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/microL) and among populations (means vary from 699 to 1244 cells/microL), 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/microL, 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.
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/microL) and among populations (means vary from 699 to 1244 cells/microL), 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/microL, 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.
Authors: Brian G Williams; Reuben Granich; Kevin M De Cock; Philippe Glaziou; Abhishek Sharma; Christopher Dye Journal: Proc Natl Acad Sci U S A Date: 2010-10-25 Impact factor: 11.205
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Authors: S D Lawn; A D Harries; B G Williams; R E Chaisson; E Losina; K M De Cock; R Wood Journal: Int J Tuberc Lung Dis Date: 2011-05 Impact factor: 2.373
Authors: Jernej Pušnik; Michael A Eller; Boonrat Tassaneetrithep; Bruce T Schultz; Leigh Anne Eller; Sorachai Nitayaphan; Josphat Kosgei; Lucas Maganga; Hannah Kibuuka; Galit Alter; Nelson L Michael; Merlin L Robb; Hendrik Streeck Journal: J Virol Date: 2019-06-28 Impact factor: 5.103
Authors: C Mair; S E Hawes; H D Agne; P S Sow; I N'doye; L E Manhart; P L Fu; G S Gottlieb; N B Kiviat Journal: Clin Exp Immunol Date: 2008-01-10 Impact factor: 4.330