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Differences in CD4 Cell Counts at Seroconversion and Decline Among 5739 HIV-1–Infected Individuals with Well-Estimated Dates of Seroconversion

CASCADE Collaboration

JAIDS Journal of Acquired Immune Deficiency Syndromes: 1 September 2003 - Volume 34 - Issue 1 - pp 76-83
Epidemiology & Social Science

We studied repeated measurements of CD4 cell counts on 5739 HIV-1–infected individuals with reliably estimated dates of seroconversion (SC) aged ≥15 years at SC prior to initiation of highly active antiretroviral therapy (HAART) or AIDS using random effects models. Estimated CD4 cell count at SC differed significantly by sex, exposure group, and age, being higher in women, hemophilic men, and injection drug users (IDUs) as well as in those aged >40 years at SC. The rate of CD4 cell count decline did not differ significantly by sex; thus, differences between men and women were stable throughout the HIV-1 incubation period. There was a monotonic relationship between CD4 slopes and age at SC, with steeper slopes in older subjects. At 5 years after SC, the median difference in CD4 cell counts between the oldest (>40 years at SC) and youngest (16–20 years at SC) subjects was around 90 cells/μL. Mean rate of CD4 decline was significantly steeper in subjects diagnosed during acute infection. There was no evidence of a faster loss of CD4 cells in subjects who seroconverted after 1994. Apart from hemophilic men, who tended to have a steeper rate of CD4 decline on average, mean CD4 slopes did not differ by exposure category. These results suggest that before the initiation of HAART or other interventions based on immune status, consideration of demographic factors may be worthwhile.

From the CASCADE Collaboration.

Received for publication December 2, 2002; accepted July 11, 2003.

CASCADE is funded through a grant from the European Union [QLK2-2000-01431] and has received additional funding from GlaxoSmithKline.

Address correspondence to Giota Touloumi, Department of Hygiene & Epidemiology, Athens University Medical School, M Asias 75, Athens 11527, Greece (e-mail:

In HIV-1–infected individuals, CD4 cell counts and plasma HIV RNA levels are used to estimate the stage of the disease and to guide therapeutic decisions. Most of the studies on the prognostic values of these markers have been based on populations of white homosexual men, however. 1,2 Although it is well established that older age at seroconversion (SC) is associated with more rapid progression to AIDS and death, 3–8 several studies have failed to show an effect of age on CD4 cell decline. 9,10

Results from early studies suggesting differences in disease progression by sex and exposure category 3,11–14 were probably due to differences in the age at SC by exposure category. 4,15,16 Although it is likely that there is at most a minimal effect of sex and exposure category on disease progression, studies of both HIV-uninfected and HIV-infected subjects close to SC have shown that women tend to have higher CD4 cell counts than men, with a mean difference of around 100 cells/μL. 5,17–19 Little information exists regarding the effect of sex on trends of CD4 cell decline, however. 20–24 Whether differences in CD4 cell count at SC and rate of CD4 decline exist by exposure category is even less clear. 6,21,22

Further, the presence, severity, and duration of a clinically recognized SC illness is associated with more rapid progression to AIDS. 25–27 Little is known about CD4 differences at SC and subsequent CD4 decline between those presenting with and without illness indicative of primary infection, however. Because most cohort studies have enrolled HIV seroconverters retrospectively, information on symptomatic primary infection may not be available. It has been shown that in such cases, a short HIV test interval of 30 days or less between the negative and positive test dates can be used as a proxy measure for primary infection. 28,29

Another factor that may influence the rate of CD4 decline is temporal changes in HIV itself. Two studies have reported a shorter time to reaching lower CD4 cell counts in those individuals more recently seroconverting to HIV, suggesting the possibility of more virulent strains of HIV emerging. 30,31 These results were not confirmed, however, by data from other large cohort studies. 32,33

This study aims to assess the effect of age at SC, sex, exposure group, calendar year of SC, and HIV test interval as a proxy for primary infection on the CD4 cell count at the time of SC and on the rates of CD4 cell count decline during the course of HIV infection from SC to the development of clinical AIDS or death in the absence of highly active antiretroviral therapy (HAART). A methodology that accounts for selective censoring in CD4 cell count data has been applied. No previous studies have been large enough to assess such effects on CD4 trends over time while, at the same time, correcting for potential biases induced by selective censoring due to disease progression.

