Men who have sex with men (MSM) are one of the highest risk groups for HIV infection in China. Although official figures estimate that 2%–4% of the Chinese adult male population are MSM, they account for 32.5% of about 48,000 new HIV cases in 2009.1–3 Relatively few data on the natural history of HIV/AIDS exist outside Europe, North America, and Australia and very little from China.4–6 Better understanding of the course of disease among HIV-positive MSM in China is, therefore, urgently required to provide appropriate medical care for this population, limit HIV transmission to uninfected individuals, and plan for healthcare resources.
It is widely recognized that CD4+ T-cell count and HIV-RNA are important predictors for disease progression and prognosis among HIV-infected individuals7,8 and of treatment response.9,10 Variations in CD4+ T-cell decline and HIV-RNA have been observed between different HIV subtypes and by ethnicity.11–14 These studies have, however, mostly focused on HIV-infected individuals in resource-rich settings; so, the trends identified in these populations may not be generalizable to trends in the HIV-positive Chinese population and MSM in China.15,16
We aimed to estimate the rate of CD4+ T-cell decline and HIV-RNA changes among HIV-infected MSM in China before antiretroviral therapy (ART) initiation and compare these with rate of CD4+ T cell decline and HIV-RNA changes experienced by MSM from resource-rich country settings. The results may provide the foundation to revise HIV treatment and management guidelines for individuals infected with HIV in China.
This study includes 2 groups of MSM seroconverters: (1) the Beijing PRIMO cohort study of primary HIV-1-infected individuals (Wu et al, unpublished data) and (2) individuals enrolled into seroconverter cohorts in Concerted Action on SeroConversion to AIDS and Death in Europe (CASCADE) Table 1.
Recruitment for the Beijing PRIMO cohort started in October 2006 in the HIV care clinic at You’an hospital, Beijing. It is an ongoing, open, cohort of HIV-1 seronegative MSM who are provided with risk-education counseling before being tested for HIV antibody and HIV-1-RNA every 2 months. As of December 2009, among 4162 men recruited in the study, 135 were diagnosed with primary HIV infection (PHI) (12 with an indeterminate Western blot, 107 with a negative test followed by a positive one, and 16 ELISA negative whereas HIV-RNA positive). After seroconversion, clinical and laboratory measurements are taken at weeks 1, 2, 4, 8, and 12, and then every 3 months.
CASCADE is a collaboration of 28 HIV seroconverter cohort studies in Europe, Australia, Canada, and sub-Saharan Africa, enrolling seroconverters from the 1980s onward. Details of the CASCADE collaboration appear elsewhere.17 Briefly, all cohorts include individuals infected with HIV-1 for whom date of seroconversion can be reliably estimated, most commonly as the midpoint between the last negative and the first positive test, with a time between tests <3 years. Data used for the current analyses were pooled in 2009 within EuroCoord (www.EuroCoord.net).
As the objective was to compare CD4+ T-cell count and HIV-RNA changes during ART naive follow-up in MSM from China and from resource-rich settings, seroconverters from the 2 sub-Saharan African cohorts in CASCADE were excluded. Because of the shorter length of follow-up in the Beijing PRIMO cohort, we only considered follow-up in the first 3 years following HIV seroconversion in CASCADE.
For the 2 cohorts, follow-up started at the estimated date of seroconversion and ended at the earliest of: ART initiation, death, 3 years from seroconversion (for CASCADE), or last date the participant was known to be alive. Because of the large variability in CD4+ T-cell count measurements in PHI, we excluded CD4+ T-cell count within the first 4 weeks of the estimated date of HIV seroconversion in both cohorts.
CD4+ T cells measured during follow-up from the 2 groups were merged and analyzed together. Maximum likelihood linear mixed models were used to compare the rate of CD4+ T-cell count decline on the cubic root in the 2 groups. The cubic transformation was chosen as it seemed to normalize the marker distribution and linearize changes over time in both groups. Departures from the assumption of linearity were assessed using fractional polynomials.18 The time origin was defined as the estimated date of seroconversion. The correlation between individual initial values and subsequent individual slope was handled through the unstructured covariance matrix of random effects. To test for the difference in rate of decline in the 2 cohorts, we included and tested for an interaction term between cohort and slope. To account for the difference in the CD4+ T-cell count distribution at seroconversion in the 2 groups, we also compared the rate of decline conditioning on the CD4+ T-cell count at seroconversion.19 We also adjusted for age at seroconversion in categories roughly based on the quartiles of the overall age distribution (<28, 28–32, 33–39, and 40+ years).
