2026-10-13T14:04:14Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33967490 2026-09-22T17:32:49Z category_7 category_21 category_45 category_734 category_811 category_106 portal_5 item_type_3 month_year_09_2026

<p>Dataset.</p> Laban Muteebwa (18784310) Patience A. Muwanguzi (11914169) Shivan Nuwasiima (23214428) Ivan Ahimbisibwe (18784304) Edson Atwine (23235537) Dan Muramuzi (17720111) Cathbert Tumusiime (18784307) Fred C. Semitala (4386067) Joanita Nangendo (4386058) Medicine Biotechnology Sociology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Science Policy mixed logit model mean age 19 main effects model discrete choice experiment 60 %), followed sd ): 2 based distribution model based convenience sampling acquiring hiv living primary versus secondary 23 )), primary improving testing coverage primary delivery channel based versus community ci ): 0 primary delivery secondary delivery delivery channel 2 %). 17 )), hiv using hiv self young women urban settings standard provider standard deviation significantly influence sample type predicted uptake optimize uptake may 2025 marginal utilities individual attributes inadequately reached important attribute higher risk designing models december 2024 crucial innovation confidence interval 8 years 71 ), 6 ), 24 )). &# 8217 <div><p>HIV self-testing (HIVST) is a crucial innovation for improving testing coverage, particularly among adolescent girls and young women (AGYW) who are inadequately reached by standard provider-assisted HIV testing approaches. Understanding their preferences for HIVST services is essential for designing models that optimize uptake. We assessed the HIVST preferences among AGYW at a higher risk of acquiring HIV living in urban Uganda. We enrolled AGYW at a higher risk of HIV using a community-based convenience sampling in a discrete choice experiment (DCE). Each participant completed eight choice sets, each comprising of two hypothetical HIVST service alternatives and an opt-out option. The four attributes included level of assistance (assisted versus unassisted), distribution model (facility-based versus community-based), delivery channel (primary versus secondary) and sample type (oral swab versus blood). A mixed logit model was used to estimate the marginal utilities, relative importance (RI), HIVST profile utilities and predicted uptake (compared to opt-out). Between December 2024 and May 2025, 340 participants were enrolled with mean age 19.8 years (standard deviation (SD): 2.6), and 65.9% of them were not in school. In the main effects model, participants preferred; facility-based over community-based distribution model (β = 0.14 (95% confidence interval (CI): 0.06, 0.23)), primary over secondary delivery channel (β = 0.09 (95% CI: 0.00, 0.17)), and unassisted over assisted testing (β = 0.46 (95% CI: 0.37, 0.54). The sample type didn’t significantly influence the preferences of participants to use HIVST (β = 0.05 (95%CI: -0.04, 0.13). The level of assistance emerged as the most important attribute (RI: 60%), followed by facility-based distribution model (RI: 18.9%) and primary delivery channel (RI: 12.2%). There was a significant interaction between delivery channel and level of assistance (β = 0.66 (95% CI: 0.07, 1.24)). Primary delivery was preferred over secondary delivery when testing was unassisted (β = 0.41 (95% CI: 0.11, 0.71), but not when testing was assisted (β = -0.25 (95% CI: -0.56, 0.06). AGYW preferred HIVST models combining primary delivery with unassisted testing while facility-based distribution also increased preference. This highlights the importance of considering both individual attributes and their interaction when designing HIV delivery models.</p></div> 2026-09-22T17:32:45Z Dataset Dataset 10.1371/journal.pgph.0005431.s003 https://figshare.com/articles/dataset/_p_Dataset_p_/33967490 CC BY 4.0