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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

CASCADE Collaboration

Clinical Science

Background: One key piece of information required when deciding whether to initiate antiretroviral therapy is the risk of AIDS before the next clinic visit. Information on the short-term (6-month) risk of AIDS according to the current viral load and CD4 cell count in untreated individuals and those treated in the zidovudine monotherapy era (i.e., pre-September 1995), especially in those with CD4 cell count > 200 × 106 cells/l, is lacking.

Methods: Risk of AIDS was assessed in 3226 subjects with viral load and CD4 cell count known before initiation of antiretroviral therapy or during the zidovudine monotherapy era. These were from CASCADE Collaboration in which data from 20 cohorts of individuals with known dates of seroconversion to HIV, based in clinics in Europe and Australia, have been combined.

Results: During a total 5126.0 person-years of follow-up, 219 individuals developed AIDS. In those with current CD4 cell count < 200 × 106 cells/l, 6-month risks were 4.9, 12.7, 17.7 and 22.4% for viral load groups < 10 000, 10 000–29 999, 30 000– 99 999 and ≥ 100 000 copies/ml, respectively. For CD4 cell counts 200–349 × 106 cells/l risks were 0.5, 1.6, 3.2 and 4.7%, respectively, for the four viral load groups while the corresponding values for group with CD4 cell count ≥ 350 × 106 cells/l were 0.2%, 0.5%, 0.9% and 2.2%, respectively. Results were similar when analysis was restricted to those with no antiretroviral drug experience. Older people had a higher risk of AIDS for a given CD4 cell count and viral load than younger people.

Conclusion: Combined with consideration of other issues, these estimates should prove useful information when deciding whether to initiate antiretroviral therapy in HIV-infected individuals.

From the Cascade collaboration. *See the Appendix for members.

Requests for reprints: Dr K. Porter, MRC Clinical Trials Unit, 222 Euston Rd, London NW1 2DA, UK. Email:

Correspondence to: Dr A. Phillips, Royal Free Centre for HIV Medicine and Department of Primary Care & Population Sciences, Royal Free & University College Medical School, Royal Free Campus, Rowland Hill St, London NW3 2PF, UK. Email:

Received: 4 November 2002; revised: 14 April 2003; accepted: 23 June 2003.

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When deciding on whether to initiate antiretroviral therapy in asymptomatic individuals, one key issue is the risk of development of AIDS if treatment is deferred. Of principal interest is the extent of AIDS risk over the period of time before the patient is next assessed, typically 3–6 months. If treatment is deferred, then the issue can be reassessed at the next patient visit – using newly available information on AIDS predictors – and the treatment decision revised if appropriate. While lower CD4 cell count and higher HIV viral load in plasma are well-established predictors of raised risk of AIDS [1–12], information on the absolute risk of AIDS over the 3–6-month time scale is surprisingly lacking and treatment guidelines have tended to rely on longer term estimates [13–15]. Such longer term estimates (e.g., over 3 years) were perhaps more relevant before the advent of triple combination therapies, when risk of AIDS could not be so rapidly reversed by initiation of therapy as is now the case.

In the present study, data from Cascade (Concerted Action on SeroConversion to AIDS and Death in Europe; a multicohort collaborative project) is used to produce estimates of the short-term risk of AIDS and death for deciding when/whether to initiate therapy.

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Details of CASCADE are reported elsewhere [16]. In brief, CASCADE is a collaboration among the investigators of 20 cohorts in Europe and Australia that began in April 1997. All are cohorts of HIV-1-infected individuals for whom it was possible to estimate the time of HIV seroconversion, most commonly because there was a negative HIV antibody test result at most 3 years before the first positive test result. Data pooled from these cohorts in July 2001 included demographic and HIV-exposure information, date and type of initial AIDS diseases, antiretroviral treatments, CD4 lymphocyte counts and HIV viral load values.

