Epidemiology & Social
Low mortality in HIV-infected patients starting highly active antiretroviral therapy: a comparison with the general population
Jensen-Fangel, Sørena; Pedersen, Larsb; Pedersen, Courtc; Larsen, Carsten Sd; Tauris, Pallee; Møller, Axelf; Sørensen, Henrik Tb; Obel, Nielsc
From the aDepartment of Infectious Diseases and bDepartment of Clinical Epidemiology, Aarhus University Hospital, Aarhus, the cDepartment of Infectious Diseases, Odense University Hospital, Odense, the dDepartment of Infectious Diseases, Aalborg Hospital, Aalborg, the eDepartment of Infectious Diseases, Herning Hospital, Herning and the fDepartment of Infectious Diseases, Kolding Hospital, Kolding, Denmark.
Correspondence to Søren Jensen-Fangel, Department of Infectious Diseases, Aarhus University Hospital, Skejby Hospital, DK-8200 Aarhus N, Denmark.
Tel: +45 89498491; fax: +45 89498490; e-mail: email@example.com
Received: 10 April 2003; revised: 5 June 2003; accepted: 30 June 2003.
Objectives: To assess the mortality in a cohort of HIV-infected patients starting highly active antiretroviral therapy (HAART) compared to the mortality of the general population, focusing on the influence of the CD4 cell count at the time of starting HAART.
Methods: Patients in the HIV Cohort Study in Western Denmark starting HAART before 1 January 2002 were identified. For each patient, 100 population controls matched on age and gender were extracted from the Danish Civil Registration System. Mortality rates were compared between the two cohorts overall, and in four groups defined by baseline CD4 cell counts.
Results: A total of 647 HIV-infected patients and 64 700 population controls were included, accounting for 53 and 815 deaths during follow-up. In the HIV group, mortality rates were 70.0 per 1000 person-years at risk in the lowest CD4 cell group (< 50 × 106 cells/l), and 3.2 in the highest (≥ 200 × 106 cells/l). Compared with population controls, mortality rate ratios declined with increasing CD4 cell counts, being 15.3 [95% confidence interval (CI), 9.8–23.8], 8.6 (95% CI, 4.3–16.8), 5.9 (95% CI, 3.0–11.4), and 3.6 (95% CI, 2.0–6.5) in the groups with CD4 cell count < 50, 50–99, 100–199, and ≥ 200 × 106 cells/l.
Conclusion: In comparison with the general population, HIV-infected patients starting HAART with a CD4 cell count above 200 × 106 cells/l had low mortality rates that were comparable with the rates found in other chronic medical diseases. The mortality rates increased considerably when treatment was started at lower baseline CD4 cell counts.
The prognosis of HIV infection has improved considerably after the introduction of highly active antiretroviral therapy (HAART) [1–5]. However, several observational studies on HIV patients starting HAART have shown an association between clinical progression to AIDS or death, and the CD4 cell count and plasma HIV-RNA levels at baseline [6,7]. Recently a large pooled analysis on 12 574 treatment-naive patients starting HAART in 13 different cohorts, found that the CD4 cell count at the time of the initiation of therapy was the dominant prognostic factor for disease progression .
Most information on mortality related to HIV infection has been derived from studies on risk factors for disease progression, or from comparisons with historical cohorts, not taking into account the mortality of the general population. The aim of the present study was to compare the mortality in HIV-infected patients starting HAART with the mortality of population controls, with focus on the impact of the CD4 cell count level at the time of starting treatment.
Subjects and methods
Demographics of HIV infection and health care in Denmark
The population of Denmark as of January 2002 was 5 368 000, of which 55% lived in Western Denmark defined as Funen and Jutland. HIV infection has a relatively low prevalence in Denmark: an estimated 3800 people were living with HIV infection in 2001, yielding an adult HIV prevalence of 0.15%.
Treatment for HIV infection in Denmark is restricted to specialized public centres, of which five are localised in Western Denmark. The Danish health care system provides free medical care and treatment for all residents, including antiretroviral treatment for HIV- infected individuals. Patient care and treatment strategies are similar throughout the country, with routine visits planned every 3 months for patients receiving antiretroviral therapy, and every 3 to 6 months during earlier stages of the disease. The main criteria for institution of antiretroviral therapy are: CD4 cell count < 300 × 106 cells/l, plasma HIV-RNA > 100 000 copies/ml, or HIV-related disease.
Population controls were retrieved by use of the Danish Civil Registration System, a nation-wide registry on all persons residing in Denmark after 1 April 1968. All residents in Denmark are assigned a unique 10-digit person identification number at birth (civil registration number, or CPR number), which is stored in the Danish Civil Registration System along with information on date of birth, residency, date of immigration or emigration, and date of death. The 10-digit CPR number is made up of the birth date, and a four-digit code that contains information on the gender of the individual.
