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

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.

Spatial distribution of trachoma cases in the City of Bauru, State of São Paulo, Brazil, detected in 2006: defining key areas for improvement of health resources

“…Income, education, and access to water are not variables related to individuals, but characterize the profile of a population group 1 . People live in groups and only group analysis can capture behaviors, values, or even the transmission of infections that occur on this scale 25 .…”

“…In the case of trachoma, the environment is undoubtedly of paramount importance. Numerous studies have associated the disease with a lack of water and sanitation 1,9 . However, presently, in Brazil, most of the urban population has access to water, through the expansion of supply networks, either due to investment by sanitation companies, through individual efforts connecting networks, or even the creation of small unofficial supply networks 27 .…”

“…A similar method was used in Sudan, making it possible to establish risk areas linked to the transmission of trachoma, and to establish detection and treatment plans 1 .…”

“…Trachoma, a chronic recurrent keratoconjunctivitis, is a chronic inflammatory eye disease of sudden or insidious onset that may persist for many years if left untreated, with the risk of scarring in the conjunctiva that may cause repeated trauma to the cornea, leading to the development of corneal opacities, reduced vision, and eventually blindness 1 . Trachoma is present in approximately 50 countries, accounting for 1.3 million blind individuals and 1.8 million individuals with compromised vision, with the risk of blindness to a further 8.3 million people worldwide 2,3 .…”

Interpreting predictive maps of disease: highlighting the pitfalls of distribution models in epidemiology

“…Data from a sample of locations (surveys or surveillance) can be used to fit a model, and subsequent interpolation or extrapolation can provide a spatially continuous prediction of disease (Brooker, 2007), alleviating the need for comprehensive and large-scale surveys. These outputs can allow the consideration of spatial heterogeneity in disease distributions during planning, implementation and monitoring of interventions, including targeting interventions to areas with the greatest predicted risk of disease (Clements et al, 2006), identification of areas with a low risk of disease (which can be considered to be of low priority for intervention) (Clements et al, 2010) and recognition of areas in which intervention may be detrimental . The consideration of uncertainty in outputs allows the delineation of areas from which additional information is required; thus, allowing targeted data acquisition (Clements et al, 2006).…”