Spatial-temporal risk clusters, social vulnerability, and identification of priority areas for surveillance and control of cutaneous leishmaniasis in Maranhão, Brazil: an ecological study.

Cutaneous leishmaniasis (CL) is a neglected disease widely distributed in Maranhão, Brazil and presents a significant public health problem. However, its transmission dynamics and determining factors are not clearly understood. In this context, geospatial technologies help interpret the process. This study, then, characterized the space-time dynamics and the influence of social vulnerability on CL in an endemic area in Northeast Brazil. This is an ecological study about new cases of CL in Maranhão, from 2007 to 2020, obtained directly from the Notifiable Diseases Information System. The incidence rate was smoothed using a spatial empirical Bayesian method. Subsequently, global and local Moran statistics and their association with social vulnerability indicators were determined. Disease distribution was not random but grouped in space and time. All Social Vulnerability Index domains were positively correlated with the CL incidence. A likely cluster was detected in western Maranhão (P < 0.001), which encompassed 18 municipalities, from January 2007 to December 2013, with a high relative risk (5.06). The research findings suggest that planning public health actions and allocating resources should be prioritized in these areas to help effectively reduce the incidence of the disease.

Spatiotemporal Distribution of Leishmaniasis in an Endemic Area of Northeast Brazil: Implications for Intervention Actions.

This study aimed to analyze the spatiotemporal distribution of leishmaniases, and contribute to the knowledge of their epidemiological dynamics from 2007 to 2017 in the municipality of Caxias, Maranhão, Northeast Brazil. Data on American tegumentary leishmaniasis (ATL) and human visceral leishmaniasis (HVL) were obtained in the Epidemiological Surveillance Sector of Caxias, while data on canine visceral leishmaniasis (CVL) were obtained in the Zoonoses Surveillance Unit. For data analysis and spatial representation of leishmaniasis cases, the geoprocessing of the data was performed, and the geometric features of the state of Maranhão, Caxias, and the disease registration sites were obtained from the shapefile database of the Brazilian Institute of Geography and Statistics. Geostatistics was used to create maps based on the Kernel density method, starting from the points, producing a raster file for each case with several data frames, allowing the instantaneous comparison of the phenomena. During the study period, ATL, HVL, and CVL were reported in Caxias, accounting for 114,304 and 8,498 cases, respectively. The geoprocessing analysis showed that leishmaniasis is widely distributed in the urban area of Caxias. However, there are risk areas for the transmission of these diseases to humans and dogs, associated with deforestation and urban expansion, and may vary over time. Preventive measures must focus on risk areas, including conservation efforts and urban planning, in order to reduce the transmission of leishmaniases.