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Life expectancy of recently diagnosed asymptomatic HIV-infected patients approaches that of uninfected individuals
van Sighem, Arda; Gras, Luuka; Reiss, Peterb; Brinkman, Keesc; de Wolf, Franka,don behalf of the ATHENA national observational cohort study
AIDS: June 19th, 2010 - Volume 24 - Issue 10 - p 1527–1535
doi: 10.1097/QAD.0b013e32833a3946
Epidemiology and Social
Article OutlineOutline
Objective: To compare life expectancies between recently diagnosed HIV-infected patients and age and sex-matched uninfected individuals from the general population.
Design: National observational HIV cohort in the Netherlands.
Methods: Four thousand, six hundred and twelve patients diagnosed with HIV between 1998 and 2007 and still antiretroviral therapy-naive as of 24 weeks after diagnosis were selected. Progression to death compared to the age and sex-matched general population was studied with a multivariate hazards model in 4174 (90.5%) patients without AIDS events at 24 weeks. Life expectancy and number of life years lost were calculated using the predicted survival distribution.
Results: During 17 580 person-years of follow-up since 24 weeks after diagnosis [median follow-up 3.3 years, interquartile range (IQR) 1.6–5.8], 118 deaths occurred, yielding a mortality rate of 6.7 [95% confidence interval (CI) 5.5–8.0] per 1000 person-years. Median CD4 cell counts at 24 weeks were 480 cells/μl (IQR 360–650). According to the model, the median number of years lived from age 25 was 52.7 (IQR 44.2–59.3; general population 53.1) for men and 57.8 (49.2–63.7; 58.1) for women without CDC-B event. The number of life years lost varied between 0.4 if diagnosed with HIV at age 25 and 1.4 if diagnosed at age 55; for patients with a CDC-B event this range was 1.8–8.0 years.
Conclusion: The life expectancy of asymptomatic HIV-infected patients who are still treatment-naive and have not experienced a CDC-B or C event at 24 weeks after diagnosis approaches that of non-infected individuals. However, follow-up time is short compared to the expected number of years lived.
aStichting HIV Monitoring, the Netherlands
bCentre for Infection and Immunity Amsterdam (CINIMA), Department of Infectious Diseases, Tropical Medicine and Aids, Academic Medical Centre of the University of Amsterdam, the Netherlands
cOnze Lieve Vrouwe Gasthuis, Amsterdam, the Netherlands
dDepartment of Infectious Disease Epidemiology, Imperial College School of Medicine, London, UK.
Received 4 December, 2009
Revised 16 March, 2010
Accepted 24 March, 2010
Correspondence to Ard van Sighem, Stichting HIV Monitoring, Academic Medical Centre, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands. Tel: +31 205666781; fax: +31 205669189; e-mail: A.I.vanSighem@amc.uva.nl
Study population and mortality
Prognostic model
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Since the introduction of combination antiretroviral therapy (cART) more than a decade ago, HIV-related mortality rates and incidence of AIDS in the Western world have declined substantially [1,2]. As a result, the prognosis of HIV-infected patients has improved, and for successfully treated patients, it has been shown that mortality rates are approaching those of uninfected individuals both in industrialized and in low-income settings [3–10]. Hence, HIV is gradually acquiring the characteristics of a chronic, rather than a lethal, disease.
In the past few years, several prognostic models have been developed to estimate survival probabilities of HIV-infected patients [6,10–12]. Some of these models considered patients at the start of cART, whereas other models took into account the initial response to treatment. For patients who are not yet treated, however, few prognostic models are available [8,13,14]. As current guidelines only recommend treatment when CD4 cell counts drop below 350 cells/μl, the number of diagnosed patients who are not yet treated can be considerable [15]. In the Netherlands, for example, 19% of the in total 11 349 HIV-infected patients in clinical care did not yet start antiretroviral treatment [16].