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Statistical methods

In brief, the approach was to categorize person-time in AIDS-free people according to the most recent CD4 cell count and viral load (which had to have been measured in the previous 6 or 12 months, respectively). By counting numbers of people developing AIDS during the person-time in each of these categories, the rate of AIDS for each CD4 cell count/viral load category could be calculated. Any person-time for which the viral load had not been measured in the previous year and the CD4 cell count in the previous 6 months was not included in the analysis, so if a person developed AIDS during such a time then this also was not included. This is because an analysis was wanted that related to situations where an up-to-date level of both markers was available. The details are as follows.

Individuals were eligible for inclusion if they were treatment naive or in the zidovudine monotherapy era (pre-September 1995, although it is recognized that some dual therapy regimens were used during this time period also), AIDS-free, and with at least 1 day of active follow-up with viral load and CD4 cell count both available (defined for viral load as measured within the past 12 months and for CD4 cell count as measured in the past 6 months). A longer period for the viral load to remain ‘current’ was chosen as this parameter tends to exhibit smaller changes over time [6,7,10]. For each eligible person, the first period of follow-up (accordingly allocated to a CD4 cell count/viral load category) was counted from the date (s)he first became eligible until the first occurrence of AIDS, death (therefore, follow-up was censored at death if this occurred before an AIDS diagnosis), date last known AIDS free, a new CD4 cell count or viral load measure, expiry of the viral load (12 months after it was measured) or CD4 cell count (6 months after it was measured) or start of antiretroviral therapy/interleukin 2/hydroxyurea (or last date that these were known not to have been started). This last censoring at start of antiretroviral therapy was only applied after September 1995 (i.e., after the end of the zidovudine monotherapy era). Consequently, a period of follow-up could not exceed 6 months (because the CD4 cell count to which it relates expired at this time). A person contributed further periods of follow-up (but not necessarily to the same CD4 cell count/viral load category) if (s)he was again eligible at any later point. This was the case for those whose first period of follow-up ended through measurement of a new viral load/CD4 cell count and for those whose first period ended because of expiry of a viral load or CD4 cell count, if a new measure was subsequently made.

The main analysis consisted simply of numbers of individuals with an AIDS event, person-years of follow-up and rate of AIDS (and cumulative risk of AIDS over 6 months, based on assumption of constant rate over this period) according to CD4 cell count and viral load category. Poisson regression [17] was used to explore the influence on CD4 cell count/viral load-specific AIDS rates of additional factors such as type of assay, gender, age, exposure group, ethnicity, calendar year (before/after January 1997; as a proxy for whether the assay was carried out in real-time or on a frozen sample). We also fitted Cox models with the date of seroconversion as time zero (allowing for late entry) and with CD4 cell count and viral load fitted as time dependent covariates (and remaining current for the same time periods) to check that results were similar.

A Poisson regression model was then fitted including only CD4 cell count, viral load and age fitted as continuous covariables, in order to generate predictions of the rate for any given specific value of these three variables. Log and square root transformations were considered for the CD4 count, as well as leaving it untransformed, because these have been previously found possibly to improve the model fit (e.g. 11). For viral load and age, a log transformation was also considered (i.e. for viral load a log log transformation).

Assays used for measuring viral load were classified broadly into Roche (Roche Molecular Systems, Branchburg, New Jersey, USA), NASBA (Organon Teknika, Durham, North Carolina, USA), Chiron (Emeryville, California, USA) and other.

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Of the total 9133 individuals included in the CASCADE data set, 3226 fulfilled the eligibility criteria for inclusion in this analysis, as set out above. Table 1 shows the characteristics of these patients: 77.4% were male and the median age was 32 years. HIV exposure groups were homosexual men 51.8%, injection drug use 14.3% and heterosexual sex 21.5%. Approximately half of the subjects had their first viral load measure before 1997. Since the test was only widely introduced around the end of 1996, in most cases these measurements would have been made on frozen samples. There were a median of eight viral load values available per person [interquartile range (IQR), 4–13] and median of 12 CD4 cell counts (IQR, 7–19). Ethnic origin was only available for 1911 subjects, of whom 92% were white. This breakdown of patients reflects the fact that these were individuals for whom a date of seroconversion was known.

