ArticlePLoS neglected tropical diseases2010
Targeting trachoma control through risk mapping: the example of Southern Sudan.
Article in PLoS neglected tropical diseases, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 3 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 3 syntheses or guidelines pooled it, 68 citations in OpenAlex.
- Community-level trachoma ecological associations and the use of geospatial analysis methods: A systematic review.PLoS neglected tropical diseases · 2022Pooled it
- Risk mapping of clonorchiasis in the People's Republic of China: A systematic review and Bayesian geostatistical analysis.PLoS neglected tropical diseases · 2017Pooled it
- The impact of climatic risk factors on the prevalence, distribution, and severity of acute and chronic trachoma.PLoS neglected tropical diseases · 2013Pooled it
- Understanding the impact of covariates for trachoma prevalence prediction using geostatistical methods.BMC global and public health · 2025Article
- Trachoma in Sudan: Risk Factors and Clinical Stages in Patients From Two Eye Care Hospitals, 2015.Cureus · 2024Article
- Predicting future community-level ocular Chlamydia trachomatis infection prevalence using serological, clinical, molecular, and geospatial data.PLoS neglected tropical diseases · 2022Article
- The Connection between Climate Change, Surgical Care and Neglected Tropical Diseases.Annals of global health · 2022Article
- Integrating Geospatial Data and Measures of Disability and Wealth to Assess Inequalities in an Eye Health Survey: An Example from the Indian Sunderbans.International journal of environmental research and public health · 2019Article
- Understanding the spatial distribution of trichiasis and its association with trachomatous inflammation-follicular.BMC infectious diseases · 2019Article
- Climate Change and the Neglected Tropical Diseases.Advances in parasitology · 2018Review
- Spatial clustering of high load ocular Chlamydia trachomatis infection in trachoma: a cross-sectional population-based study.Pathogens and disease · 2017Article
- Prevalence of and Risk Factors for Trachoma in Oromia Regional State of Ethiopia: Results of 79 Population-Based Prevalence Surveys Conducted with the Global Trachoma Mapping Project.Ophthalmic epidemiology · 2016Article
- Earth Observation, Spatial Data Quality, and Neglected Tropical Diseases.PLoS neglected tropical diseases · 2015Review
- Multilevel Analysis of Trachomatous Trichiasis and Corneal Opacity in Nigeria: The Role of Environmental and Climatic Risk Factors on the Distribution of Disease.PLoS neglected tropical diseases · 2015Article
- Prevalence of trachoma at sub-district level in ethiopia: determining when to stop mass azithromycin distribution.PLoS neglected tropical diseases · 2014Article
- Global numbers of infection and disease burden of soil transmitted helminth infections in 2010.Parasites & vectors · 2014Article
- Different ontologies: land change science and health research.Current opinion in environmental sustainability · 2013Article
- The geographical distribution and burden of trachoma in Africa.PLoS neglected tropical diseases · 2013Article
- Leptospirosis in American Samoa--estimating and mapping risk using environmental data.PLoS neglected tropical diseases · 2012Article
- Prevalence of trachoma in unity state, South Sudan: results from a large-scale population-based survey and potential implications for further surveys.PLoS neglected tropical diseases · 2012Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTrachoma 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. METHODS/PRINCIPAL
findingsA 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.
conclusionIn 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.
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