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A generalized framework for estimating snakebite underreporting using statistical models: A study in Colombia

BACKGROUND: Snakebite envenoming is a neglected tropical disease affecting deprived populations, and its burden is underestimated in some regions where patients prefer using traditional medicine, case reporting systems are deficient, or health systems are inaccessible to at-risk populations. Thus, t...

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Autores principales: Bravo-Vega, Carlos, Renjifo-Ibañez, Camila, Santos-Vega, Mauricio, León Nuñez, Leonardo Jose, Angarita-Sierra, Teddy, Cordovez, Juan Manuel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9934346/
https://www.ncbi.nlm.nih.gov/pubmed/36745647
http://dx.doi.org/10.1371/journal.pntd.0011117
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author Bravo-Vega, Carlos
Renjifo-Ibañez, Camila
Santos-Vega, Mauricio
León Nuñez, Leonardo Jose
Angarita-Sierra, Teddy
Cordovez, Juan Manuel
author_facet Bravo-Vega, Carlos
Renjifo-Ibañez, Camila
Santos-Vega, Mauricio
León Nuñez, Leonardo Jose
Angarita-Sierra, Teddy
Cordovez, Juan Manuel
author_sort Bravo-Vega, Carlos
collection PubMed
description BACKGROUND: Snakebite envenoming is a neglected tropical disease affecting deprived populations, and its burden is underestimated in some regions where patients prefer using traditional medicine, case reporting systems are deficient, or health systems are inaccessible to at-risk populations. Thus, the development of strategies to optimize disease management is a major challenge. We propose a framework that can be used to estimate total snakebite incidence at a fine political scale. METHODOLOGY/PRINCIPAL FINDINGS: First, we generated fine-scale snakebite risk maps based on the distribution of venomous snakes in Colombia. We then used a generalized mixed-effect model that estimates total snakebite incidence based on risk maps, poverty, and travel time to the nearest medical center. Finally, we calibrated our model with snakebite data in Colombia from 2010 to 2019 using the Markov-chain-Monte-Carlo algorithm. Our results suggest that 10.19% of total snakebite cases (532.26 yearly envenomings) are not reported and these snakebite victims do not seek medical attention, and that populations in the Orinoco and Amazonian regions are the most at-risk and show the highest percentage of underreporting. We also found that variables such as precipitation of the driest month and mean temperature of the warmest quarter influences the suitability of environments for venomous snakes rather than absolute temperature or rainfall. CONCLUSIONS/SIGNIFICANCE: Our framework permits snakebite underreporting to be estimated using data on snakebite incidence and surveillance, presence locations for the most medically significant venomous snake species, and openly available information on population size, poverty, climate, land cover, roads, and the locations of medical centers. Thus, our algorithm could be used in other countries to estimate total snakebite incidence and improve disease management strategies; however, this framework does not serve as a replacement for a surveillance system, which should be made a priority in countries facing similar public health challenges.
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spelling pubmed-99343462023-02-17 A generalized framework for estimating snakebite underreporting using statistical models: A study in Colombia Bravo-Vega, Carlos Renjifo-Ibañez, Camila Santos-Vega, Mauricio León Nuñez, Leonardo Jose Angarita-Sierra, Teddy Cordovez, Juan Manuel PLoS Negl Trop Dis Research Article BACKGROUND: Snakebite envenoming is a neglected tropical disease affecting deprived populations, and its burden is underestimated in some regions where patients prefer using traditional medicine, case reporting systems are deficient, or health systems are inaccessible to at-risk populations. Thus, the development of strategies to optimize disease management is a major challenge. We propose a framework that can be used to estimate total snakebite incidence at a fine political scale. METHODOLOGY/PRINCIPAL FINDINGS: First, we generated fine-scale snakebite risk maps based on the distribution of venomous snakes in Colombia. We then used a generalized mixed-effect model that estimates total snakebite incidence based on risk maps, poverty, and travel time to the nearest medical center. Finally, we calibrated our model with snakebite data in Colombia from 2010 to 2019 using the Markov-chain-Monte-Carlo algorithm. Our results suggest that 10.19% of total snakebite cases (532.26 yearly envenomings) are not reported and these snakebite victims do not seek medical attention, and that populations in the Orinoco and Amazonian regions are the most at-risk and show the highest percentage of underreporting. We also found that variables such as precipitation of the driest month and mean temperature of the warmest quarter influences the suitability of environments for venomous snakes rather than absolute temperature or rainfall. CONCLUSIONS/SIGNIFICANCE: Our framework permits snakebite underreporting to be estimated using data on snakebite incidence and surveillance, presence locations for the most medically significant venomous snake species, and openly available information on population size, poverty, climate, land cover, roads, and the locations of medical centers. Thus, our algorithm could be used in other countries to estimate total snakebite incidence and improve disease management strategies; however, this framework does not serve as a replacement for a surveillance system, which should be made a priority in countries facing similar public health challenges. Public Library of Science 2023-02-06 /pmc/articles/PMC9934346/ /pubmed/36745647 http://dx.doi.org/10.1371/journal.pntd.0011117 Text en © 2023 Bravo-Vega et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Bravo-Vega, Carlos
Renjifo-Ibañez, Camila
Santos-Vega, Mauricio
León Nuñez, Leonardo Jose
Angarita-Sierra, Teddy
Cordovez, Juan Manuel
A generalized framework for estimating snakebite underreporting using statistical models: A study in Colombia
title A generalized framework for estimating snakebite underreporting using statistical models: A study in Colombia
title_full A generalized framework for estimating snakebite underreporting using statistical models: A study in Colombia
title_fullStr A generalized framework for estimating snakebite underreporting using statistical models: A study in Colombia
title_full_unstemmed A generalized framework for estimating snakebite underreporting using statistical models: A study in Colombia
title_short A generalized framework for estimating snakebite underreporting using statistical models: A study in Colombia
title_sort generalized framework for estimating snakebite underreporting using statistical models: a study in colombia
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9934346/
https://www.ncbi.nlm.nih.gov/pubmed/36745647
http://dx.doi.org/10.1371/journal.pntd.0011117
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