ReviewInternational journal of environmental research and public health2025
Bibliometric Analysis of the Evolution and Distribution of Research on Analytical Methods for Climate-Sensitive Infectious Diseases in Latin America and the Caribbean.
Review in International journal of environmental research and public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Climate-Sensitive Infectious Diseases (CSIDs) are diseases whose prevalence and transmission are heavily influenced by climatic factors, posing a significant challenge to public health, particularly in vulnerable regions such as Latin America and the Caribbean (LAC). This study employs a bibliometric analysis to evaluate the evolution and distribution of research on CSID and the analytical methods employed in the field. Using bibliometric and text-mining techniques, the analysis examines publication trends, research hotspots, and methodological developments from 2015 to 2024. The results highlight a regional concentration of research, with Brazil leading in CSID studies, particularly on arboviruses such as dengue, Zika, and chikungunya. The analysis also reveals the predominance of regression models, time-series analysis, and spatial analysis as primary methods used to forecast and analyze disease outbreaks. However, advanced techniques such as neural networks and niche modeling are gaining traction, indicating a shift towards more data-intensive approaches. The findings underscore the importance of enhancing forecasting capabilities and integrating analytical models into public-health systems to anticipate the impact of climate change on disease patterns. This study offers critical insights into methodological trends and identifies gaps for future research, contributing to more effective decision making in public health across Latin America and the Caribbean.
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What OpenQuestion holds
Registered trials
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.