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The distribution of ocular Chlamydia prevalence across Tanzanian communities where trachoma is declining.

Rahman SA, West SK, Mkocha H, Munoz B, Porco TC, Keenan JD, Lietman TM - PLoS Negl Trop Dis (2015)

Bottom Line: Local programs may benefit if a single survey could reveal whether infection was headed towards elimination.We fit multiple continuous distributions to the Tanzanian data and found the exponential gave the best approximation.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.

View Article: PubMed Central - PubMed

Affiliation: F.I. Proctor Foundation, San Francisco, California, United States of America.

ABSTRACT

Background: 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.

Methods/findings: 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.

Conclusions: 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.

No MeSH data available.


Related in: MedlinePlus

Exponential distribution and other distributions which cannot mimic the exponential, fit to Tanzanian data.This figure shows the Tanzanian trachoma prevalence data as a histogram in the background along with the fits of various distributions which cannot mimic the exponential. The black line indicates the exponential distribution fit to the data, along with the 95% confidence interval as gray shading. The best fit of all non-exponential distributions fall outside the 95% confidence interval (gray shading) of the exponential and give worse fit to the data.
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pntd.0003682.g002: Exponential distribution and other distributions which cannot mimic the exponential, fit to Tanzanian data.This figure shows the Tanzanian trachoma prevalence data as a histogram in the background along with the fits of various distributions which cannot mimic the exponential. The black line indicates the exponential distribution fit to the data, along with the 95% confidence interval as gray shading. The best fit of all non-exponential distributions fall outside the 95% confidence interval (gray shading) of the exponential and give worse fit to the data.

Mentions: The Cauchy, log-normal, chi, and chi-squared distributions do not include the exponential as a special or limiting case. These distributions gave far worse log likelihoods and AICc than the exponential. The fit of these distributions to the data is shown alongside the exponential in Fig. 2.


The distribution of ocular Chlamydia prevalence across Tanzanian communities where trachoma is declining.

Rahman SA, West SK, Mkocha H, Munoz B, Porco TC, Keenan JD, Lietman TM - PLoS Negl Trop Dis (2015)

Exponential distribution and other distributions which cannot mimic the exponential, fit to Tanzanian data.This figure shows the Tanzanian trachoma prevalence data as a histogram in the background along with the fits of various distributions which cannot mimic the exponential. The black line indicates the exponential distribution fit to the data, along with the 95% confidence interval as gray shading. The best fit of all non-exponential distributions fall outside the 95% confidence interval (gray shading) of the exponential and give worse fit to the data.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4376383&req=5

pntd.0003682.g002: Exponential distribution and other distributions which cannot mimic the exponential, fit to Tanzanian data.This figure shows the Tanzanian trachoma prevalence data as a histogram in the background along with the fits of various distributions which cannot mimic the exponential. The black line indicates the exponential distribution fit to the data, along with the 95% confidence interval as gray shading. The best fit of all non-exponential distributions fall outside the 95% confidence interval (gray shading) of the exponential and give worse fit to the data.
Mentions: The Cauchy, log-normal, chi, and chi-squared distributions do not include the exponential as a special or limiting case. These distributions gave far worse log likelihoods and AICc than the exponential. The fit of these distributions to the data is shown alongside the exponential in Fig. 2.

Bottom Line: Local programs may benefit if a single survey could reveal whether infection was headed towards elimination.We fit multiple continuous distributions to the Tanzanian data and found the exponential gave the best approximation.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.

View Article: PubMed Central - PubMed

Affiliation: F.I. Proctor Foundation, San Francisco, California, United States of America.

ABSTRACT

Background: 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.

Methods/findings: 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.

Conclusions: 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.

No MeSH data available.


Related in: MedlinePlus