Results are presented as the rate of CD4+ T-cell decline on the cube root scale, where an increased magnitude in absolute value indicates faster rate of decline. Moreover, results are also presented as the loss in back-transformed CD4+ T-cell count in the first 2 years after the estimated date of HIV seroconversion, with confidence intervals (CIs) of CD4+ T-cell count loss computed using the nonparametric bootstrap. In a sensitivity analysis to explore the role of HIV subtype, we compared the rate of CD4+ T-cell decline between the 2 cohorts restricting to participants with B subtype. In a further sensitivity analysis, we included CD4+ T-cell counts within 4 weeks of seroconversion.
To compare HIV-RNA dynamics, HIV-RNA measured during follow-up from the 2 groups were merged and analyzed together. A piecewise linear mixed model was applied to plasma HIV-RNA data on a log10 scale, with the change point at 12 months after seroconversion. The choice of the change point is based on initial exploratory data analysis and on nonlinear models.
The study included 131 and 3171 men from the Beijing PRIMO and CASCADE groups, respectively.
Compared with CASCADE, men in Beijing PRIMO were younger at seroconversion (median age: 29 vs 34 years), and their first CD4 + T-cell count and HIV-RNA measurements were available closer to seroconversion. Median interquartile ratio (IQR) initial CD4+ T-cell count were 475 (345–596) and 495 (361–654) cells/mm3 and initial log10 HIV-RNA was 4.47 (3.87–5.40) and 4.81 (4.14–5.36) copies/mL in the Beijing PRIMO and CASCADE group, respectively. During follow-up, 6 (5%) and 965 (30%) men started ART among Beijing PRIMO and CASCADE, respectively. The HIV subtype was known for 119 (91%) Beijing PRIMO MSM, of whom more than half were infected with AE subtype and 37 with B subtype; 1109 (35%) CASCADE MSM were infected with subtype B.
CD4 Count Decline in the Absence of ART
Despite marginal benefit of the fractional polynomial models, which were able to describe more carefully the dynamics close to the time origin, the linear model on the cubic root of the CD4+ T-cell count presented an adequate fit to the data. Therefore, for the sake of simplicity, results from the linear models are presented hereafter.
The CD4+ T-cell count at seroconversion estimated from the model for the Beijing PRIMO group was 504 cells/mm3 [95% CI: 463 to 547], lower than the 554 (95% CI: 544 to 564) cells/mm3 estimated for CASCADE. The estimated trajectories of the back-transformed CD4+ T-cell count in the 2 groups are presented in Fig. 1. In the unadjusted model, the CD4+ T-cell count decline was significantly faster in the Beijing PRIMO group compared with the CASCADE group (P = 0.006). Rates of decline for Beijing PRIMO and CASCADE were estimated to be − 0.59 (95% CI: − 0.72 to − 0.47) and − 0.41 (95% CI: − 0.44 to − 0.38) cubic root of CD4 cells/mm3 per year, respectively, in the unadjusted model, and − 0.55 (95% CI: − 0.68 to − 0.42) and − 0.36 (95% CI: − 0.42 to −0.31) cubic root of CD4 cells/mm3 per year in the model adjusted for age at HIV seroconversion. These rates were equivalent to an estimated average CD4+ T-cell count loss in the first 2 years after HIV seroconversion of 194 and 149 cells/mm3, respectively. Differences in rate of decline persisted after conditioning on the estimated CD4+ T-cell count at HIV seroconversion and the inclusion of counts in the first 4 weeks (data not shown).
When we restricted our analyses to the 37 Beijing and 1109 CASCADE participants infected with B subtype, differences in rate of decline remained significantly faster for Beijing PRIMO (P = 0.01) with rates on the cubic root scale of −0.74 (−0.96 to −0.52) and −0.46 (−0.51 to −0.40) for Beijing PRIMO and CASCADE, respectively.
