Background. Leprosy and schistosomiasis are co-endemic in certain areas of Brazil. It has been demonstrated that leprosy can present in its more infectious and debilitating multibacillary form if the patient concurrently has a helminth infection due to a change in immune response. We hypothesized that this association can be presented spatially and aspatially.
Methods. Aspatial logistic regression was applied to case-control data (n = 126) from a population in Minas Gerais, Brazil to estimate the association between schistosomiasis infection and leprosy infection, as well as multibacillary leprosy. The Kulldorff spatial scan statistic was used to identify clusters of infections and coinfections The Cuzick-Edwards method was used to test for heterogeneity in disease distribution. The local join count statistic was used to identify cluster cores of infections and coinfections by assessing for spatial autocorrelation.
Results. Leprosy was associated with a 4.97 (1.03, 24.09) times higher odds of schistosomiasis infection compared to non-cases. Multibacillary leprosy was associated with a 5.28 (95% CI 1.49, 18.75) times higher odds of schistosomiasis compared to paucibacillary cases. The spatial scan statistic identified schistosomiasis and coinfection clusters, while the local join count statistic identified leprosy and schistosomiasis clusters, albeit in the same general vicinity. The Cuzick-Edwards method results showed global spatial autocorrelation in leprosy cases and schistosomiasis cases. The spatial scan and local join count identified clusters of infected and coinfected individuals in the same section of the study area.
Conclusion. We successfully described aspatial and spatial associations between leprosy and schistosomiasis infection in a coendemic area in Minas Gerais, Brazil. Furthermore, we estimated the aspatial association between multibacillary leprosy and schistosomiasis infection which supports the hypothesis that schistosomiasis may be a factor in the sustained transmission of leprosy in co-endemic areas.
Table of Contents
Table of Contents
Figures and Figure Legends 23
Figure 1. The distribution of study participants in Minas Gerais, Brazil. 23
Figure 2. Bernoulli Spatial Scan Clusters of Schistosomiasis and Coinfections. 24
Figure 3. Local Join Count Cluster Cores. 25
Table 1. Demographic Data 26
Table 2. Logistic Regression Parameter Estimates 27
Table 3. Logistic Regression Odds Ratios for Schistosomiasis Infection 27
Table 4. Bernoulli Spatial Scan Clusters 28
About this Master's Thesis
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