Evidence map›Paper›PMID 40931028›Full record

ArticleScientific reports2025

Causal inference unveils how forest coverage mitigates excess snakebite cases during rainfall seasons in Colombia.

Juan David Gutiérrez, Carlos Bravo-Vega, Juan Manuel Cordovez

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Juan David GutiérrezFacultad de Medicina y Ciencias de la Salud, Instituto Masira, Universidad de Santander, Bucaramanga, Colombia.
Carlos Bravo-VegaGrupo de investigación en Biología Matemática y Computacional (BIOMAC), Departamento de Ingeniería Biomédica, Universidad de los Andes, Bogotá, Colombia. ca.bravo955@uniandes.edu.co.
Juan Manuel CordovezGrupo de investigación en Biología Matemática y Computacional (BIOMAC), Departamento de Ingeniería Biomédica, Universidad de los Andes, Bogotá, Colombia.

Funding

Ministerio de Ciencia, Tecnología e Innovación Application 727 for doctoral studentsUniversidad de los Andes Funding program for doctoral students
6 · The paper itself

Abstract

Snakebite envenoming is a neglected tropical disease that affects mainly rural populations, where antivenom is scarce. Understanding environmental drivers of snakebite incidence is critical for public health preparedness. This study employs causal inference to assess the impact of rainfall on snakebite surges in Colombia, with broader implications for tropical regions. Using a spatiotemporal database of monthly snakebite case data (2007-2021), we applied machine learning models to estimate the causal effect of rainfall, considering nine atmospheric and oceanic indices, forest coverage, and rural GDP. High rainfall significantly causes excess snakebite cases (i.e., increasing the likelihood that the number of cases exceeds what is expected based on the standardized incidence ratio): a one-standard-deviation increase in rainfall (134.65 mm) led to a 2.1% rise in excess snakebite cases (95% CI 1.3-2.9). Forest coverage exhibited an inverse relationship with the impact of rainfall on excess cases, which is positive in regions with < 50% forest cover. These findings highlight the need for climate-adaptive public health strategies. Deforested regions face heightened snakebite risk during heavy rainfall, emphasizing the role of deforestation in shaping disease dynamics. As climate change alters precipitation patterns, integrating ecological and epidemiological data is crucial for forecasting and mitigating snakebite burden globally.

Indexed as

ForestsRainSnake BitesAnimalsColombiaConservation of Natural ResourcesHumansIncidenceSeasonsTropical ClimateCausal inferenceClimateDeforestationRainfallSnakebite envenoming

Identifiers

PMID40931028
PMCPMC12423302

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.