A random sample from the Danish Civil Registration System in western Denmark was provided as population controls. For each HIV-infected patient (index-person), the data on 100 residents from this sample were retrieved. The aim with selecting 100 population controls per index-person was to give an approximation to the general population. The population controls were randomly chosen from the sample, and were matched on gender and age (month and year of birth). All population controls were alive at the date the index-person started HAART.
The HIV Cohort Study in Western Denmark is a prospective, population-based cohort study of all HIV-infected individuals treated by one of the HIV centres in Western Denmark. Study methods and baseline cohort characteristics have been described in detail . Briefly, all HIV-infected patients attending one of the centres after 1 January 1995 are included in the study. Up to January 2002, 971 patients were enrolled. The study is ongoing, with newly diagnosed HIV-infected individuals, and individuals with HIV infection who move to the region being continuously enrolled. Use of the CPR number, enables the centres to avoid multiple registrations on single patients, and to track whether the patients have died or moved outside the region when they are lost to follow-up.
Data on the patients were collected at baseline (i.e. the first visit to the centre), and every 12 months thereafter. Variables collected at baseline included date of birth, gender, mode of infection, race, ethnicity (if not Danish: country of origin and date of arrival to Denmark), date of first positive HIV-1 test, and diagnosis of AIDS-defining diseases (using the 1993 clinical definition of AIDS from the US Centers for Disease Control and Prevention ). Variables collected at follow-up visits included date and cause of death, date of initiation of HAART, date of starting and stopping an antiretroviral drug, indication for changing HAART regimen, prophylactic treatment against opportunistic infections, and laboratory values (CD4 cell count, plasma HIV-RNA, cholesterol (total) and triglycerides).
All HIV patients from the cohort who had started HAART before 1 January 2002 were eligible for this analysis. HAART was defined as a regimen consisting of at least three antiretroviral compounds. In the analysis of baseline factors, CD4 cell counts and HIV-RNA values were defined as the latest values measured within the 6 months prior to start of HAART. A previous AIDS-defining event was defined as having experienced an event prior to, or within 1 month of starting HAART. In the analyses of mortality, the outcome was death from any cause during the follow-up period. Causes of death were grouped into three categories: HIV-related, unknown, and other causes.
Comparison with the general population
In the analysis of relative mortality, we used a person-years analysis to estimate the rate of death during the follow-up period. Person-years at risk (PYR) were accrued from the time of starting HAART (or for population controls from the date the index-person started HAART) to the date of death, or last follow-up. Event-free subjects were right censored as of 1 January 2002, or at the date of moving outside the country. For both groups a mortality rate (MR) per 1000 PYR was estimated, and for comparisons between the groups we estimated the ratio between the two MRs, the mortality rate ratio (MRR). In all analyses, we used an approach, in which the patients continued to contribute to the PYR despite treatment interruptions or terminations.
To evaluate the influence of the baseline CD4 cell count on mortality, the cohort of HIV-infected patients and the respective population controls were grouped into four arbitrary baseline CD4 groups: CD4 cell count < 50, 50–99, 100–199, and ≥ 200 × 106 cells/l. The baseline CD4 cell count was defined as the latest CD4 cell count measured before start of HAART; however, individuals who had no CD4 cell count measured within 6 months of starting HAART were excluded from the analyses of the CD4 cell groups. For each CD4 cell group the MRR was estimated by comparing the MR of the HIV-infected patients, to the MR of the respective population controls. Similar, MRs were estimated for two arbitrary baseline plasma HIV-RNA levels (< 100 000 and ≥ 100 000 copies/ml). The baseline CD4 cell count and HIV-RNA groups used in this study, were chosen as they represent levels that have been demonstrated to be associated with survival in HIV patients starting HAART . In the analyses of the MRs in the various groups based on baseline CD4 cell count or plasma HIV-RNA, the HIV-infected patients and respective population controls, remained in the particular group, ignoring subsequent changes in CD4 cell count or plasma HIV-RNA during treatment.
In order to restrict the analysis to HIV-infected patients not reporting intravenous drug use (IDU) as primary mode of infection, we repeated the analysis after excluding these patients. In this analysis we accordingly excluded the respective population controls.
