인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Innovative strategies such as HIV self-testing (HIVST) are useful for identifying hard-to-reach people living with HIV/AIDS (PLHIV), especially in developing settings where considerable gaps still exist in reaching the first 95% UNAIDS target. We evaluated the effectiveness of HIVST in Cameroon using several distribution models and investigated the predictors of HIV seropositivity among self-testers. The study was conducted from 2021 to 2022 in three regions in Cameroon. HIVST kits were distributed according to 5 distribution models: antenatal, postnatal, maternal and child clinics (ANC/PNC/MCH); partners of PLHIV; workplace; community and HIV-testing services (HTS). Overall, 42,687 people received oral HIVST kits, among whom 15.6% were HIV first-testers. Approximately 85% reported on the test outcome; 2.3% (n = 825) were reactive, and 75.8% came for test confirmation. After the confirmation test, a concordance of 85% was found with the national algorithm. Overall, the HIV seroprevalence was 1.5% [95% CI: 1.4-1.6]; ANC/PNC/MCHC: 1.9%, partners of PLHIV: 6.9%, workplace: 0.5%, community: 0.1% and HTS: 7.0%, p < 0.001. The positivity rate among first-testers was 1.2%. Youths < 25 years had a lower seroprevalence (0.4%) than older people (2.6% and 2.7% for those aged 25-39 and those aged ≥ 40 years, respectively), p < 0.001. Seropositivity was negatively associated with secondary distribution, workplace model, community model and age < 25 years. On the other hand, partners of PLHIV model, HTS model, female sex and first-time-testers were positively associated with seropositivity. In Cameroon, oral HIVST is an effective approach for identifying undiagnosed PLHIV, especially when using ANC/PNC/MCHC, partners of PLHIV and HTS distribution models. However, to ensure the successful scale-up of HIVST in Cameroon, guidelines should be revised to fine-tune the target populations for HIVST and optimize the use of resources.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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