{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T03:48:18Z","timestamp":1793332098456,"version":"4.5.0"},"reference-count":40,"publisher":"American Medical Association (AMA)","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Netw Open"],"abstract":"\n Importance<\/jats:title>\n Pre-exposure prophylaxis (PrEP) is a proven effective intervention to reduce the risk for HIV infection. Critically, changes in policies that lead to increased out-of-pocket PrEP costs or that decrease access to proximate PrEP locations could reduce PrEP coverage, resulting in excess HIV infections and costs.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To estimate how decreases in PrEP coverage that would be likely results of federal policy changes may be associated with new HIV infections and their costs.<\/jats:p>\n <\/jats:sec>\n \n Design, Setting, and Participants<\/jats:title>\n In this economic evaluation, US population-based data sources were used to describe population-level PrEP use and new diagnoses under different hypothetical changes in PrEP coverage. Estimations of excess HIV infections under different policy changes were conducted using parameters from a previously published ecological model of the association between PrEP coverage and new HIV infections. Data on PrEP prescriptions from January 1, 2012, to December 31, 2022, and estimates from a previously reported original clinical model, which described decreases in HIV diagnoses associated with increases in population PrEP use, were analyzed. Data were analyzed from February 25 to May 23, 2025.<\/jats:p>\n <\/jats:sec>\n \n Exposure<\/jats:title>\n Change in PrEP coverage.<\/jats:p>\n <\/jats:sec>\n \n Main Outcomes and Measures<\/jats:title>\n Estimated change in new HIV infections under different assumed reductions in PrEP coverage, costs of treatment for avoidable HIV infections, and net costs of avoidable infections after accounting for costs of PrEP medications. We also estimated increases in new HIV diagnoses associated with hypothesized levels of decreasing PrEP coverage, and the costs of treating infections not averted.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n In 2012, there were 9565 PrEP users in the US; they were predominately male (5857 [61.2%]), and 7109 (74.3%) were aged 25 to 54 years. By race and ethnicity, 1235 PrEP users (12.9%) were Hispanic, 1857 (19.4%) were non-Hispanic Black, and 5404 (56.5%) were non-Hispanic White. Based on analyses of data from a census of US PrEP users including 17\u202f333\u202f732 person-years of time using PrEP, an absolute 3.3% annual reduction in PrEP coverage during the next 10 years (eg, 2023 to 2033) would result in 8618 avoidable HIV infections, with lifetime medical costs of $3.6 billion (discounted) for treatment.<\/jats:p>\n <\/jats:sec>\n \n Conclusions and Relevance<\/jats:title>\n In this economic evaluation estimating effects of the possible health care policy changes on HIV transmission, findings suggest that even modest reductions in PrEP coverage would result in thousands of avoidable HIV infections and billions of dollars of increases in net health care costs.<\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jamanetworkopen.2025.31341","type":"journal-article","created":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T15:00:28Z","timestamp":1757602828000},"page":"e2531341","source":"Crossref","is-referenced-by-count":8,"title":["Excess HIV Infections and Costs Associated With Reductions in HIV Prevention Services in the US"],"prefix":"10.1001","volume":"8","author":[{"given":"Patrick S.","family":"Sullivan","sequence":"first","affiliation":[{"name":"Department of Epidemiology, Emory University, Atlanta, Georgia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kristin M.","family":"Wall","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Emory University, Atlanta, Georgia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marta","family":"Juhasz","sequence":"additional","affiliation":[{"name":"Saluda Analytics, Telki, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephanie","family":"DuBose","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Emory University, Atlanta, Georgia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffrey S.","family":"Crowley","sequence":"additional","affiliation":[{"name":"O\u2019Neill 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