Risk factors within the HIV cohort
To summarize the risk of dying during follow-up in the cohort of HIV-infected patients, we used Kaplan–Meier analysis to construct survival curves. In these time-to-event analyses, the Cox proportional hazards model was used to study the association between patient baseline factors and mortality, estimating the relative risk of death, and associated 95% confidence intervals. The following potential confounders were considered for inclusion in the final model: gender, age (more than 40 years versus less than 40 years), history of IDU, year of HIV diagnosis, previous AIDS-defining event, CD4 cell count at the start of HAART (< 50, 50–99, 100–199, and ≥ 200 × 106 cells/l), viral load at the start of HAART (< 100 000 versus ≥ 100 000 copies/ml), previous antiretroviral treatment, and treatment with saquinavir (SQV) hard-gel capsule in the initial HAART regimen. A variable was included in the final model if it changed the estimated exposure effect by at least 10% when considered as the sole covariate . Data analysis was performed using SPSS version 10.0 (Norusis; SPSS Inc., Chicago, Illinois, USA) statistical software.
In total, 647 HIV-infected patients starting HAART before 1 January 2002, were included in the analyses, yielding 1970 PYR. The median follow up time was 3.5 years. Table 1 shows the demographic and baseline characteristics at the time of starting HAART in all patients, and in the patients in the four CD4 cell groups. Overall, males constituted 66.8% of the patients, and the median age at starting HAART was 37.3 years. More than half of the patients (52.6%) were infected through heterosexual contact. The median CD4 cell count was 198 × 106 cells/l, and median plasma HIV-RNA was 4.8 log10 copies/ml. Of all subjects, 44.4 % were antiretroviral treatment experienced prior to starting HAART. In the first-line HAART regimen, 64.9% received a single protease inhibitor (PI), 11.9% a dual PI, 11.6% an non-nucleoside reverse transcriptase inhibitor (NNRTI) (without a PI), 5.1% an NNRTI with a PI, and 6.5% a triple nucleoside reverse transcriptase inhibitor (NRTI) regimen. A total of 163 patients (25.2%) started a SQV hard gel-based regimen.
A total of 25 patients had no CD4 cell count measured within 6 months of starting HAART, and were excluded from the analyses of the CD4 cell groups. Among the patients starting HAART, baseline CD4 cell count was: < 50 × 106 cells/l in 118 patients (19%), 50–99 × 106 cells/l in 69 (11%), 100–199 × 106 cells/l in 134 (22%), and ≥ 200 × 106 cells/l in 301 (48%). When considering the baseline characteristics of the patients in the four CD4 cell-based groups, it appeared that median plasma HIV-RNA was highest in the group with CD4 cell count < 50 × 106 cells/l, with 5.3 log10 copies/ml, declining through the groups to 4.5 log10 copies/ml in the group with CD4 cell count ≥ 200 × 106 cells/l. Similarly, the proportion having an AIDS-defining event prior to, or at the time of starting HAART, declined from 50.8% in the lowest CD4 cell group, to 8.6% in the highest CD4 cell group. The four CD4 cell groups did not differ substantially with regard to gender, age, mode of infection, or racial background.
A total of 64 700 subjects were included as controls and followed for a total of 215 580 person-years, starting observation time at the date the index person (i.e. the HIV-infected person) started HAART.
During follow-up there were 53 deaths in the HIV-infected patients and 815 in the population cohort. The MR was 26.9 deaths per 1000 PYR among HIV-infected patients, and 3.8 deaths per 1000 PYR in the population controls. Comparing HIV-infected patients with population controls, we found a MRR of 7.1 [95% confidence interval (CI), 5.4–9.4]. Figure 1 shows the Kaplan–Meier plots for death after starting HAART (for HIV-infected patients), or the index date (for population controls). Overall, 3.9% of HIV-infected patients died within 52 weeks (95% CI, 2.4–5.5) and 0.32% (95% CI, 0.28–0.36) among population controls. At 104 weeks the estimated proportions were 5.7% (95% CI, 3.8–7.6) and 0.67% (95% CI, 0.61–0.72), respectively.
The mortality in the HIV-infected patients varied according to CD4 cell counts. Figure 2 shows the cumulative risk of death in the HIV-infected patients and the respective population controls, when dividing the cohorts into four groups according to baseline CD4 cell count. The MRs in the CD4 cell groups of HIV-infected patients differed dramatically, being 70.0 per 1000 PYR in the lowest CD4 cell group (< 50 × 106 cells/l), 45.7 in the group 51–99 × 106 cells/l, 20.8 in the group 100–199 × 106 cells/l, and 3.2 for those with the highest CD4 cell counts (≥ 200 × 106 cells/l). The Kaplan–Meier estimates of the cumulative mortality proportions at 52 weeks for the four groups were: 9.7, 8.0, 4.0 and 0.7% (from CD4 cell count < 50 to ≥ 200 × 106 cells/l).