Estimates of life expectancy are important for policy makers and healthcare providers as they allow them to make predictions on future changes in the size and composition of the HIV-infected population and the need for HIV care [8]. Also, the individual patient is likely to benefit from a favourable prognosis, as it might give easier access to mortgages and life insurances [17]. Here we present a study on mortality rates amongst HIV-infected patients registered in a large national observational cohort who are diagnosed with HIV but not yet treated. A prognostic model is developed to predict survival probabilities and life expectancies of HIV-infected patients after 24 weeks after diagnosis with HIV.
Patients were selected from the ATHENA national observational HIV cohort in the Netherlands [16]. HIV-1-infected individuals were eligible for our study if they were diagnosed with HIV in the period 1998–2007, were 16 years of age or older at the time of diagnosis, and had at least 24 weeks of follow-up. The date of diagnosis was defined as the date of the first positive HIV-1 antibody test or presence of plasma HIV RNA, whichever of the two was earlier. In addition, patients were required to be antiretroviral therapy-naive as of 24 weeks after diagnosis, but were allowed to start treatment after that time. We took into account a period of 24 weeks because there may be some delay between the HIV diagnosis and entering into clinical care in one of the 25 HIV treatment centres in the Netherlands that are allowed to prescribe antiretroviral medication.
CD4 cell counts and viral load at 24 weeks after diagnosis were determined by taking the value closest to 24 weeks measured between 6 and 42 weeks after diagnosis and prior to treatment. CDC stage at 24 weeks was defined as the most serious CDC event recorded up to 30 weeks after diagnosis [18]. AIDS-defining events were classified as severe (non-Hodgkin's lymphoma and progressive multifocal leukoencephalopathy), moderate (cryptococcosis, cerebral toxoplasmosis, AIDS dementia complex, disseminated Mycobacterium avium complex, and rare AIDS events), and mild (all other AIDS events) according to a recently proposed scheme [19].
The endpoint in the study was mortality from all causes. If patients were still alive by 1 June 2008, they were censored at that time or at the date of their last follow-up visit, whichever of the two was earliest. Patients were considered lost to follow-up if their last visit was more than a year before 1 June 2008 and if they did not die. Causes of death were classified according to the Coding of Death in HIV (CoDe) scheme based on clinical data at the time of death (http://www.cphiv.dk/CoDe/tabid/55/Default.aspx).
Time to death from 24 weeks after diagnosis onwards was analysed using a time-dependent multivariate hazards model which has been described earlier [6]. Briefly, the hazard of death in each year after 24 weeks was modelled as the sum of an expected hazard and a function containing patient-specific covariates. The expected hazard depended on the patient's age and sex and was estimated from the annual mortality rate in the general population in the Netherlands averaged over the years 2000–2005.
Covariates considered for inclusion in the prognostic model were sex, region of origin (Western countries, including Western Europe, North America, and Australia; sub-Saharan Africa; other countries), transmission risk group, history of alcohol abuse or drug use, calendar year of diagnosis, age at 24 weeks, either as continuous or as categorical variable (<35 years, 35–50 years, >50 years), disease stage (CDC stage A, B, or C), hepatitis B and C co-infection, CD4 cell counts and viral load at 24 weeks, and all interactions of these covariates with time and log-transformed time since 24 weeks after diagnosis. Treatment with cART after 24 weeks was not explicitly modelled. Thus, the model compares the average effect of clinical care on mortality between HIV-infected individuals and the general population. Patients with an AIDS diagnosis before 24 weeks and patients with a history of drug use were excluded from the prognostic model as their mortality rates were much higher than in the general Dutch population. Only covariates with a P value smaller than 0.2 and interactions with time with a P value below 0.05 in univariate analyses were considered for inclusion in the multivariate model. Covariates were retained in the multivariate model if their exclusion yielded a significantly less accurate model (P < 0.01, likelihood ratio test). For each covariate, the hazard ratio was determined together with the Wald 95% confidence interval (CI).