Changes in HIV-RNA in the Absence of ART
Estimated HIV-RNA levels were significantly higher for CASCADE compared with Beijing PRIMO, corresponding to a difference of 0.425 log10/mL (95% CI: 0.249 to 0.603; P < 0.001) (Fig. 2). During the first year following HIV seroconversion, CASCADE MSM tended to experience faster HIV-RNA decline (mean change: −0.394; IQR: −0.451 to −0.337 log10/mL) compared with Beijing PRIMO (−0.112; IQR: −0.327 to 0.102 log10/mL). After the first year, however, Beijing PRIMO participants experienced a faster increase in HIV-RNA (0.830; IQR: 0.484–1.168) log10/mL per year compared with CASCADE group (0.018; IQR: −0.035–0.067 log10/mL) (P < 0.001).
Our analyses have highlighted significant differences in CD4+ T-cell count and HIV-RNA values at the time of HIV seroconversion and in the first 3 years following it between Chinese and CASCADE MSM populations. On average, Beijing PRIMO cohort participants had lower CD4+ T-cell count (504 vs 554 cells/mm3) and lower HIV-RNA (4.5 vs 4.8 log10 copies/mL) at HIV seroconversion.
Beijing PRIMO cohort participants also experienced a more rapid CD4+ T-cell count loss (194 vs 149 cells/mm3) in the first 2 years, possibly explained by the observed more rapid increase in plasma HIV viremia over the same time span. This is the first study to highlight these differences and suggests that HIV disease progression in Chinese MSM differs from that experienced by MSM in resource-rich settings.
The lower CD4+ T-cell count at seroconversion observed among Chinese MSM may be a consequence of lower CD4+ T-cell count in HIV-negative Chinese individuals compared with Western populations. Unfortunately, we lack data on CD4+ T-cell count before seroconversion in these cohorts and, so, were unable to examine this further.20–22 However, the median (IQR) CD4+ T-cell count of 796 (597–966) cells/mm3 in a group of 130 HIV-negative MSM in China was similar to those seen in western populations, which suggests that our observed difference among HIV-positives populations is not determined by background differences.12,23 A number of studies have examined CD4+ T-cell count declines pre-ART, but the mechanisms that underlie the decline remain incompletely understood.24,25
Our finding that CD4+ T-cell loss in MSM in China may be faster than in high-income countries warrants additional investigation. Although the differences we observed in the rate of decline are not explained by differences in distribution of CD4+ T-cell count at HIV seroconversion or by age at seroconversion, the more rapid HIV disease progression that we identified in the Beijing PRIMO cohort may be due to early virological and host events during primary HIV infection. For example, there could be differences in HLA type compacting on the immune responsiveness of Chinese and individuals from resource-rich settings and in HIV subtype.11,23 In addition, investigators who have explored differences in immune response by ethnicity have suggested that these may be associated with different coinfection rates, for example, tuberculosis, hepatitis B and C, herpes, or syphilis.5 In addition, differences in lifestyle including nutritional status, alcohol consumption, and drug use might have played a role in the observed differences in HIV disease progression between the 2 populations. Unfortunately, these factors were not collected in either cohort.
There is some evidence that HIV subtype could be associated with the rate of disease progression.5,11 Given the limited data on HIV subtype in CASCADE (1974, 62% with unknown subtype), it was not possible to examine this fully. However, for those infected with subtype B, the rate of decline was faster in Beijing PRIMO men, suggesting that subtype is unlikely to explain the difference detected.
Beijing PRIMO men had shorter interval between their last negative and first positive HIV antibody test dates; 78% were tested every 2 months. HIV participants with a short test interval have been shown to be more likely to have symptomatic disease, hence the reason for presenting at that stage, and worse prognosis.26 Nevertheless, this is unlikely to be the reason for our findings given that Chinese participants in the Beijing cohort were regularly tested and testing HIV positive was not, therefore, dependent on presentation and symptoms.
We recognize that inherent unmeasured differences between the 2 studies may exist, which we were not able to adequately control for in the analysis and which may have introduced bias to our estimates. Furthermore, the follow-up time was relatively short (3 years), and we were, therefore, unable to assess differences in CD4+ T-cell count decline over a longer period or the effect of these differences on long-term outcome. Nevertheless, using the Beijing PRIMO cohort, the only source of data on MSM seroconverters in China, we have detected significant differences in CD4+ T-cell count and HIV-1-RNA in this population compared with those in resource-rich countries. Our finding of more rapid disease progression following HIV seroconversion in MSM enrolled in the Beijing PRIMO cohort calls for further investigations to explore whether this is because of differences in host immunity or viral characteristics and the impact on outcome.