Table 2 presents the MRRs comparing the MRs between HIV-infected patients with population controls. There was an excess risk of death in the HIV-infected patients in all four CD4 groups. However, the MRRs declined with increasing CD4 cell counts, from 15.3 (95% CI, 9.8–23.8) among those with the lowest counts to 3.6 (95% CI, 2.0–6.5) among those with the highest.
To minimize the impact of excess mortality in HIV patients reporting IDU as mode of transmission, we repeated the analysis after excluding these patients, 36 in all (five deaths). Compared to the non-resticted analyses this analysis showed modestly lower MRs and MRRs. Hence, excess mortality expressed by the MRR were 14.9 (95% CI, 9.5–23.4), 7.7 (95% CI, 3.8–15.8), 5.4 (95% CI, 2.6–10.9), and 3.0 (95% CI, 1.5–5.8) in the four strata (from CD4 cell count < 50 to ≥ 200 × 106 cells/l).
When focusing on the two groups based on plasma HIV-RNA at the time of starting HAART, the MRs differed considerably between the two groups: 34.5 and 13.7 deaths per 1000 PYR in the patients with ≥ 100 000 and < 100 000 copies/ml, respectively. Accordingly the MRRs also differed: 10.4 (95% CI, 6.8–15.9) and 3.6 (95% CI, 2.1–5.9) respectively (Table 2). Having a history of an AIDS-defining event at baseline also resulted in a high MR of 56.8 per 1000 PYR, as opposed to a MR of 19.2 per 1000 PYR in patients not having experienced an AIDS-defining event. The resulting MRRs were 14.1 (95% CI, 9.1–21.7) for patients with, and 5.2 (95% CI, 3.6–11.8) for patients without AIDS at baseline.
The crude and adjusted analyses of the baseline factors associated with the time to death in the cohort of HIV-infected patients, are presented in Table 3. In the multivariate analysis, only CD4 cell counts of less than 100 × 106 cells/l, and age ≥ 40 years at the time of starting HAART, were significantly associated with death. Patients with a CD4 cell count of less than 50 × 106 cells/l were 3.55 (95% CI, 1.46–8.65) times more likely to die during follow-up than patients with a CD4 cell count of 200 × 106 cells/l or more. Patients more than 40 years old were 3.59 (95% CI, 1.81–7.12) times more likely to die than patients younger than 40 years.
The causes of death for the HIV patients were HIV-related in 62.3% of the cases, other causes in 26.4%, and unknown in 11.3%. The distribution in causes of death did not change considerable between the CD4 strata; In the four strata (from CD4 cell count < 50 to ≥ 200), 59.1, 66.7%, 55.6, and 63.6 % of deaths were reported to be HIV-related, respectively.
As our analyses were restricted to patients starting HAART, some patients would have died before initiating therapy. In our cohort we found a total of 16 patients who died in the HAART-era after fulfilling positive criteria for treatment (CD4+ cell count < 300 × 106 cells/l, plasma HIV-RNA > 100 000 copies/ml, or HIV-related disease), but without having started HAART. Of these, the majority (nine patients) died before 1 January 1 1998, that is in the early HAART period.
In this population-based cohort study of HIV-infected patients, we found that those who started HAART had higher mortality rates than population controls, and that the excess mortality was highly dependent on the CD4 cell count at the time of starting HAART.
Our findings suggest that HIV-infected patients starting HAART have relatively low rates of mortality, especially when treatment was initiated with baseline CD4 cell counts of at least 200 × 106 cells/l. For these patients mortality was 3.6 times higher than for the population controls, decreasing to 3.0 times higher after restriction of the analysis to HIV patients not reporting intravenous drug use as the primary mode of transmission. Most interestingly, we found the excess mortality in patients with a baseline CD4 cell count of at least 200 × 106 cells/l to be similar to the excess mortality reported from various cohort studies on mortality in patients with insulin-treated diabetes [12–18]. These studies all find an excess mortality in the diabetic patients, but with large variations (standardized mortality ratio, (SMR) ranging from 1.9 to 7.4), probably because of considerable differences in design, location, study populations, gender and age. In a large British cohort study on more than 23 000 insulin-treated young diabetic patients (diagnosed under the age of 30 years), Laing et al. found an excess mortality with an SMR of 4.0 for females, and 2.7 for males overall, but reaching a peak of 5.7 in females aged 20–29 years, and of 4.0 in males aged 40–49 years .
