02507nas a2200397   4500000000100000008004100001653001300042653001000055653002000065653001700085653001500102653002400117653000900141653001100150653001100161653001400172653001100186653001200197653002100209653001000230653001600240100001800256700001000274700001300284700001300297700001400310700001100324700001400335700001900349245008400368856007800452300000900530490000600539520155000545022001402095       2010                            d10aTrachoma10aSudan10aRisk Management10aRisk Factors10aPrevalence10aModels, Statistical10aMale10aInfant10aHumans10aGeography10aFemale10aClimate10aChild, Preschool10aChild10aAge Factors1 aClements AC A1 aKur L1 aGatpan G1 aNgondi J1 aEmerson P1 aLado M1 aSabasio A1 aKolaczinski JH00aTargeting trachoma control through risk mapping: the example of Southern Sudan.  uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC2923154/pdf/pntd.0000799.pdf  ae7990 v43 a<p><strong>BACKGROUND: </strong>Trachoma is a major cause of blindness in Southern Sudan. Its distribution has only been partially established and many communities in need of intervention have therefore not been identified or targeted. The present study aimed to develop a tool to improve targeting of survey and control activities.</p>

<p><strong>METHODS/PRINCIPAL FINDINGS: </strong>A national trachoma risk map was developed using Bayesian geostatistics models, incorporating trachoma prevalence data from 112 geo-referenced communities surveyed between 2001 and 2009. Logistic regression models were developed using active trachoma (trachomatous inflammation follicular and/or trachomatous inflammation intense) in 6345 children aged 1-9 years as the outcome, and incorporating fixed effects for age, long-term average rainfall (interpolated from weather station data) and land cover (i.e. vegetation type, derived from satellite remote sensing), as well as geostatistical random effects describing spatial clustering of trachoma. The model predicted the west of the country to be at no or low trachoma risk. Trachoma clusters in the central, northern and eastern areas had a radius of 8 km after accounting for the fixed effects.</p>

<p><strong>CONCLUSION: </strong>In Southern Sudan, large-scale spatial variation in the risk of active trachoma infection is associated with aridity. Spatial prediction has identified likely high-risk areas to be prioritized for more data collection, potentially to be followed by intervention.</p>
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