 11–89% in different localities. The interpolation method and map algebra were employed to test the associations between the lower richness in mammal species and the risk of exposure of dogs to T. cruzi infection. Geospatial analysis indicated that the reduction of the mammal fauna (richness and abundance) was associated with higher parasitemia in small wild mammals and higher exposure of dogs to infection. A Generalized Linear Model (GLM) demonstrated that species richness and positive hemocultures in wild mammals were associated with T. cruzi infection in dogs. Domestic canine infection rates differed significantly between areas with and without Chagas disease outbreaks (Chi-squared test). Geospatial analysis by interpolation and map algebra methods proved to be a powerful tool in the evaluation of areas of T. cruzi transmission. Dog infection was shown to not only be an efficient indicator of reduction of wild mammalian fauna richness but to also act as a signal for the presence of small wild mammals with high parasitemia. The lower richness of small mammal species is discussed as a risk factor for the re-emergence of Chagas disease

Geographical information system and predictive risk maps of urinary schistosomiasis in Ogun State, Nigeria

<p>Abstract</p> <p>Background</p> <p>The control of urinary schistosomiasis in Ogun State, Nigeria remains inert due to lack of reliable data on the geographical distribution of the disease and the population at risk. To help in developing a control programme, delineating areas of risk, geographical information system and remotely sensed environmental images were used to developed predictive risk maps of the probability of occurrence of the disease and quantify the risk for infection in Ogun State, Nigeria.</p> <p>Methods</p> <p>Infection data used were derived from carefully validated morbidity questionnaires among primary school children in 2001–2002, in which school children were asked among other questions if they have experienced "blood in urine" or urinary schistosomiasis. The infection data from 1,092 schools together with remotely sensed environmental data such as rainfall, vegetation, temperature, soil-types, altitude and land cover were analysis using binary logistic regression models to identify environmental features that influence the spatial distribution of the disease. The final regression equations were then used in Arc View 3.2a GIS software to generate predictive risk maps of the distribution of the disease and population at risk in the state.</p> <p>Results</p> <p>Logistic regression analysis shows that the only significant environmental variable in predicting the presence and absence of urinary schistosomiasis in any area of the State was Land Surface Temperature (LST) (B = 0.308, p = 0.013). While LST (B = -0.478, p = 0.035), rainfall (B = -0.006, p = 0.0005), ferric luvisols (B = 0.539, p = 0.274), dystric nitosols (B = 0.133, p = 0.769) and pellic vertisols (B = 1.386, p = 0.008) soils types were the final variables in the model for predicting the probability of an area having an infection prevalence equivalent to or more than 50%. The two predictive risk maps suggest that urinary schistosomiasis is widely distributed and occurring in all the Local Government Areas (LGAs) in State. The high-risk areas (≥ 50% prevalence) however, are confined to scatter foci in the north western part of the State. The model also estimated that 98.99% of schools aged children (5–14 years) are living in areas suitable for urinary schistosomiasis transmission and are at risk of infection.</p> <p>Conclusion</p> <p>The risk maps developed will hopefully be useful to the state health officials, by providing them with detailed distribution of urinary schistosomiasis, help to delineate areas for intervention, assesses population at risk thereby helping in optimizing scarce resources.</p

Bayesian Spatio-Temporal Modeling of Schistosoma japonicum Prevalence Data in the Absence of a Diagnostic ‘Gold’ Standard

Schistosomiasis is a serious public health problem in the People's Republic of China and elsewhere, and mapping of risk areas is important for guiding control interventions. Here, a 10-year surveillance database from Dangtu County in the southeastern part of the People's Republic of China was utilized for modeling the spatial and temporal distribution of infections in relation to environmental features and socioeconomic factors. Disease surveillance was done on the basis of a serological test, and we explicitly considered the imperfect sensitivity and specificity of the test when modeling the ‘true’ infection prevalence of Schistosoma japonicum. We then produced a risk map for S. japonicum transmission, which can assist decision making for local control interventions. Our work emphasizes the importance of accounting for the uncertainty in the diagnosis of schistosomiasis, and the potential of predicting the spatial and temporal distribution of the disease when using a Bayesian modeling framework. Our study can therefore serve as a template for future risk profiling of neglected tropical diseases studies, particularly when exploring spatial and temporal disease patterns in relation to environmental and socioeconomic factors, and how to account for the influence of diagnostic uncertainty

The role of population movement in the epidemiology and control of schistosomiasis in Brazil: a preliminary typology of population movement

This paper examines recent developments in migration studies. It reviews literature related to the potential role of internal population movement in the occurrence of schistosomiasis in Brazil and modifies Prothero's typology of population movement for use in Brazil. This modified classification system may contribute to a better understanding of schistosome transmission as well as improved research and control programs. The results of this study indicate that population movement in Brazil primarily involves economically-motivated rural-urban and interregional movement. However, several movement patterns have become increasingly important in recent years as a result of changing socioeconomic and urbanisation dynamics. These patterns include urban-urban, intracity and urban-rural movement as well as the movement of environmental refugees and tourists. Little is known about the epidemiological significance of these patterns. This paper also highlights the role of social networks in the decision to migrate and to settle. Prothero's classic population movement typology categorises movement as either one-way migrations or circulations and examines them along spatial and temporal scales. However, the typology must be modified as epidemiological information about new patterns becomes available. This paper identifies areas that require further research and offers recommendations that can improve the measurement and spatial analysis of the relationship between population movement and schistosomiasis

A comparative evaluation of endemic and non-endemic region of visceral leishmaniasis (Kala-azar) in India with ground survey and space technology

In visceral leishmaniasis, phlebotomine vectors are targets for control measures. Understanding the ecosystem of the vectors is a prerequisite for creating these control measures. This study endeavours to delineate the suitable locations of Phlebotomus argentipes with relation to environmental characteristics between endemic and non-endemic districts in India. A cross-sectional survey was conducted on 25 villages in each district. Environmental data were obtained through remote sensing images and vector density was measured using a CDC light trap. Simple linear regression analysis was used to measure the association between climatic parameters and vector density. Using factor analysis, the relationship between land cover classes and P. argentipes density among the villages in both districts was investigated. The results of the regression analysis indicated that indoor temperature and relative humidity are the best predictors for P. argentipes distribution. Factor analysis confirmed breeding preferences for P. argentipes by landscape element. Minimum Normalised Difference Vegetation Index, marshy land and orchard/settlement produced high loading in an endemic region, whereas water bodies and dense forest were preferred in non-endemic sites. Soil properties between the two districts were studied and indicated that soil pH and moisture content is higher in endemic sites compared to non-endemic sites. The present study should be utilised to make critical decisions for vector surveillance and controlling Kala-azar disease vectors