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CASCADE is an international collaboration established to bring together data from seroconverter cohorts in Europe and Australia. The CASCADE study has been described in detail elsewhere. 34 Briefly, the study aims to address questions on the natural history of HIV-1 infection, which cannot be adequately addressed through single studies. Data pooled in 2001 from 20 cohorts participating in the CASCADE study comprised a total of 8729 HIV seroconverters. The date of SC was that estimated by the study investigators for each participating study, with the majority being the midpoint between the HIV-negative and HIV-positive dates. For all analyses reported here, subjects were only included if their date of SC was estimated using the midpoint method or if, on the basis of laboratory evidence of SC, their HIV test interval was less than 3 years and they had at least 2 CD4 cell count measurements during follow-up. It is known that the absolute counts of all T-cell subsets decrease naturally from birth until the age of 10 to 15 years, when they reach what is normal for adult values. 35 To avoid mixing the natural loss of CD4 cell count in children with the loss due to HIV-1 infection, all subjects aged less than 15 years at SC were excluded from the analysis. Because one of the main aims of this study was to investigate differences in CD4 cell count trends by exposure category, the 119 subjects with other and unknown modes of HIV infection were excluded from the rest of the analysis.

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Statistical Analysis

Because AIDS development and the initiation of HAART are expected to affect secular trends in CD4 natural history, whereas the use of reverse transcriptase inhibitors (RTIs) is expected to have only a small and transient effect, all CD4 data collected after initial AIDS diagnosis or HAART initiation were truncated. Clinical AIDS cases were defined in accordance with category C of the 1993 revised classification system of the Centers for Disease Control and Prevention (CDC), 36 whereas HAART was defined as any protease inhibitor– or nonnucleoside analogue reverse transcriptase–based regimen. Trends in CD4 cell counts were described by fitting random-effects (RE) models. These models provide estimates of average CD4 trends over time while accounting for correlation of repeated measurements within each individual. To linearize changes over time, the square root transformation of the CD4 values was used. We examined the effects of sex, exposure category, age at SC (16–20, 21–30, 31–40, and >40 years), calendar year of SC (before 1989, 1989–1994, 1995–1996, and 1997 or later), and HIV test interval (short [30 days or less] and long [longer than 30 days]). In total, CD4 data from 5739 (67%) individuals were analyzed.

Incomplete CD4 data due to death or disease progression are likely to be informative. In such cases, RE estimates of the mean rate of CD4 cell count decline may be biased. 37,38 Specifically, subjects who progress to AIDS tend to have fewer CD4 measurements and thus less stable subject-specific estimates of decline than those who do not progress. If subjects who develop AIDS also tend to have steeper rates of CD4 cell decline, the mean rate of CD4 loss estimated by the RE models, which is a weighted mean of the subject-specific estimates with weights proportional to their precision, will also be biased downward. To investigate the presence of selective (informative) drop outs and to reduce potential biases, a sensitivity analysis was carried out applying the joint multivariate random-effects (JMRE) method. 38 This method combines a linear RE model for the underlying pattern of CD4 trends with a log-normal survival model for informative drop outs. The JMRE model implies that log-survival times (ie, time from SC to the development of clinical AIDS or death) are a function of the subject-specific CD4 trends. To adjust standard errors of the model parameters for the unknown survival times (ie, censored survival times), the multiple imputation method was applied. More specifically, 5 pseudocomplete data sets were generated by imputing censored survival times from their conditional distribution. The total variability was the weighted sum of the variability estimated at the convergence of the model and the between-imputation variability.

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Details of the 5739 individuals eligible for analyses are presented in Table 1. The majority of the subjects were male and exposed through sex between men. Overall, the median age was 28.3 years (interquartile range: 23.8–34.2 years). Men tended to be older at SC, with a mean (SD) age at SC of 30.9 (8.9) years compared with 27.0 (7.1) years for women. Also, injection drug users (IDUs) and hemophiliacs were younger at SC compared with subjects infected through other routes. Because ethnicity was not recorded for a substantial number of subjects (57%), this demographic characteristic was not considered further. The overall median HIV test interval was 0.68 years, being 0.67 for persons infected through sex between men, 0.74 for IDUs, 0.67 for persons infected through sex between men and women, and 0.80 for hemophiliacs. There was no difference in median HIV test interval between men and women. During the follow-up period, 2683 (46.8%) subjects had been treated with RTIs at some stage (19.9% ever with monotherapy and 26.8% with dual therapy).