The standardized mortality ratio (SMR) was defined as the 1-year mortality of HIV-infected patients relative to the age and sex-matched general population. Hence, a patient with SMR r had an r times higher probability of dying within 1 year compared to an uninfected individual of the same age and sex. The median age reached at time of death was computed as the age at which predicted survival probabilities were 0.5. The corresponding interquartile range (IQR) was defined by the ages at which survival probabilities were 0.25 and 0.75. The expected number of remaining life years from a given age was calculated as the median age reached minus the current age. The number of life years lost was computed as the difference between the median age reached by an HIV-infected patient and the age reached by a sex and age-matched non-infected individual.
A 24-week viral load measurement and CD4 cell count was missing for 24.3 and 19.2% of the patients, respectively. A Markov Chain Monte Carlo method was used to impute values in case these variables were missing. This method took into account the dependency of CD4 cell counts and viral load on age, sex, transmission route, region of origin, disease stage, year of diagnosis, and whether the patient died within 1 year after diagnosis. In total, 20 datasets were generated in which values of the missing data were randomly sampled from their predicted distributions [20]. The model analyses were run on each dataset separately and the results were combined with Rubin's rules [21].
Mortality rates were calculated as the number of deaths divided by the total number of person-years of follow-up. The Poisson distribution was used to calculate 95% CIs for rates. All analyses were performed using SAS software (version 9.1.3; SAS Institute, Cary, North Carolina, USA).
In total, 9078 patients were diagnosed between 1998 and 2007 and were 16 years of age or older at the time of diagnosis. Their total follow-up time was 39 946 person-years since diagnosis and, for the 6731 (74.1%) patients who started cART, 27 189 person-years since start of cART. During follow-up, 432 patients died, whereas 88.1 deaths would have been expected in a group of age and sex-matched individuals from the general population in the Netherlands with the same follow-up. Among the 432 patients who died, 99 (23%) were patients who never started cART. The overall mortality rate was 10.8 (95% CI 9.8–11.9) per 1000 person-years. Mortality rates were highest in the first year after diagnosis, being 22 (95% CI 19–25) per 1000 person-years. Thereafter mortality rates declined and were 7.6 (6.2–9.2) during the second and third year after diagnosis, 7.3 (5.9–9.0) during the fourth till sixth year, and 9.1 (6.6–12.2) per 1000 person-years in the seventh till ninth year.
As of 24 weeks after diagnosis, 8717 (96.0%) patients were still in follow-up and 4612 (52.9%) of them were untreated (Table 1). The total follow-up after 24 weeks of the 4612 patients was 17 580 person-years, including 9846 person-years before start of cART and 7734 person-years thereafter. The majority of these patients were men (3710, 80.4%), originated from the Netherlands (2650, 57.4%), and were infected via homosexual (2792, 60.5%) or heterosexual contact (1485, 32.2%). In total, 251 patients had a history of drug use, of whom 76 (30%) were infected via injection drug use. Among the 4612 patients still untreated at 24 weeks, median CD4 cell counts at 24 weeks were high, being 480 (IQR 360–650) cells/μl and did not differ significantly between the three age categories (P = 0.5). There were 185 patients with an AIDS diagnosis before 24 weeks. Severe AIDS events were found in five (2.7%) patients and moderate AIDS events in 28 (15.1%) patients. In 81 (44%) patients, pulmonary or extrapulmonary tuberculosis was the only AIDS event, and 80% of these patients were treated for tuberculosis only at 24 weeks. No difference was observed in the proportion of patients with CDC-B or CDC-C event across age categories (P = 0.2).
During follow-up, 118 (2.6%) of the 4612 patients died, whereas 34.5 deaths would have been expected. Of these 118 patients, 41 died before starting cART (17 expected). The overall mortality rate was 6.7 (95% CI 5.6–8.0) per 1000 person-years (Table 2). The mortality rate was highest for patients with a history of drug use, being 27.3 (18.6–38.8) per 1000 person-years, and for patients who were infected via injection drug use (32.7, 18.7–53.1). Mortality rates were similar for men and women (P = 0.8), and were higher in patients of older age, in patients not originating from Western countries or sub-Saharan Africa, and in patients with lower CD4 cell counts or with a CDC event. For 29 (25%) patients, the cause of death was AIDS-related, whereas 71 (60%) patients died of non-AIDS causes and 18 (15%) patients could not be classified (P = 0.6 for difference in age).
