2018
DOI: 10.1093/trstmh/try058
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Visceral leishmaniasis and vulnerability conditions in an endemic urban area of Northeastern Brazil

Abstract: Addressing the local vulnerability conditions is important to the understanding of the distribution of visceral leishmaniasis, identifying the most susceptible areas, and planning control strategies.

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Cited by 15 publications
(10 citation statements)
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“…In spite of the efforts to control VL in Brazil, the disease is still expanding throughout the country [21][22][23][24][25][26]. Among the interventions recommended in VLSCP, canine euthanasia is the least approved by the community for obvious reasons.…”
Section: Discussionmentioning
confidence: 99%
“…In spite of the efforts to control VL in Brazil, the disease is still expanding throughout the country [21][22][23][24][25][26]. Among the interventions recommended in VLSCP, canine euthanasia is the least approved by the community for obvious reasons.…”
Section: Discussionmentioning
confidence: 99%
“…The global Moran's I index was computed to analyse the spatial autocorrelation using a first order proximity matrix, which was expanded upon using contiguity criterion. This index ranges from -1 to +1, with positive values indicating positive spatial autocorrelation and [23]. Statistical significances were identified using Monte Carlo simulations with 999 permutations.…”
Section: Spatial Cluster Analysismentioning
confidence: 99%
“…Similar to univariate analysis, the bivariate global Moran's does not reveal spatial clustering [13,29]. Thus, the bivariate LISA analysis was employed to determine the degree of spatial correlation of the data in relation to its neighbours [23], generating a scatter plot with four quadrants [30]: Q1 (municipalities with high VL incidence rates and high social vulnerability in neighbouring municipalities), Q2 (municipalities with low VL incidence rate and low social vulnerability in neighbouring municipalities), Q3 (municipalities with high VL incidence rates and low social vulnerability in neighbouring municipalities) and Q4 (municipalities with low PLOS NEGLECTED TROPICAL DISEASES VL incidence rates and high social vulnerability in neighbouring municipalities). These clusters were depicted in Moran maps and only the statistically significant results were considered (p<0.05).…”
Section: Bivariate Spatial Cluster Analysismentioning
confidence: 99%
“…These data are the most updated data accessible at this spatial scale and the one with the most range of socioeconomic variables. The methodology used to compute the social vulnerability index was described by Freitas et al [69]. They used a composite vulnerability index constructed from the three synthetic indicators: (i) social structure vulnerability indicator (SSVI), which computes household density and the proportion of literate persons responsible for the households; (ii) household structure vulnerability indicator (HSVI), which computes the proportion of water supply, bathrooms, garbage collection, and electricity in the households; and (iii) urban infrastructure vulnerability indicator (UIV), which computes the proportion of public lighting, paved roads, and arborization in households' surrounding.…”
Section: Remote Sensing and Census Datamentioning
confidence: 99%