Hierarchical spatiotemporal modeling of human visceral leishmaniasis in Rio Grande do Norte, Brazil

Abstract

Visceral leishmaniasis (VL) is a neglected tropical disease that is globally distributed and has the potential to cause very serious illness. Prior literature highlights the emergence and spread of VL is influenced by multiple factors, such as socioeconomic status, sanitation levels or animal and human reservoirs. The study aimed to retrospectively investigate the presence and infectiousness of VL in Rio Grande do Norte (RN), Brazil between 2007 and 2020. We applied a hierarchical Bayesian approach to estimate municipality-specific relative risk of VL across space and time. The results show evidence that lower socioeconomic status is connected to higher municipality-specific VL risk. Overall, estimates reveal spatially heterogeneous VL risks in RN, with a high probability that VL risk for municipalities within the West Potiguar mesoregion are more than double the expected VL risk. Additionally, given the data available, results indicate there is a high probability of increasing VL risk in the municipalities of Natal, Patu and Pau dos Ferros. These findings demonstrate opportunities for municipality-specific public health policy interventions and warrant future research on identifying epidemiological drivers in at-risk regions.

Author summary

As a neglected tropical disease (NTD), Leishmaniasis contributes to poor health outcomes, suffering, and death of vulnerable populations. Leishmania infantum or Leishmania donovani is transmitted from animals to humans through the sandfly vector and can be fatal in 5% of the cases even with treatment. Visceral leishmaniasis is a severe form of Leishmaniasis. The purpose of this study was to estimate regions of high Visceral leishmaniasis risk in Rio Grande do Norte, Brazil between 2007 and 2020 to help inform public health initiatives and potential health policy interventions for this disease. Visceral leishmaniasis is a reportable disease and therefore VL cases are documented to the Brazilian Ministry of Health database through passive surveillance. Passive surveillance data can suffer from missing information; individuals may be asymptomatic, or may not seek medical care allowing for diagnosis. When modeling disease risk from passive surveillance data, assuming there is an absence of disease in regions without documented cases can underestimate disease risk. In this paper we estimate disease risk and identify areas with high-risk and increasing infection trends over time while considering data characteristics such as missing information and high correlation of risk factors.