RESUMEN
BACKGROUND: Visceral leishmaniasis is a neglected tropical disease of great importance to public health due to its wide distribution and close relationship with social and economic conditions. This study aimed to analyse the spatiotemporal dynamics of human visceral leishmaniasis (HVL) in an endemic state in the Northeast Region of Brazil and its spatial correlation with the Social Vulnerability Index (SVI) and the Municipal Human Development Index (MHDI). METHODS: The study included all confirmed cases of HVL in Bahia from 2010 to 2017. A joinpoint regression model was used for trend analysis. Incidence rates were smoothed by a local empirical Bayesian model. Global and local Moran indices and space-time scan statistics were used for identification of spatial clusters. Bivariate and multivariate analyses were carried out to investigate the relationship between HVL incidence and the SVI and MHDI. RESULTS: Cases of HVL demonstrated stationary behaviour during the period analysed. A significant association was observed between the HVL incidence rate and social vulnerability, with high-risk clusters concentrated in the central region of the state. CONCLUSIONS: HVL has a strong correlation with social vulnerability in the state of Bahia. This study may provide assistance in planning actions and organizing health services to combat HVL.
Asunto(s)
Leishmaniasis Visceral , Teorema de Bayes , Brasil/epidemiología , Humanos , Incidencia , Leishmaniasis Visceral/epidemiología , Enfermedades Desatendidas , Vulnerabilidad SocialRESUMEN
BACKGROUND: More than 95% of visceral leishmaniasis (VL) cases in Latin America occur in Brazil, most of them in the northeast. The objective of this study was to identify spatial clusters with the highest risks of VL and to analyse the temporal behaviour of the incidence and the effects of social vulnerability on the disease transmission dynamic in northeastern Brazil. METHODS: All confirmed cases registered as residents in the state of Pernambuco during the period from 2007 to 2017 were analysed. The local empirical Bayesian method was applied and the association -between the VL incidence rate and municipal social vulnerability was tested via classic multivariate regression. RESULTS: A total of 1186 new cases were registered during the study period. Spatial analysis showed heterogeneous distribution, with the highest rates observed in the São Francisco and Sertão mesoregions. Moreover, the main factors associated with VL were urban infrastructure, income and work. CONCLUSIONS: It was observed that spatial and temporal techniques are important tools for defining risk areas for VL, in conjunction with the evaluation of indexes of social vulnerability, which was shown to be an important factor for comprehending associations with VL in the state of Pernambuco.