Background: Scorpion stings in Brazil represent a major public health problem due to their incidence and their potential ability to lead to severe and often fatal clinical outcomes. A better understanding of scorpionism determinants is essential for a precise comprehension of accident dynamics and to guide public policy. Our study is the first to model the spatio-temporal variability of scorpionism across municipalities in São Paulo (SP) and to investigate its relationship with demographic, socioeconomic, environmental, and climatic variables.
Methodology: This ecological study analyzed secondary data on scorpion envenomation in SP from 2008 to 2021, using the Integrated Nested Laplace Approximation (INLA) to perform Bayesian inference for detection of areas and periods with the most suitable conditions for scorpionism.
Principal findings: From the spring of 2008 to 2021, the relative risk (RR) increased eight times in SP, from 0.47 (95%CI 0.43-0.51) to 3.57 (95%CI 3.36-3.78), although there has been an apparent stabilization since 2019. The western, northern, and northwestern parts of SP showed higher risks; overall, there was a 13% decrease in scorpionism during winters. Among the covariates considered, an increase of one standard deviation in the Gini index, which captures income inequality, was associated with a 11% increase in scorpion envenomation. Maximum temperatures were also associated with scorpionism, with risks doubling for temperatures above 36°C. Relative humidity displayed a nonlinear association, with a 50% increase in risk for 30-32% humidity and reached a minimum of 0.63 RR for 75-76% humidity.
Conclusions: Higher temperatures, lower humidity, and social inequalities were associated with a higher risk of scorpionism in SP municipalities. By capturing local and temporal relationships across space and time, authorities can design more effective strategies that adhere to local and temporal considerations.
Copyright: © 2023 Chiaravalloti-Neto et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.