2012
DOI: 10.1371/journal.pntd.0001585
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Prevalence of Trachoma in Unity State, South Sudan: Results from a Large-Scale Population-Based Survey and Potential Implications for Further Surveys

Abstract: BackgroundLarge parts of South Sudan are thought to be trachoma-endemic but baseline data are limited. This study aimed to estimate prevalence for planning trachoma interventions in Unity State, to identify risk factors and to investigate the effect of different sampling approaches on study conclusions.Methods and FindingsThe survey area was defined as one domain of eight counties in Unity State. Across the area, 40 clusters (villages) were randomly selected proportional to the county population size in a popu… Show more

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Cited by 37 publications
(47 citation statements)
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References 30 publications
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“…Recent recommendations allow data from larger geographic areas (e.g. regions) to justify programme launch in areas where local knowledge or higher level data demonstrate that trachoma is widespread and highly endemic, as was the case in Unity state in South Sudan and Amhara region in Ethiopia [35], [36]. Much historical data in west Africa are representative at regional level and thus not directly comparable to district level data.…”
Section: Discussionmentioning
confidence: 99%
“…Recent recommendations allow data from larger geographic areas (e.g. regions) to justify programme launch in areas where local knowledge or higher level data demonstrate that trachoma is widespread and highly endemic, as was the case in Unity state in South Sudan and Amhara region in Ethiopia [35], [36]. Much historical data in west Africa are representative at regional level and thus not directly comparable to district level data.…”
Section: Discussionmentioning
confidence: 99%
“…Random effect values were then predicted for each school using a process termed conditional simulation, which uses semivariogram parameters to spatially predict random effect values for each prediction school. One thousand conditional simulations were conducted to generate 1,000 equally probable and spatially realistic “realizations” of possible random effect values at all schools 10,19,20. These random effect values were added to the fixed effect predictions and back transformed from log odds to generate age-specific prevalence values.…”
Section: Methodsmentioning
confidence: 99%
“…Another [84] study in Ethiopia reported that dirty faces and not going to school were significant independent risk factors for children aged 1–9 years. Ocular and nasal discharge in Sudan [85] and flies on a face and a dirty face in Nigeria and Mali [86, 87] were independent risk factors. Flies on a face and nasal discharge were found to be associated with trachoma in Niger [88], but the only significant risk factor was that (rather counterintuitively) the risk of infection increased when the household head had more years of formal education.…”
Section: Face Washing and Environmental Improvementmentioning
confidence: 99%