Cargando…

Optimizing Screening for HIV

BACKGROUND: The HIV epidemic is unevenly distributed throughout the United States, even within neighborhoods. This study evaluated how effectively current testing approaches reached persons at risk for HIV infection across San Diego (SD) County, California. METHODS: HIV case and testing data, sexual...

Descripción completa

Detalles Bibliográficos
Autores principales: Chaillon, Antoine, Hoenigl, Martin, Freitas, Lorri, Feldman, Haruna, Tilghman, Winston, Wang, Lawrence, Smith, Davey, Little, Susan, Mehta, Sanjay R
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7009491/
https://www.ncbi.nlm.nih.gov/pubmed/32055638
http://dx.doi.org/10.1093/ofid/ofaa024
_version_ 1783495678504206336
author Chaillon, Antoine
Hoenigl, Martin
Freitas, Lorri
Feldman, Haruna
Tilghman, Winston
Wang, Lawrence
Smith, Davey
Little, Susan
Mehta, Sanjay R
author_facet Chaillon, Antoine
Hoenigl, Martin
Freitas, Lorri
Feldman, Haruna
Tilghman, Winston
Wang, Lawrence
Smith, Davey
Little, Susan
Mehta, Sanjay R
author_sort Chaillon, Antoine
collection PubMed
description BACKGROUND: The HIV epidemic is unevenly distributed throughout the United States, even within neighborhoods. This study evaluated how effectively current testing approaches reached persons at risk for HIV infection across San Diego (SD) County, California. METHODS: HIV case and testing data, sexually transmitted infection (STI) data, and sociodemographic data for SD County were collected from the SD Health and Human Services Agency and the “Early Test” community-based HIV screening program between 1998 and 2016. Relationships between HIV diagnoses, HIV prevalence, and STI diagnoses with screening at the ZIP code level were evaluated. RESULTS: Overall, 379 074 HIV tests were performed. The numbers of HIV tests performed on persons residing in a ZIP code or region overall strongly correlated with prevalent HIV cases (R(2) = .714), new HIV diagnoses (R(2) = .798), and STI diagnoses (R(2) = .768 [chlamydia], .836 [gonorrhea], .655 [syphilis]) in those regions. ZIP codes with the highest HIV prevalence had the highest number of tests per resident and fewest number of tests per diagnosis. Even though most screening tests occurred at fixed venues located in high-prevalence areas, screening of residents from lower-prevalence areas was mostly proportional to the prevalence of HIV and rates of new HIV and STI diagnoses in those locales. CONCLUSIONS: This study supported the ability of a small number of standalone testing centers to reach at-risk populations dispersed across SD County. These methods can also be used to highlight geographic areas or demographic segments that may benefit from more intensive screening.
format Online
Article
Text
id pubmed-7009491
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-70094912020-02-13 Optimizing Screening for HIV Chaillon, Antoine Hoenigl, Martin Freitas, Lorri Feldman, Haruna Tilghman, Winston Wang, Lawrence Smith, Davey Little, Susan Mehta, Sanjay R Open Forum Infect Dis Major Article BACKGROUND: The HIV epidemic is unevenly distributed throughout the United States, even within neighborhoods. This study evaluated how effectively current testing approaches reached persons at risk for HIV infection across San Diego (SD) County, California. METHODS: HIV case and testing data, sexually transmitted infection (STI) data, and sociodemographic data for SD County were collected from the SD Health and Human Services Agency and the “Early Test” community-based HIV screening program between 1998 and 2016. Relationships between HIV diagnoses, HIV prevalence, and STI diagnoses with screening at the ZIP code level were evaluated. RESULTS: Overall, 379 074 HIV tests were performed. The numbers of HIV tests performed on persons residing in a ZIP code or region overall strongly correlated with prevalent HIV cases (R(2) = .714), new HIV diagnoses (R(2) = .798), and STI diagnoses (R(2) = .768 [chlamydia], .836 [gonorrhea], .655 [syphilis]) in those regions. ZIP codes with the highest HIV prevalence had the highest number of tests per resident and fewest number of tests per diagnosis. Even though most screening tests occurred at fixed venues located in high-prevalence areas, screening of residents from lower-prevalence areas was mostly proportional to the prevalence of HIV and rates of new HIV and STI diagnoses in those locales. CONCLUSIONS: This study supported the ability of a small number of standalone testing centers to reach at-risk populations dispersed across SD County. These methods can also be used to highlight geographic areas or demographic segments that may benefit from more intensive screening. Oxford University Press 2020-01-19 /pmc/articles/PMC7009491/ /pubmed/32055638 http://dx.doi.org/10.1093/ofid/ofaa024 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of Infectious Diseases Society of America. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Major Article
Chaillon, Antoine
Hoenigl, Martin
Freitas, Lorri
Feldman, Haruna
Tilghman, Winston
Wang, Lawrence
Smith, Davey
Little, Susan
Mehta, Sanjay R
Optimizing Screening for HIV
title Optimizing Screening for HIV
title_full Optimizing Screening for HIV
title_fullStr Optimizing Screening for HIV
title_full_unstemmed Optimizing Screening for HIV
title_short Optimizing Screening for HIV
title_sort optimizing screening for hiv
topic Major Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7009491/
https://www.ncbi.nlm.nih.gov/pubmed/32055638
http://dx.doi.org/10.1093/ofid/ofaa024
work_keys_str_mv AT chaillonantoine optimizingscreeningforhiv
AT hoeniglmartin optimizingscreeningforhiv
AT freitaslorri optimizingscreeningforhiv
AT feldmanharuna optimizingscreeningforhiv
AT tilghmanwinston optimizingscreeningforhiv
AT wanglawrence optimizingscreeningforhiv
AT smithdavey optimizingscreeningforhiv
AT littlesusan optimizingscreeningforhiv
AT mehtasanjayr optimizingscreeningforhiv