Linked Life Data

20889880

Source:http://linkedlifedata.com/resource/pubmed/id/20889880

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2010-10-4
pubmed:abstractText
Background malaria-control programs are increasingly dependent on accurate risk maps to effectively guide the allocation of interventions and resources. Advances in model-based geostatistics and geographical information systems (GIS) have enabled researchers to better understand factors affecting malaria transmission and thus, more accurately determine the limits of malaria transmission globally and nationally. Here, we construct Plasmodium falciparum risk maps for Bangladesh for 2007 at a scale enabling the malaria-control bodies to more accurately define the needs of the program. A comprehensive malaria-prevalence survey (N = 9,750 individuals; N = 354 communities) was carried out in 2007 across the regions of Bangladesh known to be endemic for malaria. Data were corrected to a standard age range of 2 to less than 10 years. Bayesian geostatistical logistic regression models with environmental covariates were used to predict P. falciparum prevalence for 2- to 10-year-old children (PfPR(2-10)) across the endemic areas of Bangladesh. The predictions were combined with gridded population data to estimate the number of individuals living in different endemicity classes. Across the endemic areas, the average PfPR(2-10) was 3.8%. Environmental variables selected for prediction were vegetation cover, minimum temperature, and elevation. Model validation statistics revealed that the final Bayesian geostatistical model had good predictive ability. Risk maps generated from the model showed a heterogeneous distribution of PfPR(2-10) ranging from 0.5% to 50%; 3.1 million people were estimated to be living in areas with a PfPR(2-10) greater than 1%. Contemporary GIS and model-based geostatistics can be used to interpolate malaria risk in Bangladesh. Importantly, malaria risk was found to be highly varied across the endemic regions, necessitating the targeting of resources to reduce the burden in these areas.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
AIM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
1476-1645
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
83
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
861-7
pubmed:dateRevised
2011-10-5
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Mapping malaria risk in Bangladesh using Bayesian geostatistical models.
pubmed:affiliation
Pacific Malaria Initiative Support Centre (PacMISC), University of Queensland, School of Population Health, Brisbane, Queensland, Australia. heidilouisereid@gmail.com
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't