Targeting Trachoma Control through Risk Mapping: The Example of Southern Sudan

Background: 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. Methods/Principal Findings: 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. Conclusion: 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. Author Summary: Trachoma, caused by the bacterium Chlamydia trachomatis, is the leading cause of preventable blindness worldwide and a major cause of blindness in Southern Sudan. However, the trachoma distribution in Southern Sudan has only been partially established and many communities in need of intervention have not been identified or targeted. Incomplete mapping and intervention coverage is largely attributable to trachoma resources being scarce and not always deployed most efficiently. The present study aimed at improving programme efficiency by developing maps to help target the available resources for trachoma surveys and interventions to areas where these are most needed. Data on active trachoma prevalence, collected during baseline surveys between 2001 and 2009, were incorporated into Bayesian geostatistical models to develop a national trachoma risk map. The model predicted the west of the country to be largely at no or very low trachoma risk, while most of the high-risk areas are located in the centre, north, and south-east. Risk mapping has allowed Southern Sudan's trachoma control programme to identify areas where collection of additional data would be most useful. As a direct result, baseline data were collected in March 2010 for the whole of Unity State, with antibiotic mass drug administration being scaled up from June 2010 onwards.