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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
medicine
The Distribution of Ocular Chlamydia Prevalence across Tanzanian Communities Where Trachoma Is Declining
PLoS Neglected Tropical Diseases, Volume 9, No. 3, Article e0003682, Year 2015
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Description
Mathematical models predict an exponential distribution of infection prevalence across communities where a disease is disappearing. Trachoma control programs offer an opportunity to test this hypothesis, as the World Health Organization has targeted trachoma for elimination as a public health concern by the year 2020. Local programs may benefit if a single survey could reveal whether infection was headed towards elimination. Using data from a previously-published 2009 survey, we test the hypothesis that Chlamydia trachomatis prevalence across 75 Tanzanian communities where trachoma had been documented to be disappearing is exponentially distributed. We fit multiple continuous distributions to the Tanzanian data and found the exponential gave the best approximation. Model selection by Akaike Information Criteria (AICc) suggested the exponential distribution had the most parsimonious fit to the data. Those distributions which do not include the exponential as a special or limiting case had much lower likelihoods of fitting the observed data. 95% confidence intervals for shape parameter estimates of those distributions which do include the exponential as a special or limiting case were consistent with the exponential. Lastly, goodness-of-fit testing was unable to reject the hypothesis that the prevalence data came from an exponential distribution. Models correctly predict that infection prevalence across communities where a disease is disappearing is best described by an exponential distribution. In Tanzanian communities where local control efforts had reduced the clinical signs of trachoma by 80% over 10 years, an exponential distribution gave the best fit to prevalence data. An exponential distribution has a relatively heavy tail, thus occasional high-prevalence communities are to be expected even when infection is disappearing. A single cross-sectional survey may be able to reveal whether elimination efforts are on-track. © 2015 Rahman et al.
Authors & Co-Authors
Rahman, Salman A.
United States, San Francisco
F.i. Proctor Foundation
Vos, Theo K.
United States, Baltimore
Johns Hopkins University
Mkocha, Harran A.
United States, Baltimore
Johns Hopkins University
Muñoz, Beatriz E.
United States, Baltimore
Johns Hopkins University
Porco, Travis C.
United States, San Francisco
F.i. Proctor Foundation
United States, San Francisco
University of California, San Francisco
Keenan, Jeremy David
United States, San Francisco
F.i. Proctor Foundation
United States, San Francisco
University of California, San Francisco
Lietman, Thomas M.
United States, San Francisco
F.i. Proctor Foundation
United States, San Francisco
University of California, San Francisco
Statistics
Citations: 8
Authors: 7
Affiliations: 3
Identifiers
Doi:
10.1371/journal.pntd.0003682
ISSN:
19352727
Study Design
Cross Sectional Study
Study Approach
Quantitative