The median time between HIV-1 SC and the first CD4 measurement was 0.71 years (range: same day to 14.1 years). The time interval until the first CD4 measurement did not differ significantly by sex but tended to be longer for IDUs (median [range]: 1 [0–12.9] years) and more so for hemophiliacs (median [range]: 3 [0–14.1] years) compared with subjects with other exposure categories (median [range]: 0.59 [0–11.3] and 0.63 [0–11.7] years for sex between men and sex between men and women, respectively). The median follow-up time from SC to the last CD4 measurement eligible for this analysis was 5.2 years (range: 1 month to 20.5 years). On average, 9 (range: 2–79) CD4 measurements per subject were available, with a median interval of 3.5 months between any 2 successive measurements. By the end of the follow-up period, 1605 (27.4%) subjects had developed clinical AIDS and 302 (5.2%) had died without a diagnosis of AIDS.

Both models of predicted median CD4 trajectories estimated by RE models assuming either a linear or quadratic CD4 cell count model (on the square root scale) show, as expected, a decline over time from SC and give similar predictions during the first 6 to 7 years after SC. After that time, the quadratic model suggests slightly less steep declines than the linear model. Despite the marginally better fit of the quadratic model, for the sake of simplicity, results from the linear model are presented hereafter.

Estimated CD4 cell count at SC differed significantly by age, sex, and exposure group. Although the model adjusted for informative drop outs gave slightly higher estimated CD4 counts of about 10 to 15 cells/μL at SC than the unadjusted model, differences among subgroups were similar (Table 2). Specifically, subjects aged below 40 years at SC tended to have higher CD4 cell counts at SC (about 40 cells/μL) than subjects older at SC (P < 0.01), and women tended to have higher CD4 counts at SC than men (P < 0.01), with the median difference between the sexes being about 30 to 60 cells/μL. IDUs and hemophilic men also tended to have a higher median CD4 cell count at SC than men and women infected through another route (P < 0.01).

Compared with the conventional RE model, estimated average slopes of CD4 decline were steeper when allowing for informative drop out. This is expected, because patients with steeper CD4 decline have a poorer prognosis and thus tend to drop out earlier. The difference between the 2 models was more pronounced for older age groups and for persons with a short HIV-1 test interval, suggesting that informative drop outs were more common among these groups.

There was a monotonic relationship between CD4 slopes and age at SC, with steeper declines in older subjects in both models (see Table 2). Mean rate of CD4 decline was also significantly steeper in subjects with a short HIV-1 test interval [mean (95% confidence interval [CI]) difference in average rate of CD4 decline per year on the square root scale: −0.21 (−0.07 to −0.36) and −0.26 (−0.10 to −0.41) for the unadjusted model and adjusted for informative drop out model, respectively]. There was a tendency for steeper CD4 declines in subjects who seroconverted prior to 1994 than in subjects who seroconverted after 1994 in both models. In the unadjusted model, the mean difference in the rate of CD4 decline (on the square root scale) compared with subjects who seroconverted after 1996 was −0.30 (95% CI: −0.60–0.010; P = 0.058), −0.30 (−0.61–0.001; P = 0.051), and 0.19 (−0.14–0.52; P = 0.255) for those who seroconverted prior to 1989, during 1989 through 1994, and during 1995 through 1996, respectively. The corresponding figures from the model adjusted for informative drop outs were −0.37 (−0.65 to −0.10; P = 0.004), −0.36 (−0.64 to −0.08; P = 0.005), and 0.11 (−0.18–0.41; P = 0.753), respectively. In Table 3, the estimated median (95% CI) CD4 cell counts at 2 and 5 years after SC for those infected through sex between men who seroconverted during 1990 through 1994 are shown by age at SC and HIV-1 test interval. At 5 years after SC, the median difference in CD4 cell counts between the oldest age group and the youngest age group was around 90 cells/μL, whereas the median difference between those with and without a short HIV-1 test interval was around 40 cells/μL.