Beijing PRIMO cohort
PHI Cohort follow-up and data collection: Xiaojie Huang. Screening for HIV infection test: Zhiying Liu, Feili Wei, Yunxia Ji. Data collection for HIV-1 negative MSM individuals cohort: Tong Zhang, Yan Fu, Wei Xia. Sample collection and storage: Huiping Yan, Xin Zhang, Weihua Li. Pooling HIV-1-RNA test: Yan Jiang, Pin Liang Pan.
CASCADE Steering Committee
Julia Del Amo (Chair), Laurence Meyer (Vice Chair), Heiner C. Bucher, Geneviève Chêne, Osamah Hamouda, Deenan Pillay, Maria Prins, Magda Rosinska, Caroline Sabin, Giota Touloumi.
CASCADE Co-ordinating Center
Kholoud Porter (Project Leader), Ashley Olson, Kate Coughlin, Sarah Walker, Abdel Babiker.
CASCADE Clinical Advisory Board
Heiner C. Bucher, Andrea De Luca, Martin Fisher, Roberto Muga.
Australia: PHAEDRA cohort (Tony Kelleher, David Cooper, Pat Grey, Robert Finlayson, Mark Bloch) Sydney AIDS Prospective Study and Sydney Primary HIV Infection cohort (Tony Kelleher, Tim Ramacciotti, Linda Gelgor, David Cooper, Don Smith); Austria: Austrian HIV Cohort Study (Robert Zangerle); Canada: South Alberta clinic (John Gill); Estonia: Tartu Ülikool (Irja Lutsar); France: ANRS CO3 Aquitaine cohort (Geneviève Chêne, Francois Dabis, Rodolphe Thiebaut, Bernard Masquelier), ANRS CO4 French Hospital Database (Dominique Costagliola, Marguerite Guiguet), Lyon Primary Infection cohort (Philippe Vanhems), French ANRS CO6 PRIMO cohort (Marie-Laure Chaix, Jade Ghosn), ANRS CO2 SEROCO cohort (Laurence Meyer, Faroudy Boufassa); Germany: German HIV-1 seroconverter cohort (Osamah Hamouda, Claudia Kücherer, Barbara Bartmeyer); Greece: AMACS (Anastasia Antoniadou, Georgios Chrysos, Georgios L. Daikos); Greek Haemophilia cohort (Giota Touloumi, Nikos Pantazis, Olga Katsarou); Italy: Italian Seroconversion Study (Giovanni Rezza, Maria Dorrucci), ICONA cohort (Antonella d'Arminio Monforte, Andrea De Luca.) Netherlands: Amsterdam Cohort Studies among homosexual men and drug users (Maria Prins, Ronald Geskus, Jannie van der Helm, Hanneke Schuitemaker); Norway: Oslo and Ulleval Hospital cohorts (Mette Sannes, Oddbjorn Brubakk, Anne-Marte Bakken Kran); Poland: National Institute of Hygiene (Magdalena Rosinska); Spain: Badalona IDU hospital cohort (Roberto Muga, Jordi Tor), Barcelona IDU Cohort (Patricia Garcia de Olalla, Joan Cayla), CoRIS-scv (Julia del Amo, Santiago Moreno, Susana Monge); Madrid cohort (Julia Del Amo, Jorge del Romero), Valencia IDU cohort (Santiago Pérez-Hoyos); Switzerland: Swiss HIV Cohort Study (Heiner C. Bucher, Martin Rickenbach, Patrick Francioli); Ukraine: Perinatal Prevention of AIDS Initiative (Ruslan Malyuta); United Kingdom: Health Protection Agency (Gary Murphy), Royal Free haemophilia cohort (Caroline Sabin), UK Register of HIV Seroconverters (Kholoud Porter, Anne Johnson, Andrew Phillips, Abdel Babiker), University College London (Deenan Pillay). African cohorts: Genital Shedding Study (US: Charles Morrison; Family Health International, Robert Salata, Case Western Reserve University, Uganda: Roy Mugerwa, Makerere University, Zimbabwe: Tsungai Chipato, University of Zimbabwe); International AIDS Vaccine Initiative (IAVI) Early Infections Cohort (Kenya, Rwanda, South Africa, Uganda, Zambia: Pauli N. Amornkul, IAVI, USA; Jill Gilmour, IAVI, UK; Anatoli Kamali, Uganda Virus Research Institute/Medical Research Council Uganda; Etienne Karita, Projet San Francisco, Rwanda).
