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.

Fig 1

Location and Mesoregions of Rio Grande do Norte. The image on the left highlights the northeastern location of Rio Grande do Norte (in red) within Brazil. The image on the right shows the four Mesoregions in Rio Grande do Norte and municipalities within each region. This figure was created using the geobr package in R [25].

Fig 2

Spatio-temporal distribution of human visceral leishmaniasis rates per 100,000 between 2007 and 2020. This figure was created using the geobr package in R [25].

Fig 3

Distribution of average human visceral leishmaniasis rates per 100,000 in Rio Grande do Norte between 2007 and 2020. This figure was created using the geobr package in R [25].

Fig 4. Distribution of percent of residents that fall within each income brackets for all RN municipalities as captured by the 2010 Brazilian Federal Census.

Fig 5

Distribution of percent of residents that were reported as economically active and illiterate within each municipality as captured by the 2010 Brazilian Federal Census. Additionally shown in this figure is the distribution of percent of homes with inadequate or semi-adequate sanitation statues and the percent of homes that were located in rural or urban settings within each municipality.

Fig 6

Distribution of percentages of each animal. Two municipalities not plotted in this figure, Mossoro and Parnamirim, had the largest poultry production of all municipalities. The percent of poultry production for both was approximately 16%.

Fig 7

The 2010 Brazilian Federal Census age data was quantified as the percent of individuals within each municipality that fell in each age bracket. The figure above shows the distribution of percentages of residents falling within each age bracket of all RN municipalities.

Fig 8. Human VL Exploratory Factor Analysis results with 2010 Brazilian Federal Census primary variable contributions.

Fig 9

Spatial and Temporal posterior mode estimate of municipality-level relative risk of disease. This figure shows regions where, based on model results, it is likely that the risk of disease is higher than what we deem clinically significant. This figure was created using the geobr package in R [25].

Fig 10

The spatial and temporal posterior probability that a municipality’s relative risk estimate shown in Fig 9 is greater than two. Darker shades of red indicate it is likely that the municipality’s relative risk estimate is clinically significant. This figure was created using the geobr package in R [25].

Fig 11

This figure shows the most varying municipality-specific RR trends within the four mesoregions in RN. Municipality-specific risk trends were obtained as posterior distribution estimates if VL cases were observed or as predictions from the posterior predictive distributions if VL cases were unobserved for each year. Intervals included in this figure for each trend are 95% HPD credible intervals. The horizontal line represents a relative risk of one.