After exclusion of patients with AIDS at 24 weeks and patients with a history of drug use, the population eligible for the prognostic model for progression to death comprised 4174 patients and 76 cases of death. In total, 351 (8.4%) patients were considered lost to follow-up, with a higher proportion of sub-Saharan Africans (21.1%) being lost to follow-up than patients from Western (4.9%) or other countries (10.4%). The only covariates associated with progression to death were age at 24 weeks with hazard ratio 1.07 (95% CI 1.05–1.10) per year older, being in CDC stage B (hazard ratio 4.9, 2.1–11.5), and country of birth (hazard ratio 4.9, 2.3–10.4, other vs. Western countries/sub-Saharan Africa). The patient-specific hazard function did not significantly change over time since 24 weeks after diagnosis. Log-transformed CD4 cell counts at 24 weeks were not statistically significantly related with progression to death (hazard ratio 0.70, 95% CI 0.34–1.44 per unit increase) and were not included in the model.
Figure 1a shows the predicted standardized mortality ratios for HIV-infected men and women without a CDC event as a function of age at 24 weeks after diagnosis. Standardized mortality ratios were highest for men between 30 and 40 years of age and for women around 30 years of age. Overall, women had higher SMRs than men as the general population mortality in women was lower than in men. SMRs were highest in the first year after 24 weeks after diagnosis and decreased thereafter as the expected hazard of death for age and sex-matched non-infected individuals increased. The increase in probability of death within 1 year was approximately log-linear after the age 30 (Fig. 1b). For HIV-infected patients without clinical events, the probability of death within 1 year was similar to that in uninfected individuals who were approximately 9 years older.
The expected median number of remaining life years from age 25 in the general population was 53.1 (IQR 44.9–59.5) years for men and 58.1 (50.1–63.9) for women (Fig. 2a). For HIV-infected patients who were 25 years of age at 24 weeks after diagnosis and originated from Western countries or sub-Saharan Africa, the expected median number of remaining life years was 52.7 (44.2–59.3) for men and 57.8 (49.2–63.7) for women. The number of life years lost increased from 0.4 years at age 25 to 1.3 years at age 55 for HIV-infected men and from 0.4 to 1.4 years for women (Fig. 2b). The number of life years lost was larger for patients with a CDC-B event at 24 weeks: 1.8 years for men and 1.9 years for women 25 years of age and 6.3 and 8.0 years, respectively, for individuals 55 years of age.
The effect of age on the hazard of death was further scrutinized. Including age as a categorical rather than as a continuous variable showed that, compared to patients younger than 35, patients 35–50 years of age had a similar risk of death (hazard ratio 1.38, 0.50–3.76, P = 0.4), whereas patients 50 years of age or older had a higher risk (8.51, 3.59–20.2, P < 0.001). An alternative model including patient's age in each year of follow-up instead of age at 24 weeks fitted the data equally well. In this model, the hazard of death increased more rapidly over time than in the model including age at 24 weeks. As a result, prognosis worsened which was most apparent at younger ages. For HIV-infected men, the number of life years lost was 3.4 at age 25 and then slowly decreased with increasing age at 24 weeks (Fig. 2c). For patients with a CDC-B event, the number of life years lost was as high as 15 at younger ages.
Our analysis of data from the national HIV cohort in the Netherlands shows that the life expectancy of recently diagnosed HIV-infected patients from Western countries or sub-Saharan Africa without clinical symptoms and without a history of drug use is at most a few years less than that of age and sex-matched non-infected individuals. These results are in line with a recent study of a multinational collaboration of HIV cohort studies which showed that the life expectancy of HIV-infected treated individuals who were 20 years of age increased from 36.1 years in 1996–1999 to 49.4 years in 2003–2005 [7]. Increasing life expectancies and decreasing mortality rates from 1996 onwards as a result of treatment with cART have also been reported by other groups [8,22,23].
