Ronaldo Guilherme Carvalho Scholte

Soil-transmitted helminth infection in South America: a systematic review and geostatistical meta-analysis

BACKGROUND The four common soil-transmitted helminth species-Ascaris lumbricoides, Trichuris trichiura, and the two hookworm species Ancylostoma duodenale and Necator americanus-are endemic in South America, but their distribution, infection prevalence, and regional burden are poorly understood. We aimed to estimate the risk and number of people infected with A lumbricoides, T trichiura, and hookworm across South America. METHODS We did a systematic review of reports on the prevalence of soil-transmitted helminth infection in South America published up to May 14, 2012. We extracted and georeferenced relevant survey data and did a meta-analysis of the data to assess the geographical distribution of the infection risk with Bayesian geostatistical models. We used advanced Bayesian variable selection to identify environmental determinants that govern the distribution of soil-transmitted helminth infections. FINDINGS We screened 4085 scientific papers and identified 174 articles containing relevant survey prevalence data. We georeferenced 6948 survey locations and entered the data into the open-access Global Neglected Tropical Diseases database. Survey data were sparse for the south of the continent and for the western coast, and we identified no relevant information for Uruguay and little data for smaller countries such as Suriname, Guyana, French Guiana, and Ecuador. Population-adjusted prevalence of infection with A lumbricoides was 15·6%, with T trichiura was 12·5%, and with hookworm was 11·9% from 2005 onwards. Risks of contracting soil-transmitted helminth infection have substantially reduced since 2005 (odds ratio 0·47 [95% Bayesian credible interval 0·46-0·47] for A lumbricoides, 0·54 [0·54-0·55] for T trichiura, and 0·58 [0·58-0·59] for hookworm infection). INTERPRETATION Our findings offer important baseline support for spatial targeting of soil-transmitted helminthiasis control, and suggest that more information about the prevalence of soil-transmitted helminth infection is needed, especially in countries in which we estimate prevalence of infection to be high but for which current data are scarce. FUNDING UBS Optimus Foundation and Brazilian Swiss Joint Research Programme (BSJRP 011008).

Schistosomiasis risk estimation in Minas Gerais State, Brazil, using environmental data and GIS techniques

The influence of climate and environmental variables to the distribution of schistosomiasis has been assessed in several previous studies. Also Geographical Information System (GIS), is a tool that has been recently tested for better understanding the spatial disease distribution. The objective of this paper is to further develop the GIS technology for modeling and control of schistosomiasis using meteorological and social variables and introducing new potential environmental-related variables, particularly those produced by recently launched orbital sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Shuttle Radar Topography Mission (SRTM). Three different scenarios have been analyzed, and despite of not quite large determination factor, the standard deviation of risk estimates was considered adequate for public health needs. The main variables selected as important for modeling purposes was topographic elevation, summer minimum temperature, the NDVI vegetation index, and the social index HDI91.

Bayesian Geostatistical Modeling of Leishmaniasis Incidence in Brazil

Background Leishmaniasis is endemic in 98 countries with an estimated 350 million people at risk and approximately 2 million cases annually. Brazil is one of the most severely affected countries. Methodology We applied Bayesian geostatistical negative binomial models to analyze reported incidence data of cutaneous and visceral leishmaniasis in Brazil covering a 10-year period (2001–2010). Particular emphasis was placed on spatial and temporal patterns. The models were fitted using integrated nested Laplace approximations to perform fast approximate Bayesian inference. Bayesian variable selection was employed to determine the most important climatic, environmental, and socioeconomic predictors of cutaneous and visceral leishmaniasis. Principal Findings For both types of leishmaniasis, precipitation and socioeconomic proxies were identified as important risk factors. The predicted number of cases in 2010 were 30,189 (standard deviation [SD]: 7,676) for cutaneous leishmaniasis and 4,889 (SD: 288) for visceral leishmaniasis. Our risk maps predicted the highest numbers of infected people in the states of Minas Gerais and Pará for visceral and cutaneous leishmaniasis, respectively. Conclusions/Significance Our spatially explicit, high-resolution incidence maps identified priority areas where leishmaniasis control efforts should be targeted with the ultimate goal to reduce disease incidence.