In the interpretation of the results in our study, it is important to bear in mind that the excess mortality estimates will underestimate the efficacy of the HAART regimens. As we used an approach, in which the patients continued to contribute to the person-years at risk despite treatment interruptions or terminations, some of the patients starting HAART will contribute to the number of deaths even though they are no longer treated with antiretrovirals. A similar approach was used when defining the groups based on CD4 cell count at the start of HAART. Hence, of the patients dying in the highest CD4 cell group (≥ 200 × 106 cells/l), four patients (36%) had a CD4 cell count of less than 200 × 106 cells/l as the latest CD4 cell count measured (data not shown).
Most previous studies on mortality in HIV-infected patients have focused on the decline in mortality rates after introduction of HAART in comparison with mortality rates in historical cohorts of untreated patients [1,3–5,19–21], or on identifying risk factors for clinical progression with death as endpoint [6,7,22–24]. To our knowledge, only one previous study has focused on the mortality in comparison with the general population, but this investigation did not take into account baseline CD4 counts. Among 1157 HIV-1 infected patients starting HAART, Lewden et al. found that mortality was 7.8 times higher in HIV patients starting HAART than in the general population . Our study was performed using a population-based cohort of HIV-infected patients and a general population under good general medical surveillance. Through linkage with the personal identification number to the well-maintained Danish mortality files, we were able to follow a large number of population controls over time, greatly enhancing the comparisons under study . The opportunity of selecting a large number of well-described population controls from the region covered by the HIV cohort, led us to estimate mortality by calculating a MRR in stead of using standardizated rates based on the indirect method. Along with the population-based design of the cohort of HIV-infected patients, the strength of our study is the setting: a uniformly organized health care system with free access to medical care, and similar patient care and treatment strategies throughout the region. Furthermore the patients had a long follow-up period of 3.5 years (median).
Nonetheless, our study has several potential weaknesses. Above all, our analyses comparing mortality rates between HIV-infected patients and population controls are crude analyses apart from the matching on gender and age. The population controls are derived from a central register, which does not leave us any information on a number of potential confounding factors. Apart from the HIV-related morbidity, the HIV-infected individuals are likely to have a higher co-morbidity than the general population, ultimately leading to a higher mortality from diseases that are not related directly to the HIV infection. Hence, it is well-known that the prevalence of tobacco smoking and alcohol intake is higher among HIV-infected individuals than in the general population [27–29]. Other confounding factors that most likely are unevenly distributed between the two groups are: intravenous drug use, chronic hepatitis C infection, depression, and low socio-economic status [30,31]. In an attempt to minimize this bias, we performed additional analyses restricted to patients not being infected through intravenous drug use. The use of HAART has also been documented to induce several long-term toxicities, including metabolic changes with hyperlipidemia, and insulin resistance with impaired glucose tolerance and diabetes [32–34]. Whether these toxicities will evolve into a higher incidence of death from cardiovascular disease, for example, remains to be clarified.
When focusing on the causes of death, most were related to the HIV infection, even in the patients starting HAART with a CD4 cell count ≥ 200 × 106 cells/l. However, several of these deaths occurred at lower CD4 cell counts as a result of treatment failure or non-adherence to therapy. Mocroft et al. found that the proportion of deaths due to HIV-related causes decreased during the later years . Our study was too small to observe a similar effect.
Our study furthermore suggests the importance of being diagnosed, and starting HAART before reaching the level of severe immunosuppression. Various cohort studies have consistently shown the CD4 cell count to be the most important prognostic factor for clinical disease progression in patients starting HAART, regardless of whether the CD4 count used in the analysis was measured at baseline [7,8,24], at a certain time after starting treatment , or was the most recent CD4 cell count measured . In accordance with these findings, we found a low CD4 cell count (< 50 × 106 cells/l) at the time of starting HAART to be associated with a high mortality, 15.3 times higher than the population controls, and an increased risk of death in the Cox regression analysis with an adjusted relative risk of 3.55 compared to the group of patients with a high baseline CD4 cell count (at least 200 × 106 cells/l).
As the present study is limited to HIV patients starting HAART, it is a conservative estimate of the mortality in HIV-infected patients in the HAART-era, as some patients might die before starting HAART. However, this number was small in our setting with free access to antiretroviral therapy, with the majority dying in the early HAART-era (before 1 January 1998).
In summary, the mortality in HIV-infected patients starting HAART compared with the general population, is highly dependent on the CD4 cell count at the time of starting therapy. Mortality rate ratios declined from 15.3 in patients with CD4 cell count of less than 50 × 106 cells/l, to 3.6 in patients with CD4 cell count of at least 200 × 106 cells/l, the latter being similar to the excess mortality reported in young insulin-treated diabetic patients. These findings suggest the importance of starting HAART before attaining the level of severe impairment of the immune system, as measured by the CD4 cell count.
