Spatio-temporal analysis of leprosy risks in a municipality in the state of Mato Grosso-Brazilian Amazon: results from the leprosy post-exposure prophylaxis program in Brazil.

BACKGROUND: Leprosy post-exposure prophylaxis (LPEP) with single dose rifampicin (SDR) can be integrated into different leprosy control program set-ups once contact tracing has been established. We analyzed the spatio-temporal changes in the distribution of index cases (IC) and co-prevalent cases among contacts of leprosy patients (CP) over the course of the LPEP program in one of the four study areas in Brazil, namely the municipality of Alta Floresta, state of Mato Grosso, in the Brazilian Amazon basin. METHODS: Leprosy cases were mapped, and socioeconomic indicators were evaluated to explain the leprosy distribution of all leprosy cases diagnosed in the period 2016-2018. Data were obtained on new leprosy cases [Notifiable diseases information system (Sinan)], contacts traced by the LPEP program, and socioeconomic variables [Brazilian Institute of Geography and Statistics (IBGE)]. Kernel, SCAN, factor analysis and spatial regression were applied to analyze changes. RESULTS: Overall, the new case detection rate (NCDR) was 20/10 000 inhabitants or 304 new cases, of which 55 were CP cases among the 2076 examined contacts. Changes over time were observed in the geographic distribution of cases. The highest concentration of cases was observed in the northeast of the study area, including one significant cluster (Relative risk = 2.24; population 27 427, P-value < 0.001) in an area characterized by different indicators associated with poverty as identified through spatial regression (Coefficient 3.34, P-value = 0.01). CONCLUSIONS: The disease distribution was partly explained by poverty indicators. LPEP influences the spatial dynamic of the disease and results highlighted the relevance of systematic contact surveillance for leprosy elimination.

Social school contacts of multibacillary leprosy cases in children living in the hyperendemic region of the Midwest of Brazil.

OBJECTIVE: To analyze the prevalence of Mycobacterium leprae detection and the associated factors among social contacts in the school environment of multibacillary cases living in a hyperendemic municipality of the state of Mato Grosso. METHODS: Cross-sectional study with 236 social contacts of multibacillary leprosy from public schools and residents in Cuiabá (Mato Grosso) in 2018. The sources of information were interviews and nasal swab tests for molecular analysis by polymerase chain reaction - PCR. For the prevalence ratio estimates, crude and adjusted analyses were performed using robust Poisson regression and their respective confidence intervals (95% CI). The ArcGIS 9.1 software was used for the geographic distribution analyses. RESULTS: The prevalence of detection of M. leprae in social contacts was 14%. A total of 63.6% of the schools surveyed had 5.1% to 50% of the social contacts of leprosy with positive PCR. The analysis of the geographic distribution in the neighborhoods showed a high prevalence of infection, being higher than 50% in some localities. The highest proportion of positive results occurred in the northern region of the city and from a precarious socioeconomic class. CONCLUSION: The results showed a high prevalence of detection of M. leprae among social contacts in areas with poor socioeconomic conditions. In these regions, there is a greater risk of infection and of getting sick.

Geographic information systems and logistic regression for high-resolution malaria risk mapping in a rural settlement of the southern Brazilian Amazon.

BACKGROUND: In Brazil, 99% of the cases of malaria are concentrated in the Amazon region, with high level of transmission. The objectives of the study were to use geographic information systems (GIS) analysis and logistic regression as a tool to identify and analyse the relative likelihood and its socio-environmental determinants of malaria infection in the Vale do Amanhecer rural settlement, Brazil. METHODS: A GIS database of georeferenced malaria cases, recorded in 2005, and multiple explanatory data layers was built, based on a multispectral Landsat 5 TM image, digital map of the settlement blocks and a SRTM digital elevation model. Satellite imagery was used to map the spatial patterns of land use and cover (LUC) and to derive spectral indices of vegetation density (NDVI) and soil/vegetation humidity (VSHI). An Euclidian distance operator was applied to measure proximity of domiciles to potential mosquito breeding habitats and gold mining areas. The malaria risk model was generated by multiple logistic regression, in which environmental factors were considered as independent variables and the number of cases, binarized by a threshold value was the dependent variable. RESULTS: Out of a total of 336 cases of malaria, 133 positive slides were from inhabitants at Road 08, which corresponds to 37.60% of the notifications. The southern region of the settlement presented 276 cases and a greater number of domiciles in which more than ten cases/home were notified. From these, 102 (30.36%) cases were caused by Plasmodium falciparum and 174 (51.79%) cases by Plasmodium vivax. Malaria risk is the highest in the south of the settlement, associated with proximity to gold mining sites, intense land use, high levels of soil/vegetation humidity and low vegetation density. CONCLUSIONS: Mid-resolution, remote sensing data and GIS-derived distance measures can be successfully combined with digital maps of the housing location of (non-) infected inhabitants to predict relative likelihood of disease infection through the analysis by logistic regression. Obtained findings on the relation between malaria cases and environmental factors should be applied in the future for land use planning in rural settlements in the Southern Amazon to minimize risks of disease transmission.

