The primary goal of public health efforts to control HIV epidemics is to diagnose and treat people with HIV infection as soon as possible after seroconversion. The timing of initiation of antiretroviral therapy (ART) treatment after HIV diagnosis is, therefore, a critical population-level indicator that can be used to measure the effectiveness of public health programs and policies at local and national levels. However, population-based data on ART initiation are unavailable because ART initiation and prescription are typically measured indirectly by public health departments (e.g., with viral suppression as a proxy). In this paper, we present a random change-point model to infer the time of ART initiation utilizing routinely reported individual-level HIV viral load from an HIV surveillance system. To deal with the left-censoring and the nonlinear trajectory of viral load data, we formulate a flexible segmented nonlinear mixed effects model and propose a Stochastic version of EM (StEM) algorithm, coupled with a Gibbs sampler for the inference. We apply the method to a random subset of HIV surveillance data to infer the timing of ART initiation since diagnosis and to gain additional insights into the viral load dynamics. Simulation studies are also performed to evaluate the properties of the proposed method.
翻译:公共卫生努力控制艾滋病毒流行病的首要目标是在血清转化后尽快诊断和治疗艾滋病毒感染者。因此,在艾滋病毒诊断后开始抗逆转录病毒疗法治疗的时间是一个重要的人口指标,可用于衡量地方和国家各级公共卫生方案和政策的有效性,但是,没有基于人口的抗逆转录病毒疗法启动数据,因为抗逆转录病毒疗法的启动和处方通常由公共卫生部门间接衡量(例如,病毒抑制作为代名词)。在本文中,我们提出了一个随机变化点模型,以利用艾滋病毒监测系统报告的个体一级艾滋病毒病毒负荷来推算抗逆转录病毒疗法启动的时间。为了处理病毒负荷数据的左侧检查和非线轨迹,我们制定了一个灵活的非线性非线性混合效应模型,并提出了EM(StEM)算法的随机版本,以及一个用于推断的Gibbs抽样器。我们对艾滋病毒监测数据的一个随机组别采用这一方法,以推断自诊断以来抗逆转录病毒疗法启动的时间,并获得对病毒负荷特性的更多了解。还进行了模拟研究。