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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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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 |
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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 |
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