Sponsorship: This study received financial support from The Danish AIDS Foundation, Aarhus University, and the Western Danish Research Forum for Health Sciences. The funding sources had no involvement in the study design, in the collection, analysis, and interpretation of data, in the writing of the report, or in the decision to submit the paper for publication.
1. Mocroft A, Brettle R, Kirk O, Blaxhult A, Parkin JM, Antunes F, et al. Changes in the cause of death among HIV positive subjects across Europe: results from the EuroSIDA study. AIDS 2002, 16:1663–1671.
2. Palella FJ Jr, Delaney KM, Moorman AC, Loveless MO, Fuhrer J, Satten GA, et al. Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. HIV Outpatient Study Investigators. N Engl J Med 1998, 338:853–860.
3. Mocroft A, Vella S, Benfield TL, Chiesi A, Miller V, Gargalianos P, et al. Changing patterns of mortality across Europe in patients infected with HIV-1. EuroSIDA Study Group. Lancet 1998, 352:1725–1730.
4. Valdez H, Chowdhry TK, Asaad R, Woolley IJ, Davis T, Davidson R, et al. Changing spectrum of mortality due to human immunodeficiency virus: analysis of 260 deaths during 1995–1999. Clin Infect Dis 2001, 32:1487–1493.
5. Louie JK, Hsu LC, Osmond DH, Katz MH, Schwarcz SK. Trends in causes of death among persons with acquired immunodeficiency syndrome in the era of highly active antiretroviral therapy, San Francisco, 1994–1998. J Infect Dis 2002, 186:1023–1027.
6. Lewden C, Raffi F, Cuzin L, Cailleton V, Vilde JL, Chene G, et al. Factors associated with mortality in human immunodeficiency virus type 1-infected adults initiating protease inhibitor-containing therapy: role of education level and of early transaminase level elevation (APROCO-ANRS EP11 study). The Antiproteases Cohorte Agence Nationale de Recherches sur le SIDA EP 11 study. J Infect Dis 2002, 186:710–714.
7. Hogg RS, Yip B, Chan KJ, Wood E, Craib KJ, O'Shaughnessy MV, et al. Rates of disease progression by baseline CD4 cell count and viral load after initiating triple-drug therapy. JAMA 2001, 286:2568–2577.
8. Egger M, May M, Chene G, Phillips AN, Ledergerber B, Dabis F, et al. Prognosis of HIV-1-infected patients starting highly active antiretroviral therapy: a collaborative analysis of prospective studies. Lancet 2002, 360:119–129.
9. Jensen-Fangel S, Pedersen C, Larsen CS, Tauris P, Moller A, Obel N. Changing demographics in an HIV-infected population: results from an observational cohort study in Western Denmark. Scand J Infect Dis 2001, 33:765–770.
10. Anon. 1993 revised classification system for HIV infection and expanded surveillance case definition for AIDS among adolescents and adults. MMWR Recomm Rep 1992, 41:1–19.
11. Greenland S. Modeling and variable selection in epidemiologic analysis. Am J Public Health 1989, 79:340–349.
12. Laing SP, Swerdlow AJ, Slater SD, Botha JL, Burden AC, Waugh NR, et al. The British Diabetic Association Cohort Study, I: cause-specific mortality in patients with insulin-treated diabetes mellitus. Diabet Med 1999, 16,459–465.
13. Morrish NJ, Wang SL, Stevens LK, Fuller JH, Keen H. Mortality and causes of death in the WHO Multinational Study of Vascular Disease in Diabetes. Diabetologia 2001, 44 (suppl 2):S14–S21.
14. Wang SL, Head J, Stevens L, Fuller JH. Excess mortality and its relation to hypertension and proteinuria in diabetic patients. The world health organization multinational study of vascular disease in diabetes. Diabetes Care 1996, 19:305–312.
15. Weiderpass E, Gridley G, Nyren O, Pennello G, Landstrom AS, Ekbom A. Cause-specific mortality in a cohort of patients with diabetes mellitus: a population-based study in Sweden. J Clin Epidemiol 2001, 54:802–809.
16. Wibell L, Nystrom L, Ostman J, Arnqvist H, Blohme G, Lithner F, et al. Increased mortality in diabetes during the first 10 years of the disease. A population-based study (DISS) in Swedish adults 15–34 years old at diagnosis. J Intern Med 2001, 249:263–270.
17. Nishimura R, LaPorte RE, Dorman JS, Tajima N, Becker D, Orchard TJ. Mortality trends in type 1 diabetes. The Allegheny County (Pennsylvania) Registry 1965–1999. Diabetes Care 2001, 24:823–827.
