Patterns and drivers of Human Visceral Leishmaniasis in Pernambuco (Brazil) from 2007 to 2018

Abstract

Visceral leishmaniasis (VL) is the second most common protozoosis that affects people around the world. The aim of this study is to understand how environmental and socioeconomic factors, as well as VL control and surveillance interventions, influence the spread and detection of VL cases in Pernambuco state (Brazil). A novel model was developed to analyze cases of VL between 2007 and 2018, enabling the quantification of the association of these variables with two processes: the probability of “invasion” (emergence of new cases) at municipalities by VL, and the probability of detecting cases not reported in municipalities that have already been invaded. Pernambuco state identified 1,410 cases of VL between 2007 and 2018, with an average of 128 cases per year and average incidence of 1.28/100 thousand people. These cases were distributed in 77.1% (142/184) of the municipalities, and 54.8% (773/1,410) of them were autochthonous. Our model reveals that the proportion of agriculture was positively associated with VL invasion probability. We also find that municipalities that are closer to notification centers and/or that have received technical training and support tend to have higher detection rates of VL cases. Taken together, these results suggest that a municipality with almost no agriculture and that received technical training, located close to a notification center, is unlikely to be invaded if no cases have ever been detected. On the other hand, a municipality that is far from the notification center, with no technical training, with a large agricultural area might have already been invaded but the surveillance system might have routinely failed to detect VL cases due to low detection probability. By disentangling the processes of invasion and detection, we were able to generate insights that are likely to be useful for the strategic allocation of VL prevention and control interventions.

Estimation and interpretation problems and solutions when using proportion covariates in linear regression models

“…These compositional variables are often used as covariates (also known as independent, explanatory, or predictor variables) when attempting to understand different types of phenomena. For example, LULC change is the major driver of biodiversity decline and ecosystem integrity loss throughout the world (Díaz et al, 2019; Tilman et al, 2017) and, as a result, there has been considerable interest in understanding how the surrounding LULC influences a wide range of phenomena, such as animal behavior (e.g., Gallo et al, 2022; Giroux et al, 2023; Noonan et al, 2022; Paviolo et al, 2018; Zeller et al, 2016), occurrence/abundance of species (e.g., Canibe et al, 2022; Fink et al, 2020; Miller et al, 2019; Valle et al, 2022), water quality and pollutant concentration (Hoek et al, 2008; Piffer et al, 2021), perception of environmental problems (Suchy et al, 2023), and disease incidence (e.g., Machado et al, 2023; Valle & Tucker Lima, 2014).…”