Modelling the geographical distribution of soil-transmitted helminth infections in Bolivia

BackgroundThe prevalence of infection with the three common soil-transmitted helminths (i.e. Ascaris lumbricoides, Trichuris trichiura, and hookworm) in Bolivia is among the highest in Latin America. However, the spatial distribution and burden of soil-transmitted helminthiasis are poorly documented.MethodsWe analysed historical survey data using Bayesian geostatistical models to identify determinants of the distribution of soil-transmitted helminth infections, predict the geographical distribution of infection risk, and assess treatment needs and costs in the frame of preventive chemotherapy. Rigorous geostatistical variable selection identified the most important predictors of A. lumbricoides, T. trichiura, and hookworm transmission.ResultsResults show that precipitation during the wettest quarter above 400 mm favours the distribution of A. lumbricoides. Altitude has a negative effect on T. trichiura. Hookworm is sensitive to temperature during the coldest month. We estimate that 38.0%, 19.3%, and 11.4% of the Bolivian population is infected with A. lumbricoides, T. trichiura, and hookworm, respectively. Assuming independence of the three infections, 48.4% of the population is infected with any soil-transmitted helminth. Empirical-based estimates, according to treatment recommendations by the World Health Organization, suggest a total of 2.9 million annualised treatments for the control of soil-transmitted helminthiasis in Bolivia.ConclusionsWe provide estimates of soil-transmitted helminth infections in Bolivia based on high-resolution spatial prediction and an innovative variable selection approach. However, the scarcity of the data suggests that a national survey is required for more accurate mapping that will govern spatial targeting of soil-transmitted helminthiasis control.

Spatial distribution of Biomphalaria mollusks at São Francisco River Basin, Minas Gerais, Brazil, using geostatistical procedures.

Geostatistics is used in this work to make inferences about the presence of the species of Biomphalaria (B. glabrata, B. tenagophila and/or B. straminea), intermediate hosts of Schistosoma mansoni, at the São Francisco River Basin, in Minas Gerais, Brazil. One of these geostatistical procedures, known as indicator kriging, allows the classification of categorical data, in areas where the data are not available, using a punctual sample set. The result is a map of species and risk area definition. More than a single map of the categorical attribute, the procedure also permits the association of uncertainties of the stochastic model, which can be used to qualify the inferences. In order to validate the estimated data of the risk map, a fieldwork in five municipalities was carried out. The obtained results showed that indicator kriging is a rather robust tool since it presented a very good agreement with the field findings. The obtained risk map can be thought as an auxiliary tool to formulate proper public health strategies, and to guide other fieldwork, considering the places with higher occurrence probability of the most important snail species. Also, the risk map will enable better resource distribution and adequate policies for the mollusk control. This methodology will be applied to other river basins to generate a predictive map for Biomphalaria species distribution for the entire state of Minas Gerais.

Analysis and estimative of schistosomiasis prevalence for the state of Minas Gerais, Brazil, using multiple regression with social and environmental spatial data

The aim of this work is to establish a relationship between schistosomiasis prevalence and social-environmental variables, in the state of Minas Gerais, Brazil, through multiple linear regression. The final regression model was established, after a variables selection phase, with a set of spatial variables which contains the summer minimum temperature, human development index, and vegetation type variables. Based on this model, a schistosomiasis risk map was built for Minas Gerais.

Angiostrongylus cantonensis (Nematode: Metastrongyloidea) in molluscs from harbour areas in Brazil