It should be emphasized that our findings only apply to a very selective group of HIV-infected patients. The aim of our study, however, was not to determine survival probabilities for the entire HIV population in the Netherlands. Instead, we focused on a group of patients with a relatively low-risk lifestyle who were diagnosed early in their infection as reflected by a high CD4 cell count at 24 weeks after diagnosis. These patients are most likely the ones who can benefit from life insurance policies that have recently become available for HIV-infected patients in the Netherlands [17]. These policies exclude drug users and patients with AIDS events.
Several studies compared mortality rates in HIV-infected and uninfected individuals [6,8,23,24]. A Danish study found that the number of years lived from age 25 was more than 35 years [23]. In this study, however, the overall mortality rate between 2000 and 2005 was considerably higher than in our study, 25 compared to 10.8 per 1000 person-years, which could in part be attributed to differences in the population. For example, the proportion of injection drug users was 12% in the Danish study compared to 2% in our analysis. In addition, our analysis only considered patients who were not yet treated at 24 weeks after diagnosis, thus excluding a large proportion of those patients who presented late in their infection.
Patients with an AIDS event at 24 weeks were excluded from the prognostic model, as having been diagnosed with AIDS is associated with a significantly worse outcome [3,10,12]. A large number of patients who were diagnosed with AIDS before 24 weeks were still not treated at 24 weeks. However, a large majority of these patients had AIDS events that were classified as mild [19]. Moreover, we found that almost half of these patients had tuberculosis only and no other AIDS events. The majority of these patients were treated for tuberculosis and probably would commence cART once the tuberculosis cure had finished. When patients with AIDS events were included in the model, hazard ratios did not change significantly. The hazard ratio associated with having an AIDS event was only slightly higher than the hazard ratio associated with CDC-B events (data not shown).
Our analysis showed that CD4 cell counts at 24 weeks after diagnosis were not associated with progression to death once other covariates were taken into account, whereas generally CD4 cell counts are an important prognostic marker [3,7,12]. In our study population, however, CD4 cell counts at 24 weeks were relatively high with more than 75% of the patients having CD4 cell counts exceeding 350 cells/μl. In treated patients, increments in CD4 cell counts above this threshold are only associated with minor changes in mortality rates [25,26].
We found that older age was associated with an increased risk of death even after expected mortality in uninfected individuals was taken into account [8]. In our study population, patients of older age were not further advanced in their HIV infection than patients of younger age as CD4 cell counts and CDC stage at 24 weeks were similar. Also, there were no differences in causes of death between older and younger patients. These findings are compatible with a model in which ageing is accelerated in HIV-infected patients [27]. Our analysis rendered further credibility to this supposition because it was found that probabilities of death within 1 year for HIV-infected patients were roughly the same as those for uninfected individuals 9 years older. It should be noted that this observation does not imply that life expectancies of asymptomatic HIV-infected patients are 9 years shorter than those of uninfected individuals.
Our study has a few limitations. First, the follow-up time of patients included in the study is short compared to the total life expectancy. Patients had at most 10 years of follow-up, whereas the expected number of remaining life years exceeded 50 for patients aged around 25 years. Survival predictions were based on the assumption that the excess mortality of HIV-infected patients did not change with time since 24 weeks after diagnosis. However, a model in which the excess mortality increased with age described the data equally well and predicted a larger number of life years lost. Our model could not exclude scenarios in which mortality rates increased after 10 years of follow-up. Such scenarios might be realistic when long-term treatment with cART leads to drug resistance or drug toxicities. On the contrary, over the calendar period considered in our study, more therapy options have become available and therapy regimens have become easier to adhere to. Hence, treatment failure and subsequent development of resistance have become less likely which eventually yields a better prognosis.
