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Racial and Ethnic Health Disparities"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Structural 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The 2013\u20132021 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      From 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\n                      <jats:italic>\u03b2<\/jats:italic>\n                      \u2009=\u2009\u2009\u2212\u20095.80, 95% CI [\u2212\u20098.84,\u2009\u2212\u20092.75]). Higher PNR was found in counties with lower structural racism in socioeconomic status (adjusted\n                      <jats:italic>\u03b2<\/jats:italic>\n                      \u2009=\u2009\u2009\u2212\u20092.64, 95% CI [\u2212\u20093.68,\u2009\u2212\u20091.61]). Regionally, compared to the Midwest region, counties in the West region were more likely to have higher PrEP rate (adjusted\n                      <jats:italic>\u03b2<\/jats:italic>\n                      \u2009=\u200930.99, 95% CI [22.19, 39.80]), and counties in the South had lower PNR (adjusted\n                      <jats:italic>\u03b2<\/jats:italic>\n                      \u2009=\u2009\u2009\u2212\u20091.87, 95% CI [\u2212\u20092.57,\u2009\u2212\u20091.17]).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>County-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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s40615-024-02127-5","type":"journal-article","created":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T12:02:16Z","timestamp":1723550536000},"page":"3233-3239","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Structural Racism and HIV Pre-exposure Prophylaxis Use in the Nationwide US: A County-Level Analysis"],"prefix":"10.1007","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5603-5398","authenticated-orcid":false,"given":"Fanghui","family":"Shi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianyue","family":"Mi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoming","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huan","family":"Ning","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenlong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueying","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,13]]},"reference":[{"issue":"9","key":"2127_CR1","doi-asserted-by":"publisher","first-page":"844","DOI":"10.1001\/jama.2019.1343","volume":"321","author":"AS Fauci","year":"2019","unstructured":"Fauci AS, et al. 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The AIDSVu dataset is openly accessible and contains de-identified information, ensuring the privacy and confidentiality of individuals. According to the guidelines of University of South Carolina and federal regulations, research utilizing such publicly available de-identified data does not require approval by an Institutional Review Board (IRB). Therefore, no IRB approval was necessary for this study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Publish"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}