Evidence map›Paper›PMID 39138800›Full record

ArticleJournal of racial and ethnic health disparities2025

Structural Racism and HIV Pre-exposure Prophylaxis Use in the Nationwide US: A County-Level Analysis.

Fanghui Shi, Tianyue Mi, Xiaoming Li, Huan Ning, Zhenlong Li, Xueying Yang

Abstract read
In one paragraph

Article in Journal of racial and ethnic health disparities, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Fanghui ShiArnold School of Public Health, South Carolina SmartState Center for Healthcare Quality, University of South Carolina, Columbia, SC, 29208, USA. FSHI@email.sc.edu.ORCID 0000-0002-5603-5398
Tianyue MiArnold School of Public Health, South Carolina SmartState Center for Healthcare Quality, University of South Carolina, Columbia, SC, 29208, USA.
Xiaoming LiArnold School of Public Health, South Carolina SmartState Center for Healthcare Quality, University of South Carolina, Columbia, SC, 29208, USA.
Huan NingGeoinformation and Big Data Research Laboratory, Department of Geography, The Pennsylvania State University, University Park, PA, 16802, USA.
Zhenlong LiGeoinformation and Big Data Research Laboratory, Department of Geography, The Pennsylvania State University, University Park, PA, 16802, USA.
Xueying YangArnold School of Public Health, South Carolina SmartState Center for Healthcare Quality, University of South Carolina, Columbia, SC, 29208, USA.

Funding

Patterns and predictors of racial/ethnic disparities in HIV care continuum in the SouthR21NR021079 · NINR · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI YANG, XUEYING · 2023 to 2023
$399k
NINR NIH HHS R21 NR021079NINR NIH HHS R21NR021079
6 · The paper itself

Abstract

backgroundStructural racism contributes to geographical inequalities in pre-exposure prophylaxis (PrEP) coverage in the United States (US). This study aims to investigate county-level variability in PrEP utilization across diverse dimensions of structural racism.

methodsThe 2013-2021 nationwide county-level PrEP rate and PrEP-to-need ratio (PNR) data were retrieved from AIDSVu. PrEP rate was defined as the number of PrEP users per 100,000 population, and PNR was defined as the ratio of PrEP users to new HIV diagnoses per calendar year. Linear mixed effect regression was employed to identify associations of county-level structural racism (e.g., structural racism in housing and socioeconomic status) with PrEP rate and PNR on a nationwide scale of the US.

resultsFrom 2013 to 2021, the mean PrEP rate and PNR increased from 3.62 to 71.10 and from 0.39 to 10.20, respectively. Counties with more structural racism in housing were more likely to have low PrEP rates (adjusted β =  - 5.80, 95% CI [- 8.84, - 2.75]). Higher PNR was found in counties with lower structural racism in socioeconomic status (adjusted β =  - 2.64, 95% CI [- 3.68, - 1.61]). Regionally, compared to the Midwest region, counties in the West region were more likely to have higher PrEP rate (adjusted β = 30.99, 95% CI [22.19, 39.80]), and counties in the South had lower PNR (adjusted β =  - 1.87, 95% CI [- 2.57, - 1.17]).

conclusionsCounty-level structural racism plays a crucial role in understanding the challenges of scaling up PrEP coverage. The findings underscore the importance of tailored strategies across different regions and provide valuable insights for future interventions to optimize PrEP implementation.

Indexed as

Healthcare DisparitiesHIV InfectionsPre-Exposure ProphylaxisRacismAdultFemaleHumansMaleMiddle AgedUnited StatesCounty levelHIVPre-exposure prophylaxisStructural racism

Identifiers

PMID39138800
PMCPMC11822042

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.