Another limitation is that our analysis does not take into account differences between the HIV-infected population and the general population that might at least partially explain differences in mortality rates. For example, a higher frequency of smoking and alcohol consumption has been observed among HIV-infected patients [6,24,28,29]. Also, the HIV-infected population consists mostly of men having sex with men and patients originating from sub-Saharan Africa or other non-Western countries who are likely to have a lifestyle and socio-economic status different from the general population in the Netherlands. Socio-economic status may also explain why patients from other non-Western countries had a higher risk of death than patients from sub-Saharan Africa or Western countries. For the same reason, however, sub-Saharan Africans would also be expected to have a higher risk of death compared to Western patients. The larger proportion of sub-Saharan Africans being lost to follow-up may be the reason why this was not observed in the data.
Finally, our study might be limited by under-reporting of deaths which would imply that life expectancies reported here are the most optimistic estimates. In our cohort, potential loss to follow-up due to death is ascertained by contacting a patient's general practitioner or relatives when a patient does not show up at a scheduled visit. Thus, we expect that under-reporting does not pose a serious problem in our analysis.
In conclusion, our analysis indicates that the life expectancy of asymptomatic HIV-infected patients without CDC-B or C events who are still not eligible for antiretroviral treatment at 24 weeks after diagnosis approaches that of age and sex-matched noninfected individuals. Predictions depend, however, on continuing success of cART beyond the maximum of 10 years of treatment in our study.
The ATHENA database is supported by a grant from the Dutch Health Minister and was set up and is maintained by the Stichting HIV Monitoring.
A.vS. designed the study, performed the analyses, and wrote the paper. L.G. contributed to the statistical analyses and critically read the manuscript. P.R. and K.B. were involved in the study design and critically read the manuscript. F.dW. was involved in the study design, critically read the manuscript, and was responsible for the data used in the study. All authors read and approved the final manuscript before submission. The physicians and data analysts include (*site coordinating physicians): Professor Dr F. de Wolf (director), Dr D.O. Bezemer, Drs L.A.J. Gras, Drs A.M. Kesselring, Dr A.I. van Sighem, Dr C. Smit, Drs S. Zhang (data analysis group), Drs S. Zaheri (data collection), Stichting HIV Monitoring, Amsterdam; Dr J.M. Prins*, Drs J.C. Bos, Dr J.K.M. Eeftinck-Schattenkerk, Dr S.E. Geerlings, Dr M.H. Godfried, Professor Dr J.M.A. Lange, Dr J.T.M. van der Meer, Dr F.J.B. Nellen, Dr T. van der Poll, Professor Dr P. Reiss, Drs S.U.C. Sankatsing, Drs M. van der Valk, Drs J.N. Vermeulen, Drs S.M.E. Vrouenraets, Dr M. van Vugt, Dr F.W.M.N. Wit, Academisch Medisch Centrum bij de Universiteit van Amsterdam, Amsterdam; Dr G. Schreij*, Dr S. van der Geest, Dr A. Oude Lashof, Dr S. Lowe, Dr A. Verbon, Academisch Ziekenhuis Maastricht; Maastricht; Dr B. Bravenboer*, Drs M.J.H. Pronk, Catharina