The rate of CD4 cell count decline did not differ significantly by sex; thus, on the square root scale, the difference between men and women observed at SC was stable throughout the HIV-1 incubation period (P = 0.235) (Fig. 1). Hemophilic men tended to have, on average, a steeper CD4 cell decline (P = 0.014). Nevertheless, given that this group also had a higher median CD4 cell count at SC than the other exposure groups, hemophilic men tended to reach critical low CD4 values at similar times as the rest of the exposure groups. Individuals in the sex between women and men category tended to have, on average, less steep CD4 cell count declines, whereas CD4 mean slopes did not differ significantly between IDUs and subjects in the sex between men category (see Fig. 1).

To investigate the sensitivity of our results to the time interval between SC and the first CD4 measurement, we repeated the analysis, including only subjects with at least 1 CD4 measurement within 1 year since SC. All results apart from those regarding hemophilic men, the majority of whom did not have a CD4 measurement within this interval, were confirmed in this sensitivity analysis. Further sensitivity analysis, including HIV test interval as a continuous variable in the model, gave practically identical results to those of the main analysis. To examine whether time to double antiretroviral therapy (ART) with RTIs acts as an effect modifier, we repeated the analysis, excluding all CD4 measurements taken after the initiation of double ART with RTIs. Results showed that the overall CD4 decline was increased by about 35%, whereas the difference in CD4 decline among those who seroconverted prior to 1994 compared with those who seroconverted after 1994 was diluted. Apart from those changes, all the rest of the results remained qualitatively and quantitatively similar.

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It has been suggested that the age effect on HIV disease progression may be mediated through a more rapid decline in the immune system at older ages. 8,9,39 Douek and colleagues 40 showed in peripheral blood mononuclear cells that the concentration of T-cell receptor–rearrangement DNA circles (TRECs), a marker of recent thymic emigrant αβT cells, declined with age and on HIV-1 infection. Similar results have been reported in other studies. 41,42 We found that shortly after SC, subjects older than 40 years at SC have, on average, a lower CD4 cell count than younger subjects. This finding may imply that under the stress of the HIV-1 infection, the ability of the immune system to replace lost CD4 counts is reduced, particularly in subjects who are older at HIV-1 SC. Several studies have failed to show an age effect on CD4 cell counts trends, however. 9,10,43 We found a monotonic relationship between CD4 slopes and age at SC, with steeper declines in older subjects. At 5 years after SC, the difference in the estimated median CD4 cell count between the youngest (15–20 years) and oldest (>40 years) age groups was about 90 cells/μL. This is in agreement with findings from a recent study conducted in HIV-infected hemophilic men, in which it was shown that subjects aged 30 years or more at SC tended to have, on average, a steeper CD4 decline than subjects aged 13 to 29 years at SC. 44 Both studies used methodology that adjusts for informative drop out.

The large data set from this international collaboration, the wide range of ages at SC as opposed to the typically narrow range observed in cohorts of homosexual men and IDUs, and the adjustment for informative drop out may explain why this study found an age effect but other studies were unable to show an age effect on CD4 cell count trends. In particular, failure to adjust for informative drop out tends to dilute differences by age group and may explain, in part, the discrepancy between our results and those previously reported. 9,10,43

We also found that hemophilic men had a substantially higher CD4 cell count at SC than other subjects, but they also tended to have significantly steeper CD4 declines over time. Although this initial CD4 advantage has previously been reported, 45 the reason for the steeper decline in this group is unclear. This may simply be an artifact, because hemophilic men seroconverted during the early years of the HIV-1 epidemic (1979–1985); thus, CD4 data for this group are rare during the first 7 years after SC. Alternatively, other factors such as HCV/HIV coinfection, which is present in almost all hemophiliacs, could contribute to the explanation of this finding. Nevertheless, the fact that such a finding was not observed among IDUs, the majority of whom are also coinfected with HCV, makes this explanation less likely. It is worth noting that estimated CD4 cell counts in later years do not differ significantly between hemophilic men and others. Our finding that IDUs had a higher estimated median CD4 cell count at SC compared with individuals in the sex between women and men and sex between men categories is consistent with studies reporting a lower viral load among IDUs compared with homosexual men, 24,46 even after adjustment for CD4 cell count. 47 One study, however, has concluded that any differences in viral load by exposure group were mainly driven by the effect of sex differences. 48 In this study, it was found that the absolute CD4 count did not differ significantly by exposure group or sex but that the median CD4 count percent was lower in homosexual men compared with IDUs and in male IDUs compared with female IDUs. These contradictory findings highlight the value of large data sets, which are also heterogeneous with respect to exposure categories, age, and sex. It is possible that non–HIV-related factors such as use of drugs or continuous immune activation due to the existence of other concomitant infections may explain the observed differences. We found no significant difference in CD4 cell decline between IDUs and persons infected through sex between men. We further found that subjects infected though sex between men and women had, on average, a less steep CD4 decline than the rest of the subjects. The biologic reason as well as the clinical relevance of the differences in immunologic response by exposure category observed in this study remains unclear.

