Rangel-Camacho R., Yáñez-Arenas C., Chippaux Jean-Philippe, Martin G. (2025). Socioeconomic and ecological drivers of snakebite incidence in Mexico : a spatial analysis of risk factors. PLoS Neglected Tropical Diseases, 19 (10), p. e0013582 [16 p.]. ISSN 1935-2735.
Auteurs
Rangel-Camacho R., Yáñez-Arenas C., Chippaux Jean-Philippe, Martin G.
Source
PLoS Neglected Tropical Diseases, 2025,
19 (10), p. e0013582 [16 p.] ISSN 1935-2735
Background Snakebite envenoming constitutes a significant public health challenge in tropical and subtropical regions, with Mexico reporting substantial incidence rates in the Americas. While previous investigations have documented the socioeconomic burden of snakebites, particularly in economically marginalized regions, a comprehensive understanding of the relative contributions of biological and socioeconomic determinants to spatial heterogeneity in snakebite incidence remains poorly understood. This study aimed to identify and quantify the main determinants of snakebite spatial heterogeneity across Mexico while accounting for potential reporting biases in surveillance data.Methods/principal findings We implemented a rigorous Bayesian analytical framework utilizing a conditional autoregressive zero-inflated Poisson model to examine snakebite incidence across 2,463 Mexican municipalities. Our methodological approach integrated three critical components: environmental suitability indices for venomous snake species derived from refined species distribution models, socioeconomic vulnerability metrics, and healthcare accessibility parameters. Social lag index (beta = 0.308, 95% CI: 0.106-0.522), road network density (beta = 0.376, 95% CI: 0.215-0.539), and environmental suitability for Bothrops asper (beta = 0.268, 95% CI: 0.047-0.504) emerged as the primary factors explaining spatial variation in snakebite incidence. Healthcare facility distribution (beta = 0.225, 95% CI: 0.126-0.326) was identified as a significant source of reporting bias. After controlling for this bias, our model revealed substantially different spatial pattern of risk, with elevated predicted incidence in urban centers and specific coastal regions not previously identified as high-risk areas.Methods/principal findings We implemented a rigorous Bayesian analytical framework utilizing a conditional autoregressive zero-inflated Poisson model to examine snakebite incidence across 2,463 Mexican municipalities. Our methodological approach integrated three critical components: environmental suitability indices for venomous snake species derived from refined species distribution models, socioeconomic vulnerability metrics, and healthcare accessibility parameters. Social lag index (beta = 0.308, 95% CI: 0.106-0.522), road network density (beta = 0.376, 95% CI: 0.215-0.539), and environmental suitability for Bothrops asper (beta = 0.268, 95% CI: 0.047-0.504) emerged as the primary factors explaining spatial variation in snakebite incidence. Healthcare facility distribution (beta = 0.225, 95% CI: 0.126-0.326) was identified as a significant source of reporting bias. After controlling for this bias, our model revealed substantially different spatial pattern of risk, with elevated predicted incidence in urban centers and specific coastal regions not previously identified as high-risk areas.Conclusions Our findings demonstrate that snakebite risk in Mexico is driven by a complex interaction between social vulnerability, infrastructure development, and the distribution of key venomous snake species. The identification of systematic reporting biases offers critical insights for optimizing surveillance protocols and implementing targeted interventions in high-risk municipalities.
