Imperial College London
Alternate

ProfessorPeterWhite

Faculty of Medicine, School of Public Health

Professor of Public Health Modelling
 
 
 
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Contact

 

p.white Website

 
 
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Location

 

Praed StreetSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Lewis:2017:10.1097/EDE.0000000000000655,
author = {Lewis, J and White, PJ},
doi = {10.1097/EDE.0000000000000655},
journal = {Epidemiology},
pages = {492--502},
title = {Estimating local chlamydia incidence and prevalence using surveillance data},
url = {http://dx.doi.org/10.1097/EDE.0000000000000655},
volume = {28},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Background: Understanding patterns of chlamydia prevalence is important for addressing inequalities and planning cost-effective control programs. Population-based surveys are costly; the best data for England come from the Natsal national surveys which are only available once per decade, and are nationally representative but not powered to compare prevalence in different localities. Prevalence estimates at finer spatial and temporal scales are required.Methods: We present a method for estimating local prevalence by modeling the infection, testing and treatment processes. Prior probability distributions for parameters describing natural history and treatment-seeking behavior are informed by the literature or calibrated using national prevalence estimates. By combining them with surveillance data on numbers of chlamydia tests and diagnoses, we obtain estimates of local screening rates, incidence and prevalence. We illustrate the method by application to data from England.Results: Our estimates of national prevalence by age group agree with the Natsal-3 survey. They could be improved by additional information on the number of diagnosed cases that were asymptomatic. There is substantial local-level variation in prevalence, with more infection in deprived areas. Incidence in each sex is strongly correlated with prevalence in the other. Importantly, we find that positivity (the proportion of tests which were positive) does not provide a reliable proxy for prevalence.Conclusion: This approach provides local chlamydia prevalence estimates from surveillance data, which could inform analyses to identify and understand local prevalence patterns and assess local programs. Estimates could be more accurate if surveillance systems recorded additional information, including on symptoms.
AU  - Lewis,J
AU  - White,PJ
DO  - 10.1097/EDE.0000000000000655
EP  - 502
PY  - 2017///
SN  - 1531-5487
SP  - 492
TI  - Estimating local chlamydia incidence and prevalence using surveillance data
T2  - Epidemiology
UR  - http://dx.doi.org/10.1097/EDE.0000000000000655
UR  - http://hdl.handle.net/10044/1/42837
VL  - 28
ER  -
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