It has been consistently reported that among HIV-uninfected subjects, women have around a 60- to 100-cells/μL higher CD4 count than age-matched men. 17,18,49 This difference seems to persist in HIV-1–infected subjects for at least 5 years after SC. 19 It has been reported that women seroconvert for HIV, develop AIDS, and die at a higher CD4 cell count than men. 20 Results remain inconclusive, however, regarding the rate of CD4 cell count decline, with some authors suggesting similar rates of CD4 decline between women and men 20–22 and some suggesting faster rates in women. 23,24 In this study, we found that women have a higher median CD4 cell count at SC than men, with the difference being around 30 to 50 cells/μL, which may be too small a difference to be reliably detected in all studies. The subsequent rate of CD4 decline did not differ significantly by sex, however. Thus, on the square root scale, the initially observed difference between the sexes persists throughout the HIV-1 incubation period. This implies that the difference in the absolute number of CD4 cells diminishes over time (see Fig. 1). Several studies have suggested that women also have lower plasma HIV RNA levels than men, even after controlling for CD4 cell counts. 14,23,24,50 These differences by sex (and possibly by exposure group) have raised concerns as to whether guidelines for therapy should be adjusted for demographic characteristics. 24,50 Despite the immunologic and virologic differences between the sexes, several studies have reported a very marginal or absence of effect of sex on rates of HIV-1 disease progression. 4,6,15,16 Social (eg, socioeconomic status, access to care) or biologic (eg, hormonal status) factors could influence sex differences. Elucidation of the influencing mechanisms could increase understanding of the pathogenesis of HIV-1 infection and may lead to new treatment modalities. Further investigation is needed to evaluate the clinical relevance of the observed differences, given the size of the difference observed.

Subjects with a short HIV-1 test interval appeared to have faster loss of CD4 cell counts, consistent with previously reported results that the presence of a clinically recognized SC illness is associated with faster disease progression. 25,26 Furthermore, this finding highlights the importance of early virologic-immunologic events on disease progression.

In this study, we found a marginal tendency for steeper CD4 declines in subjects who seroconverted prior to 1994 than in subjects who seroconverted after 1994. This could reflect secular trends in other factors such as closer immunologic follow-up as knowledge of the pathogenesis of HIV-1 and of patient's care has increased over time. Indeed, as sensitivity analysis showed, much of the observed difference in CD4 decline by calendar year could be attributed to the availability of double ART with RTIs in more recent years. At any rate, there was certainly no suggestion of a faster loss of CD4 cells in those seroconverting more recently, which does not support the hypothesis of the emergence of more virulent strains of HIV.

Our results provide further information regarding factors that influence the HIV-1 disease progression mediated through CD4 cell decline during the natural history of the HIV-1 infection. They suggest that before the initiation of HAART or other interventions based on immune status, consideration of demographic factors may be worthwhile. Nevertheless, further investigation is needed to elucidate the underlying biologic mechanisms and the clinical relevance of the observed differences in CD4 cell count trends in terms of disease progression and/or evaluation of optimal time for HAART initiation, particularly for women and IDUs.

