ReviewSpatial and spatio-temporal epidemiology2015
Sources of spatial animal and human health data: Casting the net wide to deal more effectively with increasingly complex disease problems.
Review in Spatial and spatio-temporal epidemiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Day-1 Competencies for Veterinarians Specific to Health Informatics.Frontiers in veterinary science · 2021Article
- Data-Driven Surveillance: Effective Collection, Integration, and Interpretation of Data to Support Decision Making.Frontiers in veterinary science · 2021Article
- An Online Geographic Data Visualization Tool to Relate Preterm Births to Environmental Factors.Preventing chronic disease · 2019Article
- Article
- Using Open Street Map Data in Environmental Exposure Assessment Studies: Eastern Massachusetts, Bern Region, and South Israel as a Case Study.International journal of environmental research and public health · 2018Article
- The impact of the built environment on health behaviours and disease transmission in social systems.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2018Review
- Translating Big Data into Smart Data for Veterinary Epidemiology.Frontiers in veterinary science · 2017Article
- Lessons from the Ebola Outbreak: Action Items for Emerging Infectious Disease Preparedness and Response.EcoHealth · 2016Review
- Spatial Modelling Tools to Integrate Public Health and Environmental Science, Illustrated with Infectious Cryptosporidiosis.International journal of environmental research and public health · 2016Review
- The Effect of Altitude on Intraocular Pressure in Vitrectomized Eyes with Sulfur Hexafluoride Tamponade by the Friedenwald Method: Rabbit Animal Model.BioMed research international · 2016Article
- Technology and Data Collection in Chronic Disease Epidemiology.Preventing chronic disease · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
During the last 30years it has become commonplace for epidemiological studies to collect locational attributes of disease data. Although this advancement was driven largely by the introduction of handheld global positioning systems (GPS), and more recently, smartphones and tablets with built-in GPS, the collection of georeferenced disease data has moved beyond the use of handheld GPS devices and there now exist numerous sources of crowdsourced georeferenced disease data such as that available from georeferencing of Google search queries or Twitter messages. In addition, cartography has moved beyond the realm of professionals to crowdsourced mapping projects that play a crucial role in disease control and surveillance of outbreaks such as the 2014 West Africa Ebola epidemic. This paper provides a comprehensive review of a range of innovative sources of spatial animal and human health data including data warehouses, mHealth, Google Earth, volunteered geographic information and mining of internet-based big data sources such as Google and Twitter. We discuss the advantages, limitations and applications of each, and highlight studies where they have been used effectively.
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.