Linked Life Data

16246384

Source:http://linkedlifedata.com/resource/pubmed/id/16246384

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2006-2-6
pubmed:abstractText
Geographic information systems (GIS) and remote sensing were used to identify villages at high risk for sleeping sickness, as defined by reported incidence. Landsat Enhanced Thematic Mapper (ETM) satellite data were classified to obtain a map of land cover, and the Normalised Difference Vegetation Index (NDVI) and Landsat band 5 were derived as unclassified measures of vegetation density and soil moisture, respectively. GIS functions were used to determine the areas of land cover types and mean NDVI and band 5 values within 1.5 km radii of 389 villages where sleeping sickness incidence had been estimated. Analysis using backward binary logistic regression found proximity to swampland and low population density to be predictive of reported sleeping sickness presence, with distance to the sleeping sickness hospital as an important confounding variable. These findings demonstrate the potential of remote sensing and GIS to characterize village-level risk of sleeping sickness in endemic regions.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
0035-9203
pubmed:author
pubmed:issnType
Print
pubmed:volume
100
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
354-62
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Using remote sensing and geographic information systems to identify villages at high risk for rhodesiense sleeping sickness in Uganda.
pubmed:affiliation
Sleeping Sickness Programme, National Agricultural Research Organization, LIRI Hospital, P.O. Box 96, Tororo, Uganda.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't