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Members of the CASCADE Collaboration

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Analysis and Writing Committee

Giota Touloumi, Nikos Pantazis, Abdel Babiker, A. Sarah Walker, Olga Katsarou, Angelos Hatzakis, Anastasia Karafoulidou, Kholoud Porter, and Janet Darbyshire

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Steering Committee

Valerie Beral, Roel Coutinho, Janet Darbyshire (Project Leader), Julia Del Amo, Noël Gill (Chairman), Christine Lee, Laurence Meyer, and Giovanni Rezza

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Coordinating Center

Kholoud Porter (Scientific Coordinator), Abdel Babiker, A. Sarah Walker, Janet Darbyshire, and Freya Tyrer

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Aquitaine cohort, France: Francois Dabis, Rodolphe Thiebaut, Geneviève Chêne, and Sylvie Lawson-Ayayi; SEROCO cohort, France: Laurence Meyer and Faroudy Boufassa; German cohort, Germany: Osamah Hamouda and Klaus Fischer; Italian Seroconversion Study, Italy: Patrizio Pezzotti and Giovanni Rezza; Greek Hemophilia cohort, Greece: Giota Touloumi, Angelos Hatzakis, Anastasia Karafoulidou, and Olga Katsarou; Edinburgh Hospital cohort, U.K.: Ray Brettle; Madrid cohort, Spain: Julia Del Amo and Jorge del Romero; Amsterdam Cohort Studies among homosexual men and drug users, The Netherlands: Liselotte van Asten, Birgit van Benthem, Maria Prins, and Roel Coutinho; Copenhagen cohort, Denmark: Ole Kirk and Court Pedersen; Valencia Injection Drug User cohort, Spain: Ildefonso Hernández Aguado and Santiago Pérez-Hoyos; Oslo and Ulleval Hospital cohorts, Norway: Anne Eskild, Johan N. Bruun, and Mette Sannes; Royal Free hemophilia cohort, U.K.: Caroline Sabin and Christine Lee; U.K. Register of HIV Seroconverters, U.K.: Anne M. Johnson, Andrew N. Phillips, Abdel Babiker, Janet H. Darbyshire, Noël Gill, and Kholoud Porter; Swiss HIV cohort, Switzerland: Patrick Francioli, Philippe Vanhems, Matthias Egger, and Martin Rickenbach; Sydney AIDS Prospective Study, Australia: David Cooper and John Kaldor; Sydney Primary HIV Infection cohort, Australia: David Cooper, John Kaldor, and Tim Ramacciotti; Badalona Injection Drug User hospital cohort, Spain: Roberto Muga; Lyon Primary Infection cohort, France: Philippe Vanhems; MRC Biostatistics Unit, Cambridge, U.K.: Nicholas E. Day and Daniela De Angelis