EuroCoord Executive Board
Heiner Bucher, Basel Institute for Clinical Epidemiology & Biostatistics University Hospital Basel, Switzerland; Fiona Burns, University College London, UK; Geneviève Chêne, University of Bordeaux II, France; Dominique Costagliola, Institut National de la Santé et de la Recherche Médicale, France; Carlo Giaquinto, Fondazione PENTA, Italy; Di Gibb (Scientific Coordinator), Medical Research Council, UK; Jesper Grarup, Københavns Universitet, Denmark; Ole Kirk, Københavns Universitet, Denmark; Jesper Kjaer, Københavns Universitet, Denmark; Laurence Meyer, Institut National de la Santé et de la Recherche Médicale, France; Alex Panteleev, St. Petersburg City AIDS Center, Russian Federation; Andrew Phillips, University College London, UK, Kholoud Porter, Medical Research Council, UK; Peter Reiss, Academic Medical Center, Netherlands; Claire Thorne (Chair), University College London, UK.
EuroCoord Council of Partners
Jean-Pierre Aboulker, Institut National de la Santé et de la Recherche Médicale, France; Jan Albert, Karolinska Institute, Sweden; Silvia Asandi, Romanian Angel Appeal Foundation, Romania; Geneviève Chêne, University of Bordeaux II, France; Dominique Costagliola, INSERM, France; Antonella d'Arminio Monforte, ICoNA Foundation, Italy; Stéphane De Wit, St. Pierre University Hospital, Belgium; Frank De Wolf, Stichting HIV Monitoring, Netherlands; Julia Del Amo, Instituto de Salud Carlos III, Spain; José Gatell, Fundació Privada Clínic per a la Recerca Bíomèdica, Spain; Carlo Giaquinto, Fondazione PENTA, Italy; Osamah Hamouda, Robert Koch Institut, Germany; Igor Karpov, University of Minsk, Belarus; Bruno Ledergerber, University of Zurich, Switzerland; Jens Lundgren, Københavns Universitet, Denmark; Ruslan Malyuta, Perinatal Prevention of AIDS Initiative, Ukraine; Claus Møller, Cadpeople A/S, Denmark; Andrew Phillips, University College London, UK; Kholoud Porter, Medical Research Council, United Kingdom; Maria Prins, Academic Medical Center, Netherlands; Aza Rakhmanova, St. Petersburg City AIDS Center, Russian Federation; Jürgen Rockstroh (Chair), University of Bonn, Germany; Magda Rosinska, National Institute of Public Health, National Institute of Hygiene, Poland; Claire Thorne, University College London, UK; Giota Touloumi, National and Kapodistrian University of Athens, Greece; Alain Volny Anne, European AIDS Treatment Group, France.
EuroCoord External Advisory Board
David Cooper, University of New South Wales, Australia; Nikos Dedes, Positive Voice, Greece; Kevin Fenton, Centers for Disease Control and Prevention, USA; David Pizzuti, Gilead Sciences, USA; Marco Vitoria, World Health Organisation, Switzerland.
Kate Coughlin, MRC Clinical Trials Unit, UK; Silvia Faggion, Fondazione PENTA, Italy; Lorraine Fradette, MRC Clinical Trials Unit; Richard Frost, MRC Regional Center London, UK; Miriam Sabin, Københavns Universitet, Denmark; Christine Schwimmer, University of Bordeaux II, France; Martin Scott, UCL European Research & Development Office, UK.
1. Liu H, Yang H, Li X, et al.. Men who have sex with men and human immunodeficiency virus/sexually transmitted disease control in China
. Sex Transm Dis. 2006;33:68–76.
2. Zhang B, Li X, Shi T. A primary estimation of the number of population and HIV
prevalence in homosexual and bisexual men in China
. Chin J AIDS/STD Prev Control. 2002;8:197–199 [in Chinese].
4. Zhang M, Shang H, Wang Z, et al.. Natural history of HIV
infection in former plasma donors in rural China
. Front Med China
. 2010;4:346–350 [in Chinese].
5. Qu W, Robinson M, Zhang FJ. Factors influencing the natural history of HIV
-1 infection. Chin Med J (Engl). 2008;121:2613–2621.
6. Zheng X, Zhang J, Wang X, et al.. The natural history of HIV
infection among IDUs in Ruili, Yunnan province, China
. Zhonghua Liu Xing Bing Xue Za Zhi. 2000;21:17–18 [in Chinese].
