Spatiotemporal analysis of visceral leishmaniasis in the state of Bahia, Brazil, 2007 - 2017: Socioeconomic and environmental aspects
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Background. Visceral leishmaniasis (VL) is a neglected zoonosis caused by the parasite Leishmania infantum, present throughout the world. In Bahia, a state in the north-eastern region of Brazil, there is a high prevalence.
Objectives. To evaluate the role of socioeconomic and environmental factors in the epidemiology of VL though spatiotemporal analysis.
Methods. Epidemiological data for Bahia were extracted from the System for Notification of Diseases of the Ministry of Health (SINAN), data on prolonged droughts were obtained through the Civil Bureau of the Government of the State of Bahia, and socioeconomic indicators were obtained through the Socioeconomic Performance Index (IPESE) and DATASUS, the Brazilian public unified health system department responsible for providing health data. Relative risk for VL was calculated for age classes, clinical manifestations, comorbidities and deaths, while spatial analysis relied on kernel density analyses to determine spatial patterns. Correlations between socioeconomic indices and epidemiological markers were explored.
Results. From 2007 to 2017, 3 847 new VL cases and 227 deaths were reported in Bahia. The mean incidence was 1.73 per 100 000 population. Cases and deaths were concentrated in the central-northern region of the state, a relatively poor area with a dry/semi-arid climate. Cases were more frequent in urban areas (54.1%), in males (60.7%) and in children aged <10 years (48.2%). Municipalities that declared a state of emergency due to prolonged drought (SECO dataset) and IPESE were positively correlated (p=0.0001 and p=0.013, respectively) with VL cases reported in all age groups. Deaths from VL were significantly correlated with the Gini index (p=0.043).
Conclusion. Our results paint a picture of socioeconomic and climatic vulnerability as determinants of VL epidemiology. Addressing socioeconomic inequalities and enhancing models to predict prolonged drought are actions that should be included in the prevention and control of VL, given that vulnerable populations will be the most affected by the frequency and intensity of extreme climatic events.
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