Angiostrongylus cantonensis is the most common aetiological agent of human eosinophilic meningoencephalitis. Following a report indicating the presence of this parasite in Brazil in 2007, the present study was undertaken to investigate the presence of A. cantonensis in the surrounding Brazilian port areas. In total, 30 ports were investigated and the following molluscs were identified: Achatina fulica, Belocaulus sp., Bradybaena similaris sp., Cyclodontina sp., Helix sp., Leptinaria sp., Melampus sp., Melanoides tuberculata, Phyllocaulis sp., Pomacea sp., Pseudoxychona sp., Rhinus sp., Sarasinula marginata, Streptaxis sp., Subulina octona, Succinea sp., Tomigerus sp., Wayampia sp. and specimens belonging to Limacidae and Orthalicinae. Digestion and sedimentation processes were performed and the sediments were examined. DNA was extracted from the obtained larvae and the internal transcribed spacer region 2 was analysed by polymerase chain reaction-restriction fragment length polymorphism after digestion with the endonuclease ClaI. Of the 30 ports investigated in this study, 11 contained molluscs infected with A. cantonensis larvae. The set of infected species consisted of S. octona, S. marginata, A. fulica and B. similaris. A total of 36.6% of the investigated ports were positive for A. cantonensis, indicating a wide distribution of this worm. It remains uncertain when and how A. cantonensis was introduced into South America.

A geoprocessing approach for studying and controlling schistosomiasis in the state of Minas Gerais, Brazil

Geographical information systems (GIS) are tools that have been recently tested for improving our understanding of the spatial distribution of disease. The objective of this paper was to further develop the GIS technology to model and control schistosomiasis using environmental, social, biological and remote-sensing variables. A final regression model (R(2) = 0.39) was established, after a variable selection phase, with a set of spatial variables including the presence or absence of Biomphalaria glabrata, winter enhanced vegetation index, summer minimum temperature and percentage of houses with water coming from a spring or well. A regional model was also developed by splitting the state of Minas Gerais (MG) into four regions and establishing a linear regression model for each of the four regions: 1 (R(2) = 0.97), 2 (R(2) = 0.60), 3 (R(2) = 0.63) and 4 (R(2) = 0.76). Based on these models, a schistosomiasis risk map was built for MG. In this paper, geostatistics was also used to make inferences about the presence of Biomphalaria spp. The result was a map of species and risk areas. The obtained risk map permits the association of uncertainties, which can be used to qualify the inferences and it can be thought of as an auxiliary tool for public health strategies.

Predictive risk mapping of schistosomiasis in Brazil using Bayesian geostatistical models.

Schistosomiasis is one of the most common parasitic diseases in tropical and subtropical areas, including Brazil. A national control programme was initiated in Brazil in the mid-1970s and proved successful in terms of morbidity control, as the number of cases with hepato-splenic involvement was reduced significantly. To consolidate control and move towards elimination, there is a need for reliable maps on the spatial distribution of schistosomiasis, so that interventions can target communities at highest risk. The purpose of this study was to map the distribution of Schistosoma mansoni in Brazil. We utilized readily available prevalence data from the national schistosomiasis control programme for the years 2005-2009, derived remotely sensed climatic and environmental data and obtained socioeconomic data from various sources. Data were collated into a geographical information system and Bayesian geostatistical models were developed. Model-based maps identified important risk factors related to the transmission of S. mansoni and confirmed that environmental variables are closely associated with indices of poverty. Our smoothed predictive risk map, including uncertainty, highlights priority areas for intervention, namely the northern parts of North and Southeast regions and the eastern part of Northeast region. Our predictive risk map provides a useful tool for to strengthen existing surveillance-response mechanisms.

The Estrada Real project and endemic diseases: the case of schistosomiasis, geoprocessing and tourism

Geographical Information System (GIS) is a tool that has recently been applied to better understand spatial disease distributions. Using meteorological, social, sanitation, mollusc distribution data and remote sensing variables, this study aimed to further develop the GIS technology by creating a model for the spatial distribution of schistosomiasis and to apply this model to an area with rural tourism in the Brazilian state of Minas Gerais (MG). The Estrada Real, covering about 1,400 km, is the largest and most important Brazilian tourism project, involving 163 cities in MG with different schistosomiasis prevalence rates. The model with three variables showed a R(2) = 0.34, with a standard deviation of risk estimated adequate for public health needs. The main variables selected for modelling were summer vegetation, summer minimal temperature and winter minimal temperature. The results confirmed the importance of Remote Sensing data and the valuable contribution of GIS in identifying priority areas for intervention in tourism regions which are endemic to schistosomiasis.