Niwa Miyu, Green Dylan, Smith Tyler, Klyn Brandon, Kamgwira Yohane, Allinder Sara, Hoege Deborah, Likumbo Suzike, Holmes Charles B, Kawalazira Gift, Chewere Linley
Cooper/Smith, Austin, Texas, USA.
National AIDS Commission, Lilongwe, Malawi.
BMJ Public Health. 2025 Sep 8;3(2):e002568. doi: 10.1136/bmjph-2025-002568. eCollection 2025.
Innovative and efficient methods are needed to identify remaining people living with HIV unaware of their status. Routine health information system (RHIS) data, widely available in high-burden HIV settings, may help target areas of high risk to deliver timely prevention services. Often underused, RHIS data were leveraged at the facility level to predict changes in HIV test positivity in Malawi.
From District Health Information Software-2 from January 2017 to March 2023, we analysed sexually transmitted infection (STI) cases and HIV tests and test results across 563 health facilities in Malawi. A multilevel model was employed to determine whether changes in STI diagnoses were predictive of changes in HIV test positivity. We considered STI types and their incubation periods, and controlled for facility type, ownership, quarter, season, zonal HIV and STI prevalence (2016 Population-Based HIV Impact Assessment).
Among 139 million HIV tests, overall positivity was 2.8%. Blantyre facilities had the highest positivity (6.0%) while those in the central-east zone had the lowest (1.8%). Key variables-changes in syndromic STI counts (lagged and cross-sectional)-showed weak or no associations with HIV positivity (OR: 1.01, CI: 1.01 to 1.01; OR: 1.00, CI: 1.00 to 1.00). However, contextual covariates, including zonal HIV prevalence (OR: 1.04, CI: 1.04 to 1.04), genital ulcers (OR: 1.16, CI: 1.16 to 1.16) and clinical STI diagnoses (OR: 1.29, CI: 1.29 to 1.29), were positively associated with HIV positivity.
In settings with high STI screening uptake, RHIS data can be used to monitor changes in STI diagnoses and contextual factors to identify HIV hotspots and guide targeted testing, prevention and treatment services.
需要创新且高效的方法来识别仍未意识到自己感染艾滋病毒的人群。在艾滋病毒高负担地区广泛可用的常规卫生信息系统（RHIS）数据，可能有助于确定高风险区域，以便提供及时的预防服务。RHIS数据常常未得到充分利用，本研究在马拉维的医疗机构层面利用这些数据来预测艾滋病毒检测阳性率的变化。
我们从2017年1月至2023年3月的地区卫生信息软件2中，分析了马拉维563家医疗机构的性传播感染（STI）病例、艾滋病毒检测及检测结果。采用多水平模型来确定STI诊断的变化是否可预测艾滋病毒检测阳性率的变化。我们考虑了STI类型及其潜伏期，并对医疗机构类型、所有权、季度、季节、地区艾滋病毒和STI患病率（2016年基于人群的艾滋病毒影响评估）进行了控制。
在1.39亿次艾滋病毒检测中，总体阳性率为2.8%。布兰太尔的医疗机构阳性率最高（6.0%），而中东部地区的医疗机构阳性率最低（1.8%）。关键变量——症状性STI病例数的变化（滞后和横断面）——与艾滋病毒阳性率的关联较弱或无关联（比值比：1.01，可信区间：1.01至1.01；比值比：1.00，可信区间：1.00至1.00）。然而，包括地区艾滋病毒患病率（比值比：1.04，可信区间：1.04至1.04）、生殖器溃疡（比值比：1.16，可信区间：1.16至1.16）和临床STI诊断（比值比：1.29，可信区间：1.29至1.29）在内的背景协变量与艾滋病毒阳性率呈正相关。
在STI筛查利用率高的环境中，RHIS数据可用于监测STI诊断和背景因素的变化，以识别艾滋病毒热点地区，并指导有针对性的检测、预防和治疗服务。