Table 1

Table 1

Included in Table 2 are the numbers of person-years of experience among AIDS-free subjects who had not started antiretroviral therapy or were in the zidovudine monotherapy era (pre-September 1995), according to the current CD4 cell count (within last 6 months) and viral load (within past 12 months). In total, there were 5126.0 person-years of such experience in the 3226 individuals included. The median calendar date of the person years was March 1995 (IQR, July 1991 to February 1998). Overall, for 55.0% of the person-years, viral load values used were measured using the Roche method, 14.6% with NASBA, 7.8% with Chiron, 4.8% with another method and for 17.9% the method was unknown. During the 5126.0 person-years of follow-up, 219 individuals developed AIDS (43 Kaposi's sarcoma, 36 Pneumocystis carinii pneumonia, 36 oesophageal candidiasis, 14 toxoplasmosis, 11 non-Hodgkin's lymphoma, 10 cryptosporidiosis, 10 HIV encephalopathy, 59 others). The distribution of person-years in CD4 cell count/viral load categories reflects the fact that subjects were identified at the time of seroconversion in that most experience is in individuals with relatively high CD4 cell counts. The tendency for those with lower CD4 cell count to have a higher viral load can be seen from the fact that, for example, only 8% of the person-years with CD4 cell count ≥ 350 × 106 cells/l are with viral load ≥ 100 000 copies/ml, compared with 14% of those with CD4 cell count 200–349 × 106 cells/l and 26% of those with CD4 cell count < 200 × 106 cells/l.

Table 2

Table 2

Also shown in Table 2 are the numbers of individuals developing AIDS in each CD4 cell count/viral load category and the AIDS incidence rate. As expected, there is a trend for increasing AIDS rate with lower CD4 cell count and higher viral load. The trend for increasing rate with higher viral load seems roughly consistent in each CD4 cell count category and, similarly, the increasing rate with lower CD4 cell count is approximately consistent across viral load categories. Overall, the rate ranges from 0.004 per person year in individuals with CD4 cell count ≥ 350 × 106 cells/l and viral load < 10 000 copies/ml to 0.507 per person-year in individuals with CD4 cell count < 200 × 106 cells/l and viral load ≥ 100 000 copies/ml, a 127-fold increase. If it is assumed that these rates are constant over a 6-month period, then they can be translated into the percentage risk of developing AIDS in 6 months [1 − exp(−0.5Rate)] and these percentage risks are also shown. The risks over 3 months are approximately half of these values.

A Poisson regression model was fitted to assess the influence of other factors on the AIDS rate. First, a basic model was fitted containing CD4 cell count and viral load in the groups shown in Table 2. The rate ratios for those with current CD4 cell count < 200 × 106 cells/l and those with current CD4 cell count 200–349 × 106 cells/l, relative to those with CD4 cell count ≥ 350 × 106 cells/l were 18.7 and 3.0, respectively. Rate ratios for those with viral load ≥ 100 000 copies/ml, 30 000–99 999 copies/ml and 10 000–29 999 copies/ml, relative to those with < 10 000 copies/ml were 6.5, 2.4 and 1.4, respectively. There was no significant interaction between the effects of CD4 cell count and viral load on AIDS rate, even when fitted as continuous variables (P = 0.1; Wald test for interaction term).

