Jansson James, Kerr Cliff C, Mallitt Kylie-Ann, Wu Jianyun, Gray Richard T, Wilson David P
aKirby Institute, UNSW Australia bComplex Systems Group, School of Physics, University of Sydney, Sydney, New South Wales, Australia.
AIDS. 2015 Jul 31;29(12):1517-25. doi: 10.1097/QAD.0000000000000679.
In some countries, HIV surveillance is based on case-reporting of newly diagnosed infections. We present a new back-projection method for estimating HIV-incidence trends using individuals' CD4 cell counts at diagnosis.
On the basis of a review of CD4 cell count distributions among HIV-uninfected people, CD4 cell count following primary infection, and rates of CD4 cell count decline over time among people with HIV, we simulate the expected distribution in time between infection and diagnosis. Applying this to all diagnosed individuals provides a distribution of likely infection times and estimates for population incidence, level of undiagnosed HIV, and the average time from infection to diagnosis each year. We applied this method to the national HIV case surveillance data of Australia for 1983-2013.
The estimated number of new HIV infections in Australia in 2013 was 912 (95% uncertainty bound 835-1002). We estimate that 2280 (95% uncertainty bound 1900-2830) people were living with undiagnosed HIV at the end of 2013, corresponding to approximately 9.4% (95% uncertainty bound 7.8-10.1%) of all people living with HIV. With increases in the average CD4 count at diagnosis, the inferred HIV testing rate has been increasing over time and the estimated mean and median times between infection and diagnosis have decreased substantially. However, the estimated mean time between infection and diagnosis is considerably greater than the median, indicating that some people remain undiagnosed for long periods. Differences were found between cases attributable to male homosexual exposure versus other cases.
This methodology provides a novel way of estimating population incidence by combining diagnosis dates and CD4 cell counts at diagnosis.
在一些国家，艾滋病毒监测基于新诊断感染病例的报告。我们提出一种新的反向推算方法，利用个体诊断时的CD4细胞计数来估计艾滋病毒感染率趋势。
基于对未感染艾滋病毒人群的CD4细胞计数分布、初次感染后的CD4细胞计数以及艾滋病毒感染者CD4细胞计数随时间下降率的回顾，我们模拟了感染与诊断之间的预期时间分布。将此应用于所有诊断个体，可得出可能的感染时间分布，并估计人群感染率、未诊断艾滋病毒的水平以及每年从感染到诊断的平均时间。我们将该方法应用于澳大利亚1983 - 2013年的全国艾滋病毒病例监测数据。
2013年澳大利亚新感染艾滋病毒的估计人数为912例（95%不确定区间835 - 1002）。我们估计，到2013年底有2280例（95%不确定区间1900 - 2830）艾滋病毒感染者未被诊断，约占所有艾滋病毒感染者的9.4%（95%不确定区间7.8 - 10.1%）。随着诊断时平均CD4细胞计数的增加，推断的艾滋病毒检测率随时间上升，感染与诊断之间的估计平均和中位数时间大幅下降。然而，感染与诊断之间的估计平均时间远大于中位数，表明一些人长期未被诊断。在男性同性恋暴露所致病例与其他病例之间发现了差异。
该方法通过结合诊断日期和诊断时的CD4细胞计数，提供了一种估计人群感染率的新方法。
