ArticleInternational journal of environmental research and public health2014
Identifying environmental risk factors of cholera in a coastal area with geospatial technologies.
Article in International journal of environmental research and public health, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- How climate change can affect cholera incidence and prevalence? A systematic review.Environmental science and pollution research international · 2020Pooled it
- Eco-Evolutionary Drivers of Vibrio parahaemolyticus Sequence Type 3 Expansion: Retrospective Machine Learning Approach.JMIR bioinformatics and biotechnology · 2024Article
- Cholera Risk: A Machine Learning Approach Applied to Essential Climate Variables.International journal of environmental research and public health · 2020Article
- An Integrated Approach for Spatio-Temporal Cholera Disease Hotspot Relation Mining for Public Health Management in Punjab, Pakistan.International journal of environmental research and public health · 2020Article
- [Using geo-intelligence to estimate risk of introduction of influenza type A in MexicoCenário de risco de introdução do vírus da influenza A no México estimado com o uso de inteligência geográfica].Revista panamericana de salud publica = Pan American journal of public health · 2019Article
- Applications of Space Technologies to Global Health: Scoping Review.Journal of medical Internet research · 2018Article
- Mapping risk of leptospirosis in China using environmental and socioeconomic data.BMC infectious diseases · 2016Article
- Factors Associated with the Choice of Peritoneal Dialysis in Patients with End-Stage Renal Disease.BioMed research international · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Satellites contribute significantly to environmental quality and public health. Environmental factors are important indicators for the prediction of disease outbreaks. This study reveals the environmental factors associated with cholera in Zhejiang, a coastal province of China, using both Remote Sensing (RS) and Geographic information System (GIS). The analysis validated the correlation between the indirect satellite measurements of sea surface temperature (SST), sea surface height (SSH) and ocean chlorophyll concentration (OCC) and the local cholera magnitude based on a ten-year monthly data from the year 1999 to 2008. Cholera magnitude has been strongly affected by the concurrent variables of SST and SSH, while OCC has a one-month time lag effect. A cholera prediction model has been established based on the sea environmental factors. The results of hot spot analysis showed the local cholera magnitude in counties significantly associated with the estuaries and rivers.
Indexed as
Identifiers
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.