HIV infection, Antiretroviral therapy, and CD4+ cell count distributions in African populations
HIV infection, Antiretroviral therapy, and CD4+ cell count distributions in African populations
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DOI：
10.1086/508206
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发表时间：
2006-11-15
影响因子：
6.4
通讯作者：
Dye, Christopher
中科院分区：
文献类型：
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作者：
Williams, Brian G.;Korenromp, Eline L.;Dye, Christopher
背景。人类免疫缺陷病毒 (HIV) 阳性和阴性非洲人群内部和之间 CD4(+) 细胞计数的变异性尚未得到解释，但对于了解个体和人群中 HIV 相关机会性感染（尤其是结核病）的发病率具有重要意义。在 HIV 阴性的非洲成年人中，CD4(+) 细胞计数在人群内部（四分位数范围 [IQR]，169-603 个细胞/μL）和人群之间（平均值从 699 到 1244 个细胞/μL 不等）存在差异，HIV 阳性成人中的 CD4(+) 细胞计数也存在同样的巨大差异。我们开发了动态数学模型，利用 HIV 阴性成人的分布来预测 HIV 阳性成人中 CD4(+) 细胞计数的分布。结果。假设存活率与血清转化前的 CD4(+) 细胞计数无关，我们拟合了 HIV 阳性成人中观察到的分布。当 CD4(+) 细胞计数为 200 个细胞/μL 时，HIV 阳性赞比亚人的中位预期寿命（4.0 年）预计是 HIV 阳性南非人（2.3 年）的 1.7 倍。结论。该模型提供了一种方法来估计 CD4(+) 细胞计数分布的变化，从而估计随着流行病的成熟，与 HIV 相关的机会性感染的发生率的变化。这可以大大改善卫生服务的规划，包括抗逆转录病毒治疗的需要和需求。需要更好的数据来更严格地测试模型及其假设，并充分了解群体内部和群体之间 CD4(+) 细胞计数的变异性。
Background. The variability in CD4(+) cell counts within and among human immunodeficiency virus (HIV) positive and -negative African populations has not been explained but has important implications for understanding the incidence of HIV-related opportunistic infections, especially tuberculosis, in both individuals and populations.Methods. In HIV-negative African adults, CD4(+) cell counts vary within populations (interquartile ranges [IQRs], 169-603 cells/mu L) and among populations (means vary from 699 to 1244 cells/mu L), with similarly wide variations in HIV-positive adults. We developed dynamic mathematical models to predict the distribution of CD4(+) cell counts in HIV-positive adults using the distribution in HIV-negative adults.Results. Under the assumption that survival is independent of the CD4(+) cell count before seroconversion, we fitted the observed distributions in HIV-positive adults. At a CD4(+) cell count of 200 cells/mu L, the median life expectancy of HIV-positive Zambians (4.0 years) was predicted to be 1.7 times that of HIV-positive South Africans (2.3 years).Conclusions. The model provides a way to estimate the changing distribution of CD4(+) cell counts and, hence, the changing incidence of HIV-related opportunistic infections as the epidemic matures. This could substantially improve the planning of health services, including the need and demand for antiretroviral therapy. Better data are needed to test the model and its assumptions more rigorously and to fully understand the variability in CD4(+) cell counts within and among populations.