18. Saydah SH, Eberhardt MS, Loria CM, Brancati FL. Age and the burden of death attributable to diabetes in the United States. Am J Epidemiol 2002, 156:714–719.
19. Hogg RS, Yip B, Kully C, Craib KJ, O'Shaughnessy MV, Schechter MT, et al. Improved survival among HIV-infected patients after initiation of triple-drug antiretroviral regimens. CMAJ 1999, 160:659–665.
20. Fordyce EJ, Singh TP, Nash D, Gallagher B, Forlenza S. Survival rates in NYC in the era of combination ART. J Acquir Immune Defic Syndr 2002, 30:111–118.
21. Lee LM, Karon JM, Selik R, Neal JJ, Fleming PL. Survival after AIDS diagnosis in adolescents and adults during the treatment era, United States, 1984–1997. JAMA 2001, 285:1308–1315.
22. Miller V, Phillips AN, Clotet B, Mocroft A, Ledergerber B, Kirk O, et al. Association of virus load, CD4 cell count, and treatment with clinical progression in human immunodeficiency virus-infected patients with very low CD4 cell counts. J Infect Dis 2002, 186:189–197.
23. Wood E, Montaner JS, Chan K, Tyndall MW, Schechter MT, Bangsberg D, et al. Socioeconomic status, access to triple therapy, and survival from HIV-disease since 1996. AIDS 2002, 16:2065–2072.
24. Anastos K, Barron Y, Miotti P, Weiser B, Young M, Hessol N, et al. Risk of progression to AIDS and death in women infected with HIV-1 initiating highly active antiretroviral treatment at different stages of disease. Arch Intern Med 2002, 162: 1973–1980.
25. Lewden C, Raffi F, Chene G, Sobel A, Leport C. Mortality in a cohort of HIV-infected adults started on a protease inhibitor-containing therapy: standardization to the general population. J Acquir Immune Defic Syndr 2001, 26:480–482.
26. Frank L. Epidemiology. When an entire country is a cohort. Science 2000, 287:2398–2399.
27. Niaura R, Shadel WG, Morrow K, Tashima K, Flanigan T, Abrams DB. Human immunodeficiency virus infection, AIDS, and smoking cessation: the time is now. Clin Infect Dis 2000, 31: 808–812.
28. Mamary EM, Bahrs D, Martinez S. Cigarette smoking and the desire to quit among individuals living with HIV. AIDS Patient Care STDS 2002, 16:39–42.
29. Galvan FH, Bing EG, Fleishman JA, London AS, Caetano R, Burnam MA, et al. The prevalence of alcohol consumption and heavy drinking among people with HIV in the United States: results from the HIV Cost and Services Utilization Study. J Stud Alcohol 2002, 63:179–186.
30. Greub G, Ledergerber B, Battegay M, Grob P, Perrin L, Furrer H, et al. Clinical progression, survival, and immune recovery during antiretroviral therapy in patients with HIV-1 and hepatitis C virus coinfection: the Swiss HIV Cohort Study. Lancet 2000, 356:1800–1805.
31. Mohsen AH, Easterbrook P, Taylor CB, Norris S. Hepatitis C and HIV-1 coinfection. Gut 2002, 51:601–608.
32. Carr A, Samaras K, Thorisdottir A, Kaufmann GR, Chisholm DJ, Cooper DA. Diagnosis, prediction, and natural course of HIV-1 protease-inhibitor-associated lipodystrophy, hyperlipidaemia, and diabetes mellitus: a cohort study. Lancet 1999, 353: 2093–2099.
33. Graham NM. Metabolic disorders among HIV-infected patients treated with protease inhibitors: a review. J Acquir Immune Defic Syndr 2000, 25(suppl 1):S4–S11.
34. Carr A, Samaras K, Burton S, Law M, Freund J, Chisholm DJ, et al. A syndrome of peripheral lipodystrophy, hyperlipidaemia and insulin resistance in patients receiving HIV protease inhibitors. AIDS 1998, 12:F51–F58.
This article has been cited 34 time(s).