Sex, age (current), HIV exposure group and calendar date (pre/post January 1997, after which most viral load assays would be measured on fresh samples) were also introduced into the model. Age was significantly associated with the AIDS rate, with older people experiencing a higher rate [rate ratio 1.23 per 10 years older; 95% confidence interval (CI), 1.07–1.42; P = 0.003, Wald test] and there was a tendency for lower rate of AIDS in more recent calendar times (rate ratio 0.64 for post- versus pre-January 1997; 95% CI, 0.45–0.91; P = 0.01, Wald test). Analyses considering only experience since January 1997 gave rates for < 10 000, 10 000–29 999, 30 000–99 999 and ≥ 100 000 copies/ml at CD4 cell count < 200 × 106 cells/l of 0.050, 0.125, 0.150 and 0.486, respectively; for CD4 200–349 × 106 cells/l of 0.018, 0.012, 0.065 and 0.058, respectively; and for CD4 cell count ≥ 350 × 106 cells/l of 0.004, 0.003, 0.011 and 0.032, respectively. There was no significant effect of sex or HIV exposure group.

Models were also fitted that allowed for the association between viral load and AIDS rate to vary according to assay (Roche, Chiron, NASBA, other/unknown) and by sex, but in neither case did these significantly improve the model fit [chi-square 16.2 on 12 degrees of freedom (df); P = 0.2 and chi-square 8.4 on 4 df; P = 0.08, respectively; likelihood ratio test]. AIDS rates were also calculated according to viral load groups separately for women (numbers were too small to calculate rates for each viral load/CD4 cell count combination). The rates were 0.009 (95% CI, 0.003–0.019), 0.048 (95% CI, 0.022–0.091), 0.048 (95% CI, 0.018–0.105) and 0.108 (95% CI, 0.040–0.236) for viral load groups < 10 000, 10 000–29 999, 30 000–99 999 and ≥ 100 000 copies/ml, respectively, compared with values of 0.010 (95% CI, 0.006–0.016), 0.031 (95% CI, 0.020–0.042), 0.082 (95% CI, 0.063–0.101) and 0.154 (95% CI, 0.119–0.189) for men.

In the main analysis, person-years in which therapy had been started were included if these occurred in the zidovudine monotherapy era (pre-September 1995). Analyses were also run on the subset of person-years (4427.6 years of the total 5126.0 person years) for which no antiretroviral therapy had been started. Rates were very similar to the main analysis, particularly in the higher two CD4 cell count categories. In a further sensitivity analysis, follow-up that occurred up to 30 days after the initiation of antiretroviral therapy was included. This was because of concern that occurrence of symptoms that subsequently lead to an AIDS diagnosis can trigger initiation of antiretroviral therapy before the date of a formal AIDS diagnosis. Rates were again very similar to the main analysis.

The risk of AIDS in 6 months given in Table 2 are to some extent limited by the fact that individuals with appreciably differing CD4 cell count and/or viral load values are categorized together, because each category has to be sufficiently large for a rate to be calculated with reasonable precision. Also, for the same reason, the effect of age is not incorporated. Therefore, a Poisson model was also fitted in which CD4 cell count, viral load and age were continuous variables; this allowed generation of a model that can be used to calculate a predicted 6-month risk from exact individual CD4 cell count and viral load values and age. In this model, CD4 cell count was fitted as square root, because this led to a model with lower likelihood. The results of the model are shown in Table 3. The unadjusted rate ratios for viral load and age were reduced markedly towards 1 after adjustment for the CD4 cell count (although still remained statistically significant), while the rate ratio for the CD4 cell count remained almost unchanged after adjustment for viral load and age. For a given CD4 cell count, viral load and age, the predicted AIDS rate can be calculated as

Table 3

Table 3

rate = exp{−3.55 + [−0.21√(CD4 cell count)] + 0.71 (log viral load) + 0.024(Age)}

From this rate, the 6-month percentage risk of AIDS can be calculated as

[1 − exp(−0.5Rate)] × 100%

Values of the 6-month risk of AIDS according to various CD4 cell count, viral load and age combinations, calculated using this formula, are shown in Table 4.

