@article{fdi:010095417,
  title = {{S}ocioeconomic and ecological drivers of snakebite incidence in {M}exico : a spatial analysis of risk factors},
  author = {{R}angel-{C}amacho, {R}. and {Y}áñez-{A}renas, {C}. and {C}hippaux, {J}ean-{P}hilippe and {M}artin, {G}.},
  editor = {},
  language = {{ENG}},
  abstract = {{B}ackground {S}nakebite envenoming constitutes a significant public health challenge in tropical and subtropical regions, with {M}exico reporting substantial incidence rates in the {A}mericas. {W}hile 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. {T}his study aimed to identify and quantify the main determinants of snakebite spatial heterogeneity across {M}exico while accounting for potential reporting biases in surveillance data.{M}ethods/principal findings {W}e implemented a rigorous {B}ayesian analytical framework utilizing a conditional autoregressive zero-inflated {P}oisson model to examine snakebite incidence across 2,463 {M}exican municipalities. {O}ur 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. {S}ocial 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 {B}othrops asper (beta = 0.268, 95% {CI}: 0.047-0.504) emerged as the primary factors explaining spatial variation in snakebite incidence. {H}ealthcare facility distribution (beta = 0.225, 95% {CI}: 0.126-0.326) was identified as a significant source of reporting bias. {A}fter 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.{M}ethods/principal findings {W}e implemented a rigorous {B}ayesian analytical framework utilizing a conditional autoregressive zero-inflated {P}oisson model to examine snakebite incidence across 2,463 {M}exican municipalities. {O}ur 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. {S}ocial 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 {B}othrops asper (beta = 0.268, 95% {CI}: 0.047-0.504) emerged as the primary factors explaining spatial variation in snakebite incidence. {H}ealthcare facility distribution (beta = 0.225, 95% {CI}: 0.126-0.326) was identified as a significant source of reporting bias. {A}fter 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.{C}onclusions {O}ur findings demonstrate that snakebite risk in {M}exico is driven by a complex interaction between social vulnerability, infrastructure development, and the distribution of key venomous snake species. {T}he identification of systematic reporting biases offers critical insights for optimizing surveillance protocols and implementing targeted interventions in high-risk municipalities.},
  keywords = {{MEXIQUE}},
  booktitle = {},
  journal = {{PL}o{S} {N}eglected {T}ropical {D}iseases},
  volume = {19},
  numero = {10},
  pages = {e0013582 [16 p.]},
  ISSN = {1935-2735},
  year = {2025},
  DOI = {10.1371/journal.pntd.0013582},
  URL = {https://www.documentation.ird.fr/hor/fdi:010095417},
}