Optimizing Screening for HIV.
Optimizing Screening for HIV.
复制标题
优化艾滋病毒筛查。
DOI：
10.1093/ofid/ofaa024
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发表时间：
2020
影响因子：
4.2
通讯作者：
Mehta,SanjayR
中科院分区：
文献类型：
--
作者：
Chaillon,Antoine;Hoenigl,Martin;Freitas,Lorri;Feldman,Haruna;Tilghman,Winston;Wang,Lawrence;Smith,Davey;Little,Susan;Mehta,SanjayR
背景HIV疫情在美国各地分布不均，甚至在社区内也是如此。这项研究评估了目前的检测方法在加利福尼亚州圣地亚哥(SD)县艾滋病毒感染高危人群中的有效性。方法收集了1998至2016年间SD县的HIV病例和检测数据、性传播感染(STI)数据和社会人口学数据。评估HIV诊断、HIV流行率和STI诊断与邮政编码水平筛查之间的关系。对居住在邮政编码或地区的人进行的艾滋病毒检测的数量总体上与这些地区的艾滋病毒流行病例(R2=.714)、新的艾滋病毒诊断(R2=.798)和性传播感染诊断(R2=.768[衣原体]、.836[淋病]、.655[梅毒])密切相关。艾滋病毒流行率最高的邮政编码每个居民的检测次数最多，而每次诊断的检测次数最少。尽管大多数筛查检测是在高发区的固定地点进行的，但对低流行区居民的筛查主要与这些地区的HIV流行率以及HIV和STI新诊断率成正比。结论本研究支持少数独立检测中心覆盖分散在SD县的高危人群的能力。这些方法也可用于突出可能受益于更密集筛查的地理区域或人口部分。
BackgroundThe 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.MethodsHIV 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.ResultsOverall, 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 (R2= .714), new HIV diagnoses (R2= .798), and STI diagnoses (R2= .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.ConclusionsThis 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.