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1. Mellors JW, Muňoz A, Giorgi JV, et al. Plasma viral load and CD+ lymphocytes as prognostic markers of HIV-1 infection. Ann Intern Med. 1997; 126:946–954.
2. O'Brien WA, Hartigan PM, Daar ES, et al. for the VA Cooperative Study Group on AIDS. Changes in plasma HIV RNA levels and CD4+ lymphocyte counts predict both response to antiretroviral therapy and therapeutic failure. Ann Intern Med. 1997; 126:939–945.
3. Cozzi Lepri A, Pezzotti P, Dorrucci M, et al. The Italian Seroconversion Study. HIV disease progression in 854 women and men infected through injecting drug use and heterosexual sex and followed for up to nine years from seroconversion. BMJ. 1994; 309:1537–1542.
4. Collaborative Group on AIDS Incubation and HIV Survival Including in the CASCADE EU Concerted Action. Time from HIV-1 seroconversion to AIDS and death before widespread use of highly-active antiretroviral therapy: a collaborative re-analysis. Lancet. 2000; 355:1131–1137.
5. Rezza G, D'Egidio P, Cozzi Lepri A, et al. Effect of age, sex, duration of drug use and infection with viral hepatitis B and C on lymphocyte subsets in a population of HIV-negative drug users in Italy. Addict Biol. 1997; 2:201–206.
6. Prins M, Veugelers PJ, for the European Seroconverter Study and the Tricontinental Seroconverter Study. Comparison of progression and non-progression in injecting drug users and homosexual men with documented dates of HIV-1 seroconversion. AIDS. 1997; 11:621–631.
7. Darby SC, Ewart DW, Giangrande PLF, et al, for the UK Haemophilia Centre Directors Organization. Importance of age at infection with HIV-1 for survival and development of AIDS in UK haemophilia population. Lancet. 1996; 347:1573–1579.
8. Phillips AN, Lee CA, Elford J, et al. More rapid progression to AIDS in older HIV-infected people: the role of CD4+ T-cell counts. J Acquir Immun Defic Syndr Hum Retrovirol. 1991; 4:970–975.
9. Touloumi G, Hatzakis A, Rosenberg PS, et al. Effects of age at seroconversion and baseline HIV RNA level on the loss of CD4+ cells among persons with hemophilia. AIDS. 1998; 12:1691–1697.
10. Taylor JMG, Cumberland WG, Sy JP. A stochastic model for analysis of longitudinal AIDS data. Journal of American Statistical Association. 1994; 89:727–736.
11. Multicohort Analysis Project Workshop. Immunologic markers of AIDS progression: consistency across five HIV-infected cohorts, part 1. AIDS. 1994; 8:911–921.
12. Brettle RP, McNeil AJ, Burns S, et al. Progression of HIV: follow-up of Edinburgh injecting drug users with narrow seroconversion intervals in 1983-1985. AIDS. 1996; 10:419–430.
13. Phillips AN, Antunes F, Stergious G, et al. A sex comparison of rates of new AIDS defining disease and death in 2554 AIDS cases. AIDS. 1994; 8:831–835.
14. Farzadegan H, Hoover DR, Astemborski J, et al. Sex differences in HIV-1 viral load and progression to AIDS. Lancet. 1998; 352:1510–1514.
15. Junghans C, Ledergerber B, Chan P, et al. Sex differences in HIV-1 viral load and progression to AIDS. Swiss HIV Cohort Study. Lancet. 1999; 353:589.
16. von Overbeck J, Egger M, Davey Smith G, et al. Survival in HIV infection, do sex and category of transmission matter? AIDS. 1994; 8:1307–1313.
17. Tollerud DJ, Clark JW, Morris Brown L, et al. The influence of age, race and sex on peripheral blood mononuclear-cell subjects in healthy nonsmokers. J Clin Immunol. 1989; 9:214–222.
18. Maini MK, Gilson RJC, Chavda N, et al. Reference ranges and sources of variability of CD4 counts in HIV-seronegative women and men. Genitourinary Med. 1996; 72:27–31.
19. Delmas MC, Jadand C, de Vincenzi I, et al. Sex differences in CD4 cell counts persist after HIV-1 infection. AIDS. 1997; 11:1071–1073.
20. Prins M, Robertson RJ, Brettle RP, et al. Do sex differences in CD4 cell count matter? AIDS. 1999; 13:2361–2364.
21. Galai N, Vlahov D, Margolick JB, et al. Changes in markers of disease progression in HIV-1 seroconverters: a comparison between cohorts of injecting drug users and homosexual men. J Acquir Immune Defic Syndr Hum Retrovirol. 1995; 8:66–74.
22. Pezzotti P, Phillips AN, Dorrucci M, et al, for the Italian Seroconversion Study Group. Category of exposure to HIV and age in the progression to AIDS: longitudinal study of 1199 people with known dates of seroconversion. BMJ. 1996; 313:583–586.
23. Sterling TR, Lyles CM, Vlahov D, et al. Sex differences in longitudinal human immunodeficiency virus type 1 RNA levels among seroconverters. J Infect Dis. 1999; 180:666–672.
24. Anastos K, Gange SJ, Lau B, et al. Association of race and sex with HIV-RNA levels and immunologic progression. J Acquir Immune Defic Syndr. 2000; 24:218–226.