[Migration and Hansen's disease in Mato Grosso]. / Migração e hanseníase em Mato Grosso.

Studies on medical geography about leprosy discuss the role of the detailed report of the occupation of the territories as a basis of the permanence of leprosy focus. In Brazil, the states that present the highest rates of detection historically are in the Amazon region, which shows an uneven regional evolution of the disease. This paper analyzes the evolution of leprosy contextualizing the migratory processes that occurred in the State of Mato Grosso since the second half of the 20th century. The economic dynamism that occurred in the State in the 1970s, 1980s and 1990s caused population growth rates higher than the national average. The data analyzed permitted an association between the evolution of leprosy and the process of occupation of the mato-grossense territory. However, the permanence of leprosy in the municipalities of the Baixada Cuiabana, as well as in other municipalities that lost population, seem to point to the existence of geographic contexts of different vulnerability to the social production of the disease in the tate. The migration would explain the appearance and evolution of leprosy. However, we consider that the maintenance of the endemic can be associated to contextual factors related to environment.

Spatial patterns of malaria in a land reform colonization project, Juruena municipality, Mato Grosso, Brazil.

BACKGROUND: In Brazil, 99% of malaria cases are concentrated in the Amazon, and malaria's spatial distribution is commonly associated with socio-environmental conditions on a fine landscape scale. In this study, the spatial patterns of malaria and its determinants in a rural settlement of the Brazilian agricultural reform programme called "Vale do Amanhecer" in the northern Mato Grosso state were analysed. METHODS: In a fine-scaled, exploratory ecological study, geocoded notification forms corresponding to malaria cases from 2005 were compared with spectral indices, such as the Normalized Difference Vegetation Index (NDVI) and the third component of the Tasseled Cap Transformation (TC_3) and thematic layers, derived from the visual interpretation of multispectral TM-Landsat 5 imagery and the application of GIS distance operators. RESULTS: Of a total of 336 malaria cases, 102 (30.36%) were caused by Plasmodium falciparum and 174 (51.79%) by Plasmodium vivax. Of all the cases, 37.6% (133 cases) were from residents of a unique road. In total, 276 cases were reported for the southern part of the settlement, where the population density is higher, with notification rates higher than 10 cases per household. The local landscape mostly consists of open areas (38.79 km²). Training forest occupied 27.34 km² and midsize vegetation 7.01 km². Most domiciles with more than five notified malaria cases were located near areas with high NDVI values. Most domiciles (41.78%) and malaria cases (44.94%) were concentrated in areas with intermediate values of the TC_3, a spectral index representing surface and vegetation humidity. CONCLUSIONS: Environmental factors and their alteration are associated with the occurrence and spatial distribution of malaria cases in rural settlements.

Habitat suitability mapping of Anopheles darlingi in the surroundings of the Manso hydropower plant reservoir, Mato Grosso, Central Brazil.

BACKGROUND: Hydropower plants provide more than 78 % of Brazil's electricity generation, but the country's reservoirs are potential new habitats for main vectors of malaria. In a case study in the surroundings of the Manso hydropower plant in Mato Grosso state, Central Brazil, habitat suitability of Anopheles darlingi was studied. Habitat profile was characterized by collecting environmental data. Remote sensing and GIS techniques were applied to extract additional spatial layers of land use, distance maps, and relief characteristics for spatial model building. RESULTS: Logistic regression analysis and ROC curves indicate significant relationships between the environment and presence of An. darlingi. Probabilities of presence strongly vary as a function of land cover and distance from the lake shoreline. Vector presence was associated with spatial proximity to reservoir and semi-deciduous forests followed by Cerrado woodland. Vector absence was associated with open vegetation formations such as grasslands and agricultural areas. We suppose that non-significant differences of vector incidences between rainy and dry seasons are associated with the availability of anthropogenic breeding habitat of the reservoir throughout the year. CONCLUSION: Satellite image classification and multitemporal shoreline simulations through DEM-based GIS-analyses consist in a valuable tool for spatial modeling of A. darlingi habitats in the studied hydropower reservoir area. Vector presence is significantly increased in forested areas near reservoirs in bays protected from wind and wave action. Construction of new reservoirs under the tropical, sub-humid climatic conditions should therefore be accompanied by entomologic studies to predict the risk of malaria epidemics.