ArticleRoyal Society open science2022
Environmental determinants predicting population vulnerability to high yellow fever incidence.
Article in Royal Society open science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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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
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of the data, methods and environmental covariates used to map Aedes-borne arbovirus transmission risk.BMC infectious diseases · 2023Pooled it
- Review
- Yellow fever in Ghana: Predicting emergence and ecology from historical outbreaks.PLOS global public health · 2024Article
- Weekly Forecasting of Yellow Fever Occurrence and Incidence via Eco-Meteorological Dynamics.GeoHealth · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Yellow fever (YF) is an endemic mosquito-borne disease in Brazil, though many locations have not observed cases in recent decades. Some locations with low disease burden may resemble locations with higher disease burden through environmental and ecohydrological characteristics, which are known to impact YF burden, motivating increased or continued prevention measures such as vaccination, mosquito control or surveillance. This study aimed to use environmental characteristics to estimate vulnerability to observing high YF burden among all Brazilian municipalities. Vulnerability was defined in three categories based on yearly incidence between 2000 and 2017: minimal, low and high vulnerability. A cumulative logit model was fit to these categories using environmental and ecohydrological predictors, selecting those that provided the most accurate model fit. Per cent of days with precipitation, mean temperature, biome, population density, elevation, vegetation and nearby disease occurrence were included in best-fitting models. Model results were applied to estimate vulnerability nationwide. Municipalities with highest probability of observing high vulnerability was found in the North and Central-West (2000-2016) as well as the Southeast (2017) regions. Results of this study serve to identify specific locations to prioritize new or ongoing surveillance and prevention of YF based on underlying ecohydrological conditions.
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Registered trials
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