25. Lindback S, Brostrom C, Karlsson A, et al. Does symptomatic primary HIV-1 infection accelerate progression to CDC stage IV disease, CD4 count below 200 × 106/l, AIDS, and death from AIDS? BMJ. 1994; 309:1535–1537.
26. Vanhems P, Lambert J, Cooper DA, et al. Acute HIV-1 illness severity and prognosis: a dose response effect. Clin Infect Dis. 1998; 26:323–329.
27. Pedersen C, Lindhardt BO, Jensen BL, et al. Clinical course of primary HIV infection: consequences for subsequent course of infection. BMJ. 1989; 299:154–157.
28. CASCADE European Collaborative Project. The relationship between the HIV test interval, demographic factors and HIV disease progression. Epidemiol Infect. 2001; 127:91–100.
29. Vanhems P, Allard R, Dhenain M, et al. HIV seroconversion interval and demographic characteristics: no evidence of selection bias. Sex Transm Infect. 2001; 77:446–448.
30. Sinicco A, Fora R, Raiteri R, et al. Is the clinical course of HIV-1 changing? Cohort Study. BMJ. 1997; 314:1232–1237.
31. Vanhems P, Lambert J, Guerra M, et al. Association between the rate of CD4+ T cell decrease and the year of human immunodeficiency virus (HIV) type 1 seroconversion among persons enrolled in the Swiss HIV Cohort Study. J Infect Dis. 1999; 180:1803–1808.
32. O'Brien TR, Hoover DR, Rosenberg P, et al. Evaluation of secular trends in CD4+ lymphocyte loss among human immunodeficiency virus type 1 (HIV-1)-infected men with known dates of seroconversion. Am J Epidemiol. 1995; 142:636–642.
33. Galai N, Cozzi Lepri A, Vlahov D, et al. Temporal trends of initial CD4 cell counts following human immunodeficiency in Italy. 1985-1992. Am J Epidemiol. 1996; 143:278–282.
34. 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 Med. 2000; 1:224–231.
35. 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.
36. Centers for Diseases Control. 1993 revised classification system for HIV infection and expanded surveillance case definition for AIDS among adolescents and adults. Morbid Mortal Wkly Rep. 1992; 41:1–19.
37. Wu MC, Bailey KR. Analyzing changes in the presence of informative right censoring caused by death and withdrawal. Stat Med. 1988; 7:337–346.
38. Touloumi G, Pocock SJ, Babiker AG, et al. Estimation and comparison of change in longitudinal studies with informative drop-outs. Stat Med. 1999; 18:1215–1233.
39. Touloumi G, Hatzakis A, Karafoulidou A, et al. The slope of change in CD4 counts as prognostic factor in the progression of HIV infection. Presented at the 10th International Conference on AIDS, Yokohama, Japan, August 1994.
40. Douek DC, McFarland RD, Keiser PH, et al. Changes in thymic function with age and during the treatment of HIV infection. Nature. 1998; 396:690–695.
41. Zhang L, Lewin SR, Markowitz M, et al. Measuring recent thymic emigrants in blood of normal and HIV-1-infected individuals before and after effective therapy. J Exp Med. 1999; 190:725–732.
42. Hatzakis A, Touloumi G, Karanicolas R, et al. Effect of recent thymic emigrants on progression of HIV-1 disease. Lancet. 2000; 355:599–604.
43. Lange N, Carlin BP, Gelfand AE. Hierarchical Bayes models for the progression of HIV infection using longitudinal CD4 T-cell numbers. Journal of American Statistical Association. 1992; 87:615–626.
44. Touloumi G, Pocock SJ, Babiker AG, et al. Impact of missing data due to selective drop-outs in cohort studies and clinical trials. Epidemiology. 2002; 13:347–355.
45. Mejer K, Van Der Meer J, Smit JW, et al. Lymphocyte subset abnormalities in multitransfused HIV-negative haemophilia A patients are not due to chronic hepatitis C virus infection. Br J Haematol. 2000; 109:529–533.
46. Katzenstein DA, Hammer SM, Hughes MD, et al. The relation of virologic and immunologic markers to clinical outcomes after nucleoside therapy in HIV-infected adults with 200 to 500 CD4 cells per cubic millimeter. N Engl J Med. 1996; 335:1091–1098.
47. Saul J, Erwin J, Sabin CA, et al. The relationship between ethnicity, risk group, and virus load in human immunodeficiency virus type 1 antiretroviral-naïve patients. Journal of Infectious Diseases. 2001; 183:1518–1521.
48. Lyles ML, Vlahov D, Farzagedan H, et al. Comparison of two measures of HIV type 1 load in HIV risk groups. J Clin Microbiol. 1998; 36:3647–3652.
49. Reichert T, DeBruyere M, Deneys V, et al. Lymphocyte subset reference ranges in adult Caucasians. Clin Immunol Immunopathol. 1991; 60:190–208.
50. Kalish LA, Collier AC, Flanigan TP, et al, for the Viral Activation Transfusion Study Group. Plasma human immunodeficiency virus (HIV) type 1 RNA load in men and women with advanced HIV-1 disease. Journal of Infectious Diseases. 2000; 182:603–606.

CD4 cells counts; seroconversion; HIV infection; age; sex; exposure category; longitudinal studies; informative drop outs

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