{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T19:25:38Z","timestamp":1792524338269,"version":"4.5.0"},"reference-count":59,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T00:00:00Z","timestamp":1668643200000},"content-version":"vor","delay-in-days":16,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Int AIDS Soc."],"published-print":{"date-parts":[[2022,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Introduction<\/jats:title><jats:p>Models that project the impact and cost\u2010effectiveness of HIV pre\u2010exposure prophylaxis (PrEP) must specify how PrEP use aligns with HIV exposure. We hypothesized that varying PrEP use according to individual\u2010level partnership dynamics rather than prioritization to population subgroups based on average risk will result in larger incidence reductions and greater efficiency.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We used an individual\u2010based network transmission model calibrated to HIV dynamics in Eswatini to simulate PrEP use among individuals ages 15\u201334 between 2022 and 2031 under two paradigms of PrEP delivery: \u201cRisk Group\u201d and \u201cPartnership.\u201d In the \u201cRisk Group\u201d paradigm, we varied PrEP coverage by risk groups (low, medium and high) defined by average partnership frequency and concurrency. In the \u201cPartnership\u201d paradigm, all individuals are potentially eligible for PrEP, but we assumed use occurs only during partnerships and varied prioritization by partner HIV status (no prioritization to high prioritization with HIV\u2010positive partners). We calculated person\u2010time on PrEP and incidence relative to a no PrEP scenario and estimated efficiency as the person\u2010years of PrEP needed to avert one additional infection (NNT).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In the Risk Group paradigm, restricting PrEP to the high\u2010risk group was the most efficient (NNT = 17), but the number of infections averted was limited by the small size of the high\u2010risk group. Expanding PrEP use to all risk groups averted up to three times more infections but with lower efficiency (NNT = 202). PrEP use under the Partnership paradigm was 2\u20136 times more efficient (NNT = 33\u2013102) than the Risk Group paradigm with all groups eligible for PrEP. A 33% reduction in incidence among 15\u2010 to 34\u2010year\u2010olds was achieved at 46% (95% CI: 39\u201352%) PrEP coverage in the Risk Group paradigm and 6% (95% CI: 5\u20137%) to 17% (95% CI: 14\u201320%) in the Partnership paradigm.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Modelling PrEP use based on risk groups resulted in a sharp trade\u2010off between PrEP efficiency and impact, whereas PrEP use predicated on partnerships resulted in much higher efficiency for widespread PrEP availability. Model estimates of PrEP impact and cost\u2010effectiveness in generalized epidemics are strongly influenced by assumptions about how PrEP use aligns with individual\u2010level HIV exposure heterogeneity.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jia2.26034","type":"journal-article","created":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T11:50:11Z","timestamp":1668685811000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["The impact of prevention\u2010effective PrEP use on HIV incidence: a mathematical modelling study"],"prefix":"10.1002","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3660-6330","authenticated-orcid":false,"given":"D. 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