Cargando…

Estimating Local Chlamydia Incidence and Prevalence Using Surveillance Data

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 na...

Descripción completa

Detalles Bibliográficos
Autores principales: Lewis, Joanna, White, Peter J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5457828/
https://www.ncbi.nlm.nih.gov/pubmed/28306613
http://dx.doi.org/10.1097/EDE.0000000000000655
_version_ 1783241617976590336
author Lewis, Joanna
White, Peter J.
author_facet Lewis, Joanna
White, Peter J.
author_sort Lewis, Joanna
collection PubMed
description 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. See video abstract at, http://links.lww.com/EDE/B211.
format Online
Article
Text
id pubmed-5457828
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher Lippincott Williams & Wilkins
record_format MEDLINE/PubMed
spelling pubmed-54578282017-06-13 Estimating Local Chlamydia Incidence and Prevalence Using Surveillance Data Lewis, Joanna White, Peter J. Epidemiology Infectious Diseases 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. See video abstract at, http://links.lww.com/EDE/B211. Lippincott Williams & Wilkins 2017-07 2017-06-01 /pmc/articles/PMC5457828/ /pubmed/28306613 http://dx.doi.org/10.1097/EDE.0000000000000655 Text en Copyright © 2017 The Author(s). Published by Wolters Kluwer Health, Inc. This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY) (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Infectious Diseases
Lewis, Joanna
White, Peter J.
Estimating Local Chlamydia Incidence and Prevalence Using Surveillance Data
title Estimating Local Chlamydia Incidence and Prevalence Using Surveillance Data
title_full Estimating Local Chlamydia Incidence and Prevalence Using Surveillance Data
title_fullStr Estimating Local Chlamydia Incidence and Prevalence Using Surveillance Data
title_full_unstemmed Estimating Local Chlamydia Incidence and Prevalence Using Surveillance Data
title_short Estimating Local Chlamydia Incidence and Prevalence Using Surveillance Data
title_sort estimating local chlamydia incidence and prevalence using surveillance data
topic Infectious Diseases
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5457828/
https://www.ncbi.nlm.nih.gov/pubmed/28306613
http://dx.doi.org/10.1097/EDE.0000000000000655
work_keys_str_mv AT lewisjoanna estimatinglocalchlamydiaincidenceandprevalenceusingsurveillancedata
AT whitepeterj estimatinglocalchlamydiaincidenceandprevalenceusingsurveillancedata