Gonsalves Gregg S, Paltiel A David, Cleary Paul D, Gill Michael J, Kitahata Mari M, Rebeiro Peter F, Silverberg Michael J, Horberg Michael, Abraham Alison G, Althoff Keri N, Moore Richard, Bosch Ronald J, Tang Tian, Hall H Irene, Kaplan Edward H
Departments of *Epidemiology of Microbial Diseases; and †Health Policy and Management, Yale School of Public Health, New Haven, CT; ‡Department of Medicine, University of Calgary, Alberta, Canada; §Center for AIDS Research, University of Washington, Seattle, WA; ‖Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN; ¶Division of Research, Kaiser Permanente Northern California, Oakland, CA; #Kaiser Permanente Mid-Atlantic Permanente Research Institute, Rockville, MD; **Department of Ophthalmology, Johns Hopkins School of Medicine, Baltimore, MD; ††Department of Epidemiology, Johns Hopkins University, Baltimore, MD; ‡‡Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD; §§Center for Biostatistics in AIDS Research, Harvard School of Public Health, Boston, MA; ‖‖ICF International, Atlanta, GA; ¶¶Division of HIV/AIDS Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, GA; and ##Yale School of Management, Yale School of Public Health, Yale School of Engineering and Applied Science, Yale University, New Haven, CT.
J Acquir Immune Defic Syndr. 2017 Aug 15;75(5):548-553. doi: 10.1097/QAI.0000000000001429.
Understanding the flow of patients through the continuum of HIV care is critical to determine how best to intervene so that the proportion of HIV-infected persons who are on antiretroviral treatment and virally suppressed is as large as possible.
Using immunological and virological data from the Centers for Disease Control and Prevention and the North American AIDS Cohort Collaboration on Research and Design from 2009 to 2012, we estimated the distribution of time spent in and dropout probability from each stage in the continuum of HIV care. We used these estimates to develop a queueing model for the expected number of patients found in each stage of the cascade.
HIV-infected individuals spend an average of about 3.1 months after HIV diagnosis before being linked to care, or dropping out of that stage of the continuum with a probability of 8%. Those who link to care wait an additional 3.7 months on average before getting their second set of laboratory results (indicating engagement in care) or dropping out of care with probability of almost 6%. Those engaged in care spent an average of almost 1 year before achieving viral suppression on antiretroviral therapy or dropping out with average probability 13%. For patients who achieved viral suppression, the average time suppressed on antiretroviral therapy was an average of 4.5 years.
Interventions should be targeted to more rapidly identifying newly infected individuals, and increasing the fraction of those engaged in care that achieves viral suppression.
了解患者在艾滋病病毒（HIV）连续护理过程中的流向对于确定如何进行最佳干预至关重要，以便使接受抗逆转录病毒治疗且病毒得到抑制的HIV感染者比例尽可能高。
利用疾病控制与预防中心以及北美艾滋病队列研究与设计合作组织2009年至2012年的免疫学和病毒学数据，我们估计了HIV连续护理各阶段所花费的时间分布以及退出概率。我们使用这些估计值来建立一个排队模型，以计算级联各阶段中预期的患者数量。
HIV感染者在确诊后平均约3.1个月才与护理机构建立联系，或有8%的概率退出该连续护理阶段。那些与护理机构建立联系的人平均还要再等3.7个月才能获得第二组实验室检查结果（表明已参与护理），或有近6%的概率退出护理。参与护理的人在通过抗逆转录病毒疗法实现病毒抑制或平均有13%的概率退出护理之前，平均要花费近1年时间。对于实现病毒抑制的患者，抗逆转录病毒疗法抑制病毒的平均时间为4.5年。
干预措施应旨在更迅速地识别新感染个体，并提高参与护理并实现病毒抑制的患者比例。