7. Mellors JW, Muñoz A, Giorgi JV, et al.. Plasma viral load and CD4+ lymphocytes as prognostic markers of HIV
-1 infection. Ann Intern Med. 1997;126:946–954.
8. Phillips A, Pezzotti P. CASCADE Collaboration. Short-term risk of AIDS according to current CD4 cell count and viral load in antiretroviral drug-naive individuals and those treated in the monotherapy era. AIDS. 2004;18:51–58.
9. Phillips AN, Staszewski S, Weber R, et al.. HIV
viral load response to antiretroviral therapy according to the baseline CD4 cell count and viral load. JAMA. 2001;286:2560–2567.
10. Mussini C, Cossarizza A, Sabin C, et al.. Decline of CD4+ T-cell count before start of therapy and immunological response to treatment in antiretroviral-naive individuals. AIDS. 2011;25:1041–1049.
11. Gottlieb GS, Nickle DC, Jensen MA, et al.. Dual HIV
-1 infection associated with rapid disease progression. Lancet. 2004;363:619–622.
12. Anglaret X, Diagbouga S, Mortier E, et al.. CD4+ T-lymphocyte counts in HIV
infection: are European standards applicable to African participants? J Acquir Immune Defic Syndr Hum Retrovirol. 1997;14:361–367.
13. Achhra AC, Zhou J, Dabis F, et al.. Difference in absolute CD4+ count according to CD4 percentage between Asian and white population HIV
-infected participants. J AIDS Clin Res. 2010;1:1–4.
14. Bofill M, Janossy G, Lee CA, et al.. Laboratory control values for CD4 and CD8 T lymphocytes. Implications for HIV
-1 diagnosis. Clin Exp Immunol. 1992;88:243–252.
15. Lebranchu Y, Thibault G, Degenne D, et al.. Abnormalities in CD4+ T lymphocyte subsets in participants with common variable immunodeficiency. Clin Immunol Immunopathol. 1991;61:83–92.
16. Korenromp EL, Williams BG, Schmid GP, et al.. Clinical prognostic value of RNA viral load and CD4 cell counts during untreated HIV
-1 infection—a quantitative review. PLoS One. 2009;17;4:e5950.
17. CASCADE collaboration. Changes in the uptake of antiretroviral therapy and survival in people with known duration of HIV
infection in Europe: results from CASCADE. HIV
18. Royston P, Sauerbrei W. The use of fractional polynomials to model continuous risk variables in epidemiology. Int J Epidemiol. 1999;28:964–974.
19. Harrison L, Dunn DT, Green H, et al.. Modelling the association between participant characteristics and the change over time in a disease measure using observational cohort data. Stat Med. 2009;28:3260–3275.
20. Kam KM, Wong KH, Li PC, et al.. Proposed CD4(+) T-cell criteria for staging human immunodeficiency virus-infected Chinese adults. Clin Immunol Immunopathol. 1998;89:11–22.
21. Wong KH, Chan KC, Cheng KL, et al.. Establishing CD4 thresholds for highly active antiretroviral therapy initiation in a cohort of HIV
-infected adult Chinese in Hong Kong. AIDS Patient Care STDS. 2007;21:106–115.
22. Kam KM, Leung WL, Kwok MY, et al.. Lymphocyte subpopulation reference ranges for monitoring human immunodeficiency virus infected Chinese adults. Clin Diagn Lab Immunol. 1996;3:326–330.
23. Chang WJ, Tan GB, Kuperan P. Establishment of adult peripheral blood lymphocyte subset reference range for an Asian population by single-platform flow cytometry: influence of age, sex, and race and comparison with other published studies. Clin Diagn Lab Immunol. 2004;11:168–173.
24. Moore RD, Keruly JC. CD4 cell count 6 years after commencement of highly active antiretroviral therapy in persons with sustained virologic suppression. Clin Infect Dis. 2007;44:441–446.
25. Waters L, Mandalia S, Randell P, et al.. The impact of HIV
tropism on decreases in CD4 cell count, clinical progression, and subsequent response to a first antiretroviral therapy regimen. Clin Infect Dis. 2008;46:1617–1623.
26. CASCADE Collaboration. The relationships between the HIV
test interval, demographic factors and HIV
disease progression. Epidemiol Infect. 2001;127:91–100.