Remis Robert S, Palmer Robert W H
Ontario HIV Epidemiologic Monitoring Unit, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.
AIDS. 2009 Feb 20;23(4):493-503. doi: 10.1097/QAD.0b013e328323ad5f.
Incidence is critical in monitoring HIV infection in populations but often difficult to measure. The Serologic Testing Algorithm for Recent HIV Seroconversion (STARHS) can estimate HIV incidence from a single specimen at low cost. Nevertheless, HIV testing patterns may introduce bias, rendering interpretation of the STARHS result problematic. We found empirical evidence of such bias in Ontario using the STARHS formula with varied window periods
In a hypothetical population of homosexual men, we calculated HIV incidence from the STARHS assay on the basis of incidence density, study duration, STARHS window period and intertest interval. We also incorporated the increased likelihood of a newly infected person having an HIV test due to seroconversion illness or high-risk behaviours ('seroconversion effect' or SCE). We also varied the intertest interval inversely as a function of incidence density. To adjust incidence estimates for bias, we fit empirical STARHS data to an algebraic formula expressing measured HIV incidence as a function of SCE and incidence.
Incidence density estimates were unbiased when SCE or incidence density-interval interactions were absent. However, estimated incidence density was higher than true incidence density in the presence of SCE, as much as seven-fold higher under certain conditions. The goodness-of-fit provided estimates with an excellent fit, yielding plausible results.
HIV incidence from STARHS may be strongly biased because of early testing in recently infected persons, resulting in substantial overestimation, at least amongst men who have sex with men. Thus, incidence estimates from STARHS must be interpreted with considerable caution. Nevertheless, incidence estimates may be adjusted to yield unbiased results.
发病率对于监测人群中的艾滋病毒感染至关重要，但往往难以测量。近期艾滋病毒血清转化的血清学检测算法（STARHS）能够以低成本从单一标本中估算艾滋病毒发病率。然而，艾滋病毒检测模式可能会引入偏差，使STARHS结果的解读存在问题。我们在安大略省发现了使用不同窗口期的STARHS公式存在此类偏差的经验证据。
在一个假设的男同性恋人群中，我们根据发病率密度、研究持续时间、STARHS窗口期和检测间隔，通过STARHS检测计算艾滋病毒发病率。我们还纳入了新感染者因血清转化疾病或高危行为而进行艾滋病毒检测的可能性增加的情况（“血清转化效应”或SCE）。我们还将检测间隔作为发病率密度的函数进行反比变化。为了调整发病率估计值以消除偏差，我们将STARHS的经验数据拟合到一个代数公式中，该公式将测得的艾滋病毒发病率表示为SCE和发病率的函数。
当不存在SCE或发病率密度-间隔相互作用时，发病率密度估计值无偏差。然而，在存在SCE的情况下，估计的发病率密度高于真实发病率密度，在某些条件下高出多达七倍。拟合优度提供了极佳的拟合估计值，得出了合理的结果。
由于近期感染者的早期检测，STARHS得出的艾滋病毒发病率可能存在强烈偏差，导致大幅高估，至少在男男性行为者中如此。因此，对STARHS得出的发病率估计值必须极为谨慎地进行解读。尽管如此，发病率估计值可进行调整以得出无偏差的结果。