Table 4

Table 4

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One of the several issues [13–15] that need to be considered when deciding whether to start antiretroviral therapy is the risk of development of AIDS if therapy is delayed. The CD4 cell count and the plasma viral load are known to be associated with this risk [1–12] and both are typically monitored every 3–6 months in individuals under clinical care for HIV infection [13–15]. This suggests that this is the most relevant time period over which to assess AIDS risk and to guide clinicians and patients in deciding whether/when to initiate therapy. We have used data from a large collaborative project that pools data from several seroconverter studies to estimate 6-month risk of AIDS according to the viral load and CD4 cell count and age. Although estimates of the risk of AIDS over a longer time period [e.g., 3–9 years in the Multicenter AIDS Cohort Study (MACS) [2]] have been published, our results are the first to our knowledge that focus on the risk of AIDS occurring before the next clinical assessment. This is an important distinction. For example, estimates from the MACS suggest that for a person with viral load [measured using reverse transcriptase–polymerase chain reaction (RT-PCR)] of > 55 000 copies/ml and CD4 cell count > 350 × 106 cells/l there is an estimated 39.6% risk of developing AIDS in the next 3 years (85.0% in 9 years) [15]. These values are used to help to decide on when to initiate therapy and it may appear, at first sight, that this risk is sufficiently high to indicate that therapy should be started. However, our estimates suggest that the 6-month risk for such a person (CD4 cell count ≥ 350 × 106 cells/l and viral load > 100 000 copies/ml) is 2.2% (Table 2). Many might consider this level of risk acceptable and defer therapy at least until the next patient visit in 3–6 months, when a further CD4 cell count and viral load measurement would be available. It is also worth noting that there are 1442 individuals in our analysis contributing to risk estimates for the CD4 cell count range 200–350 × 106 cells/l – a key area of uncertainty over therapy initiation – compared with 231 used for estimates in the treatment guidelines [2,15]. It is difficult to compare 6-month risks directly with those from the MACS, but visual inspection of Kaplan–Meier curves from the MACS suggests that in the highest viral load group (> 55 000 copies/ml using RT-PCR) risks are approximately 40%, 10% and 3% for CD4 cell count groups < 200, 200–349 and > 350 × 106 cells/l, respectively, somewhat higher than values we obtained [2]. However, in the lower viral load categories, the estimated 6-month risks in the MACS were zero in most CD4 cell count/viral load groups and hence lower than our estimates.

The viral load assay used was known for 82% of values. Of these, 67% were measured using one of the Roche RT-PCR assays. We could not detect a statistically significant difference in the association between CD4 cell count/viral load and risk of AIDS according to assay in our models. However, the Chiron branched DNA assay, which has previously been shown to measure approximately twofold lower than the Roche RT-PCR assay [2], was known to have been used for only 7.9% of viral load values. Although in our analysis we did not adjust viral load values measured using the Chiron assay, it should be born in mind when using our risk estimates in conjunction with this assay that the true risk might be more accurately estimated from two times the viral load value than from the crude value.

It has been suggested that there may be differences between men and women in the association between viral load and risk of AIDS [18]. We found no significant evidence for this in our data, but the number of women was not sufficiently large to rule out such gender differences definitively. There have also been suggestions that viral load levels may be different in those of non-white ethnic group [19,20]. We did not have data on ethnicity for a sufficient proportion of the cohort to be able to study whether CD4 cell count and viral load-specific AIDS risks differ by race; of those with data on race available, 92% were white, so these estimates should be used with caution outside the Caucasion, developed world setting. In agreement with previous reports, we identified an effect of age on risk of AIDS that was independent of the CD4 cell count and viral load [21,22]. Hence we included this in our predictive model for assessing the 6-month risk of AIDS. Although dates of seroconversion could be estimated in these individuals, we did not include this as a potential predictor because this is likely to be unknown in most clinical situations.