Ziekenhuis, Eindhoven; Professor Dr T.W. Kuijpers, Drs D. Pajkrt, Dr H.J. Scherpbier, Emma Kinderziekenhuis, AMC, Amsterdam; Dr M.E. van der Ende*, Drs H. Bax, Drs M. van der Feltz, Dr L.B.S. Gelinck, Dr J.L. Nouwen, Dr B.J.A. Rijnders, Dr E.D. de Ruiter, Dr L. Slobbe, Drs C.A.M. Schurink, Dr T.E.M.S. de Vries-Sluijs, Erasmus MC, Rotterdam; Dr G. Driessen, Dr N.G. Hartwig, Erasmus MC – Sophia, Rotterdam; Dr J. Branger, Drs M.H. Hoogewerf, Flevoziekenhuis, Almere; Dr R.H. Kauffmann*, Dr E.F. Schippers, Haga Ziekenhuis, locatie Leyenburg, Den Haag; Dr P.H.P. Groeneveld*, Dr M.A. Alleman, Isala Klinieken, Zwolle; Professor Dr R.W. ten Kate*, Dr R. Soetekouw, Kennemer Gasthuis, Haarlem; Dr F.P. Kroon*, Dr S.M. Arend, Drs M.G.J. de Boer, Professor Dr P.J. van den Broek, Professor Dr J.T. van Dissel, Drs C. van Nieuwkoop, Leids Universitair Medisch Centrum, Leiden; Dr J.G. den Hollander*, Maasstadziekenhuis, locatie Clara, Rotterdam; Dr W. Bronsveld*, Dr K. Pogany, Medisch Centrum Alkmaar, Alkmaar; Dr R. Vriesendorp*, Dr E.M.S. Leyten, Medisch Centrum Haaglanden, locatie Westeinde, Den Haag; Dr D. van Houte*, Dr M.B. Polée, Dr M.G.A. van Vonderen, Medisch Centrum Leeuwarden, Leeuwarden; Dr C.H.H. ten Napel*, Dr G.J. Kootstra, Medisch Spectrum Twente, Enschede; Professor Dr K. Brinkman*, Drs G.E.L. van den Berk, Dr W.L. Blok, Dr P.H.J. Frissen, Drs W.E.M. Schouten, Onze Lieve Vrouwe Gasthuis, Amsterdam; Dr A. van Eeden*, Dr D.W.M. Verhagen, St. Medisch Centrum Jan van Goyen, Amsterdam; Dr J.W. Mulder*, Dr E.C.M. van Gorp, Dr J. Wagenaar, Slotervaart Ziekenhuis, Amsterdam; Dr J.R. Juttmann*, Dr M.E.E. van Kasteren, St. Elisabeth Ziekenhuis, Tilburg; Dr J. Veenstra*, Dr K.D. Lettinga. St. Lucas Andreas Ziekenhuis, Amsterdam; Dr P.P. Koopmans*, Drs A.M. Brouwer, Dr A.S.M. Dofferhoff, Dr M. van der Flier, Professor Dr R. de Groot, Drs H.J.M. ter Hofstede, Dr M. Keuter, Dr A.J.A.M. van der Ven, Universitair Medisch Centrum St. Radboud, Nijmegen; Dr H.G. Sprenger*, Dr S. van Assen, Dr C.J. Stek, Universitair Medisch Centrum Groningen, Groningen; Dr R. Doedens, Dr E.H. Scholvinck, Universitair Medisch Centrum Groningen, Beatrix Kliniek, Groningen; Professor Dr I.M. Hoepelman*, Professor Dr M.J.M. Bonten, Dr P.M. Ellerbroek, Drs E. Hoornenborg, Drs C.A.J.J. Jaspers, Drs L.J. Maarschalk-Ellerbroek, Dr T. Mudrikova, Dr J.J. Oosterheert, Dr E.J.G. Peters, Dr M.M.E. Schneider, Drs M.W.M. Wassenberg, Dr S. Weijer, Universitair Medisch Centrum Utrecht, Utrecht; Dr S.P.M. Geelen, Dr T.F.W. Wolfs, Wilhelmina Kinderziekenhuis, UMC Utrecht, Utrecht; Professor Dr S.A. Danner*, Dr M.A. van Agtmael, Drs W.F.W. Bierman, Drs F.A.P. Claessen, Drs M.E. Hillebrand, Drs E.V. de Jong, Drs. W. Kortmann, Dr R.M. Perenboom, Drs E.A. bij de Vaate, VU Medisch Centrum, Amsterdam; Dr C. Richter*, Drs J. van der Berg, Dr E.H. Gisolf, Ziekenhuis Rijnstate, Arnhem; Dr A.A. Tanis*, Ziekenhuis Walcheren, Vlissingen; Dr A.J. Duits, Dr A. Durand, Dr F. Muskiet, Dr R. Voigt, Dr K. Winkel, St. Elisabeth Hospitaal/Stichting Rode Kruis Bloedbank, Willemstad, Curaçao.
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cohort study; death; HIV; life expectancy; standardized mortality ratio; statistical model
© 2010 Lippincott Williams & Wilkins, Inc.
AIDS24(10):1527-1535, June 19th, 2010.
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