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Open Access Research

Prevalence and under-detection of gambiense human African trypanosomiasis during mass screening sessions in Uganda and Sudan

Francesco Checchi1*, Andrew P Cox2, François Chappuis34, Gerardo Priotto5, Daniel Chandramohan1 and Daniel T Haydon6

Author Affiliations

1 Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E7HT, United Kingdom

2 Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E7HT, United Kingdom

3 Médecins Sans Frontières, 78 rue de Lausanne, 1202, Geneva, Switzerland

4 Geneva University Hospitals & University of Geneva, 14 rue Gabrielle-Perret-Gentil 6, 1211, Geneva, Switzerland

5 Epicentre, 8Rue Saint-Sabin, 75011, Paris, France

6 College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, G12 8QQ, United Kingdom

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Parasites & Vectors 2012, 5:157  doi:10.1186/1756-3305-5-157

Published: 7 August 2012

Abstract

Background

Active case detection through mass community screening is a major control strategy against human African trypanosomiasis (HAT, sleeping sickness) caused by T. brucei gambiense. However, its impact can be limited by incomplete attendance at screening sessions (screening coverage) and diagnostic inaccuracy.

Methods

We developed a model-based approach to estimate the true prevalence and the fraction of cases detected during mass screening, based on observed prevalence, and adjusting for incomplete screening coverage and inaccuracy of diagnostic algorithms for screening, confirmation and HAT stage classification. We applied the model to data from three Médecins Sans Frontières projects in Uganda (Adjumani, Arua-Yumbe) and Southern Sudan (Kiri).

Results

We analysed 604 screening sessions, targeting about 710 000 people. Cases were about twice as likely to attend screening as non-cases, with no apparent difference by stage. Past incidence, population size and repeat screening rounds were strongly associated with observed prevalence. The estimated true prevalence was 0.46% to 0.90% in Kiri depending on the analysis approach, compared to an observed prevalence of 0.45%; 0.59% to 0.87% in Adjumani, compared to 0.92%; and 0.18% to 0.24% in Arua-Yumbe, compared to 0.21%. The true ratio of stage 1 to stage 2 cases was around two-three times higher than that observed, due to stage misclassification. The estimated detected fraction was between 42.2% and 84.0% in Kiri, 52.5% to 79.9% in Adjumani and 59.3% to 88.0% in Arua-Yumbe.

Conclusions

In these well-resourced projects, a moderate to high fraction of cases appeared to be detected through mass screening. True prevalence differed little from observed prevalence for monitoring purposes. We discuss some limitations to our model that illustrate several difficulties of estimating the unseen burden of neglected tropical diseases.

Keywords:
Trypanosomiasis; Gambiense; Sleeping sickness; Case detection; Screening; Coverage; Prevalence; Uganda; Sudan; Mathematical model