Infectious Disease Clinics of North AmericaBehavioral aspects of HIV care: Adherence, depression, substance use, and HIV-transmission behaviorsInfectious Disease Clinics of North America
AIDS Patient Care and StdsA qualitative examination of the indirect effects of modified directly observed therapy on health behaviors other than adherenceAIDS Patient Care and Stds
Clinical Infectious Diseases
Virological control during the first 6-18 months after initiating highly active antiretroviral therapy as a predictor for outcome in HIV-infected patients: A Danish, population-based, 6-year follow-up study
Clinical Infectious Diseases, 42(1):
Pharmacy World & ScienceCommon problems with antiretroviral therapy among three Swedish groups of HIV infected individualsPharmacy World & Science
Journal of General Internal MedicineThe Association of Stigma with Self-Reported Access to Medical Care and Antiretroviral Therapy Adherence in Persons Living with HIV/AIDSJournal of General Internal Medicine
Journal of NeuroimmunologyThe HIV-1 transgenic rat as a model for HIV-1 infected individuals on HAARTJournal of Neuroimmunology
Health PsychologyAn information-motivation-behavioral skills model of adherence to antiretroviral therapyHealth Psychology
AIDS Care-Psychological and Socio-Medical Aspects of AIDS/HivLate diagnosis of HIV in Europe: definitional and public health challengesAIDS Care-Psychological and Socio-Medical Aspects of AIDS/Hiv
Journal of Womens Health
The inability to take medications openly at home: Does it help explain gender disparities in HAART use?
Journal of Womens Health, 15(2):
Scandinavian Journal of Infectious Diseases
HIV related and non-HIV related mortality before and after the introduction of highly active antiretroviral therapy (HAART) in Norway compared to the general population
Scandinavian Journal of Infectious Diseases, 39(1):
Scandinavian Journal of Infectious DiseasesLow effectiveness of highly active antiretroviral therapy and high mortality in the Greenland HIV-infected populationScandinavian Journal of Infectious Diseases
Health PsychologyRandomized controlled trial of an intervention to prevent adherence failure among HIV-Infected patients initiating Antiretroviral therapyHealth Psychology
Plos MedicineMortality of HIV-Infected Patients Starting Antiretroviral Therapy in Sub-Saharan Africa: Comparison with HIV-Unrelated MortalityPlos Medicine
International Journal of EpidemiologyCohort Profile: The Danish HIV Cohort StudyInternational Journal of Epidemiology
International Journal of EpidemiologyMortality of HIV-infected patients starting potent antiretroviral therapy: comparison with the general population in nine industrialized countriesInternational Journal of Epidemiology
Antiviral TherapyMedical and societal consequences of late presentationAntiviral Therapy
Journal of Community HealthHIV Testing Practices and Attitudes on Prevention Efforts in Six Diverse Chicago CommunitiesJournal of Community Health
Clinical Infectious Diseases
Impact of hepatitis C virus coinfection on response to highly active antiretroviral therapy and outcome in HIV-infected individuals: A Nationwide Cohort Study
Clinical Infectious Diseases, 42():
SurgeryElderly patients have more severe biliary infections: Influence of complement-killing and induction of TNF alpha productionSurgery
Journal of Infectious Diseases
Mortality in siblings of patients coinfected with HIV and hepatitis C virus
Journal of Infectious Diseases, 195(2):
AIDS Care-Psychological and Socio-Medical Aspects of AIDS/HivAn empirical test of the Information, Motivation and Behavioral Skills model of antiretroviral therapy adherenceAIDS Care-Psychological and Socio-Medical Aspects of AIDS/Hiv
Netherlands Journal of Medicine
Antiretroviral therapy adults infected in previously untreated with the human immunodeficiency virus type I: established and potential determinants of virological outcome
Netherlands Journal of Medicine, 62():
Scandinavian Journal of Infectious Diseases
Demographics of HIV-1 infection in Denmark: Results from the Danish HIV cohort study
Scandinavian Journal of Infectious Diseases, 37(5):
Annals of Internal Medicine
Survival of persons with and without HIV infection in Denmark, 1995-2005
Annals of Internal Medicine, 146(2):
Central European Journal of MedicineCauses of death in HIV-infected patients in the region of Lodz, Poland from 1995 through 2005Central European Journal of Medicine
Reproductive advice in HIV discordant couples
Medicina Clinica, 129(4):
Danish Medical Bulletin
The effectiveness of highly active antiretroviral therapy in HIV-infected patients
Danish Medical Bulletin, 51(4):
Expert Review of Anti-Infective TherapyThe MOTIVATE trials: maraviroc therapy in antiretroviral treatment-experienced HIV-1-infected patientsExpert Review of Anti-Infective Therapy
Journal of Womens HealthGender Differences in Mortality and CD4 Count Response Among Virally Suppressed HIV-Positive PatientsJournal of Womens Health
Oral DiseasesUrban legends series: oral manifestations of HIV infectionOral Diseases
HIV; antiretrovial therapy; highly active; mortality; CD4 lymphocyte count; cohort studies; databases; epidemiology
© 2004 Lippincott Williams & Wilkins, Inc.
Highlight selected keywords in the article text.