There was a statistically significant tendency for the risk of AIDS, for a given CD4 cell count and viral load, to be lower in the period post-January 1997, when viral load assays were mainly measured on fresh samples. There are several possible explanations. It could relate to some tendency for viral loads to be underestimated when the measurement is performed on frozen samples (which have perhaps sometimes been thawed and refrozen) or for stored specimens to be serum rather than plasma [23]. Also, it could be that there was some selection effect, such that those who had a higher risk of AIDS were more likely to have samples frozen. This is difficult to ascertain; however, in general, storing of samples was part of a routine protocol and not dependent on the patient's health status. In either case, this would mean that our 6-month risk estimates, based on viral load measurements measured before and after January 1997, may actually overestimate the risk associated with a given viral load value derived from a fresh sample. Conversely, the risk after January 1997 could be underestimated. There may have been some underreporting of use of therapy, which has increased in recent years, meaning that some individuals on antiretroviral therapy were incorrectly included in our analysis. This should not have occurred to a great extent because we did not include follow-up for individuals when it was uncertain whether or not antiretroviral therapy had been started. Equally, as more potent regimens have become available, individuals at higher risk of AIDS may have been more likely to start therapy, leaving a selected group of low-risk individuals in the analysis after January 1997. We partly addressed this issue by performing an analysis in which we extended follow-up to 1 month after the start date of antiretroviral therapy, to include AIDS events that perhaps triggered the start of therapy but for which formal diagnosis date was after starting highly active antiretroviral therapy. Rates from this analysis did not differ substantially from the main analysis. Another potential explanation for the lower AIDS risk in more recent years is that there may have been increased use of disease-specific prophylaxis. We do not have data to study this possibility directly. However, it would not explain why AIDS risk is lower since January 997 even in those with CD4 cell counts > 200 × 106 cells/l.

We concentrated on risk of AIDS rather than of death because we wished to focus on the endpoint that would be potentially preventable by antiretroviral therapy. This would seem to be the most relevant consideration if the risk estimates are used to decide on when to initiate therapy. A limitation of our approach is that some individuals will die from an AIDS disease but this will never have been formally diagnosed. We do not have sufficiently accurate data on cause of death on all individuals in the joint cohort to distinguish reliably HIV-related deaths from other deaths.

Although our main focus is on the risk of AIDS in the absence of therapy, we did include individuals who were on therapy before September 1995. At this time, most individuals on antiretroviral therapy were treated with zidovudine monotherapy, although some individuals in clinical trials were using dual nucleoside therapy. We felt that the association between viral load and CD4 cell count and risk of AIDS was not likely to be markedly affected by use of therapy at this time. In agreement with this, results from a subanalysis of those who had never started any antiretroviral therapy gave similar results to our main analysis.

Another feature of our analysis was that person-years were attributable to the most recent CD4 cell count and viral load measurement. This means that if a new CD4 cell count or viral load was measured then the period of follow-up was ended and continued follow-up was reallocated to a new CD4 cell count/viral load category (possibly to the same one). In some sense, there is the possibility of informative censoring, if visits and CD4 cell count/viral load measurements were driven by clinical symptoms. However, the fact that we were censoring from an observation period but not from the entire analysis makes it unlikely there was an overall tendency to underestimate CD4 cell count/viral load-specific rates.

Recently, some studies have assessed the risk of AIDS according to the latest CD4 cell count and viral load in individuals who have started antiretroviral therapy with three or more drugs [24,25]. In this setting, it appears that the viral load has relatively less independent prognostic value than in the untreated/monotherapy situation we have studied.

In summary, we have generated estimates of the short-term risk of AIDS diseases according to the current CD4 cell count and viral load in untreated or monotherapy-treated individuals. These estimates are potentially useful for decisions concerning whether to initiate antiretroviral therapy.

Sponsorship: CASCADE is funded through a grant from the European Union (QLK2–2000–01431) and has received additional funding from GlaxoSmithKline.

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Analysis and Writing Committee: Andrew N Phillips and Patrizio Pezzotti.

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

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

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


AIDS risk; CD4 cell count; viral load; therapy initiation; when to start

© 2004 Lippincott Williams & Wilkins, Inc.