{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T09:11:04Z","timestamp":1790327464925,"version":"4.1.0"},"reference-count":33,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"content-version":"vor","delay-in-days":14,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Int AIDS Soc."],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>The availability of highly efficacious HIV pre\u2010exposure prophylaxis (PrEP) makes it unethical or infeasible to conduct inactive\/placebo\u2010controlled trials to evaluate new HIV prevention options. As a result, randomized active\u2010control non\u2010inferiority trials are typically employed; however, they require large sample sizes and extended follow\u2010up. Moreover, their results can be hard to interpret. An alternative approach compares HIV incidence among individuals receiving a new PrEP product to a counterfactual incidence estimate\u2014an estimate of what the HIV incidence would have been in the absence of PrEP.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We leveraged data from three studies (AMP, Sabes and ImPrEP seroincidence) conducted among men who have sex with men and transgender persons in Lima, Peru, between 2013 and 2022 to estimate the counterfactual HIV incidence for two target populations represented by a clinical trial (AMP) and a non\u2010interventional cohort study (Sabes). We evaluated three estimation methods: prospective cohort follow\u2010up, recency testing and rectal gonorrhoea (RG) approaches. These approaches were compared directly using data from the same study, and we further assessed population adjustment approaches by comparing estimates across studies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>All three methods produced consistent HIV incidence estimates when applied to data collected from the same study. \u200bHowever, estimates differed when data from external studies were used, even after propensity score (PS) adjustment. For example, estimates for the AMP population using Sabes or ImPrEP data remained higher than the AMP gold\u2010standard estimate. In contrast, adjusted estimates for the Sabes population using AMP or ImPrEP data were lower than the Sabes follow\u2010up estimate. These differences underscore the challenges of applying external data and highlight the role of unmeasured population heterogeneity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Counterfactual HIV incidence estimates can support evaluation of new PrEP agents when direct placebo comparisons are not possible. Estimates from recency\u2010testing and RG approaches are promising when prospective follow\u2010up is infeasible, but their validity depends on data quality and population similarity. PS methods improve comparability across populations but cannot fully account for unobserved differences. Triangulating across methods and sources can improve confidence in counterfactual estimates, which should be interpreted with careful attention to population context.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jia2.70204","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T08:14:44Z","timestamp":1789460084000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparing Approaches for Estimating Counterfactual HIV Incidence Among Populations With High Vulnerability to HIV in Lima, Peru: A Multi\u2010Study Comparative Analysis"],"prefix":"10.1002","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6797-5468","authenticated-orcid":false,"given":"Fei","family":"Gao","sequence":"first","affiliation":[{"name":"Vaccine and Infectious Disease Division Fred Hutchinson Cancer Research Center  Seattle Washington USA"},{"name":"Public Health Sciences Division Fred 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USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6867-1271","authenticated-orcid":false,"given":"Ricardo","family":"Alfaro","sequence":"additional","affiliation":[{"name":"Centro de Investigaciones Tecnol\u00f3gicas Biom\u00e9dicas y Medioambientales (CITBM)  Lima Per\u00fa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Delia","family":"Pinto\u2010Santini","sequence":"additional","affiliation":[{"name":"Vaccine and Infectious Disease Division Fred Hutchinson Cancer Research Center  Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2557-601X","authenticated-orcid":false,"given":"Thiago S.","family":"Torres","sequence":"additional","affiliation":[{"name":"Funda\u00e7\u00e3o Oswaldo Cruz Instituto Nacional de Infectologia Evandro Chagas  Rio de Janeiro 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