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Open Access Research article

A rationale for continuing mass antibiotic distributions for trachoma

Kathryn J Ray12*, Travis C Porco13, Kevin C Hong1, David C Lee1, Wondu Alemayehu4, Muluken Melese4, Takele Lakew4, Elizabeth Yi1, Jenafir House1, Jaya D Chidambaram1, John P Whitcher1567, Bruce D Gaynor15 and Thomas M Lietman1567

Author Affiliations

1 Francis I. Proctor Foundation, University of California, San Francisco, USA

2 Mathematics Department, San Francisco State University, USA

3 Department of Public Health, San Francisco, USA

4 Orbis International, Addis Ababa, Ethiopia

5 Department of Ophthalmology, University of California, San Francisco, USA

6 Department of Epidemiology & Biostatistics, University of California, San Francisco, USA

7 Institute for Global Health, University of California, San Francisco, USA

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BMC Infectious Diseases 2007, 7:91  doi:10.1186/1471-2334-7-91

Published: 7 August 2007

Abstract

Background

The World Health Organization recommends periodic mass antibiotic distributions to reduce the ocular strains of chlamydia that cause trachoma, the world's leading cause of infectious blindness. Their stated goal is to control infection, not to completely eliminate it. A single mass distribution can dramatically reduce the prevalence of infection. However, if infection is not eliminated in every individual in the community, it may gradually return back into the community, so often repeated treatments are necessary. Since public health groups are reluctant to distribute antibiotics indefinitely, we are still in need of a proven long-term rationale. Here we use mathematical models to demonstrate that repeated antibiotic distributions can eliminate infection in a reasonable time period.

Methods

We fit parameters of a stochastic epidemiological transmission model to data collected before and 6 months after a mass antibiotic distribution in a region of Ethiopia that is one of the most severely affected areas in the world. We validate the model by comparing our predicted results to Ethiopian data which was collected biannually for two years past the initial mass antibiotic distribution. We use the model to simulate the effect of different treatment programs in terms of local elimination of infection.

Results

Simulations show that the average prevalence of infection across all villages progressively decreases after each treatment, as long as the frequency and coverage of antibiotics are high enough. Infection can be eliminated in more villages with each round of treatment. However, in the communities where infection is not eliminated, it returns to the same average level, forming the same stationary distribution. This phenomenon is also seen in subsequent epidemiological data from Ethiopia. Simulations suggest that a biannual treatment plan implemented for 5 years will lead to elimination in 95% of all villages.

Conclusion

Local elimination from a community is theoretically possible, even in the most severely infected communities. However, elimination from larger areas may require repeated biannual treatments and prevention of re-introduction from outside to treated areas.