{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T18:05:14Z","timestamp":1784311514534,"version":"3.55.0"},"reference-count":90,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T00:00:00Z","timestamp":1784246400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      <jats:bold>Despite<\/jats:bold>\n                      the high prevalence of HIV infection among young women in sub-Saharan Africa (SSA), the adoption of HIV testing and counseling remains low. The uptake of HIV testing among young women in SSA remains poorly understood. Consequently, identifying the spatial distributions, prevalence, and factors that influence HIV testing among these marginalized groups of the population is crucial for policymakers, implementers, and researchers to achieve the 2030 Sustainable Development Goal plans to tackle the progress of the disease. Hence, the aim of this study was to assess HIV testing uptake, its spatial variation, and associated factors among young women in SSA from 2016 to 2023.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      <jats:bold>A<\/jats:bold>\n                      community-based cross-sectional study was conducted among a weighted sample of 140,054 young women in SSA using recent demographic and health survey data. Statistical analyses were performed using R version 4.3.2, incorporating spatial analysis techniques (autocorrelation, interpolation, and SaTScan) and Bayesian hierarchical logistic regression to identify the factors associated with HIV testing uptake. Model parameters were estimated using Markov Chain Monte Carlo methods, and the results are reported as adjusted odds ratios (ORs) with 95% credible intervals.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Spatial analyses showed that HIV testing uptake was distributed unevenly; hotspots of high HIV testing uptake were found in Eastern and Southern Africa, and low uptake areas were found in Western Africa. The overall pooled prevalence of HIV testing was 38.9%. Primary education, secondary\/higher education, employment, mass media, being married, widowed\/divorced, modern contraceptive use, age, middle income, rich, risky sexual behaviors, poor attitude towards HIV patients, literacy rates, alcohol consumption, pregnant women, female households, rural residents, and distance to a health facility were statistically significant outcome variables.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The study revealed that HIV testing uptake among young women in SSA is significantly below the 95%\u221295%\u221295% target, with notable spatial clustering and variation across countries. Key factors influencing uptake included age, education, HIV knowledge, employment, media exposure, marital status, pregnancy status, and socioeconomic conditions. To improve testing rates, it is critical to implement targeted policy interventions, such as enhancing educational programs, addressing sociocultural barriers, and expanding access to healthcare, particularly in underrepresented regions. These actions are essential not only to increase HIV testing coverage but also to move closer to achieving the 95-95-95 targets and reducing new HIV infections in SSA.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0353117","type":"journal-article","created":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T17:46:06Z","timestamp":1784310366000},"page":"e0353117","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Geographic inequalities in HIV testing uptake among young women in sub-Saharan Africa: A hierarchical bayesian spatial analysis"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0063-0999","authenticated-orcid":true,"given":"Bewuketu","family":"Terefe","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1355-2763","authenticated-orcid":true,"given":"Nebiyu Mekonnen","family":"Derseh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tadesse Awoke","family":"Ayele","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,7,17]]},"reference":[{"key":"pone.0353117.ref001","unstructured":"World Health Organization. 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