Birri Makota Rutendo Beauty, Musenge Eustasius
Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
PLOS Glob Public Health. 2023 Sep 14;3(9):e0001717. doi: 10.1371/journal.pgph.0001717. eCollection 2023.
Over the years, numerous modelling studies have been proposed to estimate HIV incidence. As a result, this study aimed to evaluate two alternative methods for predicting HIV incidence in Zimbabwe between 2005 and 2015. We estimated HIV incidence from seroprevalence data using the catalytic and Farrington-2-parameter models. Data were obtained from 2005-06, 2010-11, and 2015 Zimbabwe Demographic Health Survey (ZDHS). These models were validated at the micro and macro-level using community-based cohort incidence and empirical estimates from UNAIDS EPP/SPECTRUM, respectively. The HIV incidence for the catalytic model was 0.32% (CI: 0.28%, 0.36%), 0.36% (CI: 0.33%, 0.39%), and 0.28% (CI: 0.26%, 0.30%), for the years 2005-06, 2010-11, and 2015, respectively. The HIV incidence for the Farrington model was 0.21% (CI: 0.16%, 0.26%), 0.22% (CI: 0.20%, 0.25%), and 0.19% (CI: 0.16%, 0.22%), for the years 2005-06, 2010-11, and 2015, respectively. According to these findings, the catalytic model estimated a higher HIV incidence rate than the Farrington model. Compared to cohort estimates, the estimates were within the observed 95% confidence interval, with 88% and 75% agreement for the catalytic and Farrington models, respectively. The limits of agreement observed in the Bland-Altman plot were narrow for all plots, indicating that our model estimates were comparable to cohort estimates. Compared to UNAIDS estimates, the catalytic model predicted a progressive increase in HIV incidence for males throughout all survey years. Without a doubt, HIV incidence declined with each subsequent survey year for all models. To improve programmatic and policy decisions in the national HIV response, we recommend the triangulation of multiple methods for incidence estimation and interpretation of results. Multiple estimating approaches should be considered to reduce uncertainty in the estimations from various models.
多年来，人们提出了许多建模研究来估计艾滋病毒发病率。因此，本研究旨在评估两种预测2005年至2015年津巴布韦艾滋病毒发病率的替代方法。我们使用催化模型和法林顿双参数模型从血清阳性率数据中估计艾滋病毒发病率。数据来自2005 - 2006年、2010 - 2011年和2015年的津巴布韦人口与健康调查（ZDHS）。这些模型分别在微观和宏观层面使用基于社区的队列发病率以及联合国艾滋病规划署EPP/SPECTRUM的实证估计进行了验证。催化模型在2005 - 2006年、2010 - 2011年和2015年的艾滋病毒发病率分别为0.32%（置信区间：0.28%，0.36%）、0.36%（置信区间：0.33%，0.39%）和0.28%（置信区间：0.26%，0.30%）。法林顿模型在这三个时间段的艾滋病毒发病率分别为0.21%（置信区间：0.16%，0.26%）、0.22%（置信区间：0.20%，0.25%）和0.19%（置信区间：0.16%，0.22%）。根据这些结果，催化模型估计的艾滋病毒发病率高于法林顿模型。与队列估计相比，这些估计值在观察到的95%置信区间内，催化模型和法林顿模型的一致性分别为88%和75%。布兰德 - 奥特曼图中观察到的一致性界限在所有图中都很窄，这表明我们的模型估计与队列估计具有可比性。与联合国艾滋病规划署的估计相比，催化模型预测在所有调查年份男性的艾滋病毒发病率呈逐步上升趋势。毫无疑问，所有模型的艾滋病毒发病率在随后的每个调查年份都有所下降。为了改进国家艾滋病毒应对中的规划和政策决策，我们建议采用多种方法进行发病率估计和结果解读的三角测量法。应考虑多种估计方法以减少不同模型估计中的不确定性。
