Introduction
Socioeconomic status is inversely associated with access to and use of health services by the general population and, in Europe and elsewhere, health inequalities in the general and in subpopulations such as HIV-positive people are a growing concern [1–3]. Members of disadvantaged socioeconomic groups face barriers to health services [2,4], even in European countries with universal and public health insurance [5]. In spite of the existence of universal access to confidential HIV testing, HIV care and combination antiretroviral therapy (cART) in most European countries [6], a patient's socioeconomic status may influence the ease or difficulty of access. Since the HIV epidemic is entrenched among the socially vulnerable, including homosexual men, injecting drug users (IDUs) and migrants [7,8], with access to healthcare increasingly limited for undocumented migrants [9,10], questions about the effect of socio-economic status on the diagnosis and treatment of HIV-positive persons are pressing.
Timely diagnosis of HIV infection is the first step in the HIV treatment cascade, but the COHERE collaboration of HIV cohorts of routine clinical practice in Europe recently reported that there is a very high rate of late presentation [11]. Those who do not know they have an HIV infection are less likely to take steps to prevent its transmission. Late diagnoses delay cART initiation and raise the rates of hospitalization, morbidity and mortality [12,13]. Universal access does not alone ensure that health services will be utilized. Measuring socioeconomic status is difficult. In adults, educational level is widely used as a proxy given that it is fairly stable beyond early adulthood and is therefore less likely to be affected by reverse causation [14–17], that is, if the association between socioeconomic status exists, because better health status allows to achieve a better socioeconomic status. Previous studies on the association between educational level, the timing of HIV diagnosis and initiation of cART lack a pan-European perspective to present a clear picture of the problem [18–20]. These studies have been conducted in single countries, each applying a different classification of educational level. Without a clear understanding of the role that socioeconomic inequality plays in access to HIV diagnosis and treatment, we cannot formulate effective policy and programs, or efficiently allocate resources, particularly at times of economic crisis [21].
We investigated the association between socioeconomic status and delayed HIV diagnosis and initiation of cART in COHERE, a large European collaboration of HIV cohort studies. We chose educational level as a proxy for socioeconomic position and harmonized data on education across cohorts. Our working hypothesis was that lower educational level is linked to higher risk of delayed diagnosis and cART initiation. Because of the widespread practice of antenatal testing in the study setting and the different testing strategies for the different HIV transmission categories, our secondary aims were to examine whether the association varied by sex and transmission category.
Methods
Patients
COHERE in EuroCoord is a collaboration of 35 observational cohorts covering 32 European countries, within the framework of the EuroCoord network of excellence (http://www.eurocoord.net). Each cohort submits data in a standardized format (the HIV Collaboration Data Exchange Protocol, http://www.hicdep.org) to co-ordinating centres at the Copenhagen HIV Programme, Denmark, or the Institut de Sante Publique d’Epidémiologie et de Développement (Bordeaux School of Public Health), Bordeaux, France. The Regional Coordinating Centres ensure adherence to strict quality assurance guidelines and perform data checks, including the removal of duplicate records. Data include information on patients’ characteristics (age, sex, geographical origin, and transmission category), use of cART (type of regimes and dates of start and discontinuation), CD4+ cell counts and plasma HIV-RNA over time and their dates, AIDS-defining conditions and deaths. Further information is available at http://www.cohere.org.
In 2012, EuroCoord defined socioeconomic variables with the intent of standardizing and harmonizing collection of socioeconomic data across cohorts in European countries. Educational level was the only available socioeconomic variable whose harmonization across European countries could be performed retrospectively. The definition of the variable ‘maximum attained level of education’ was based on the UNESCO/International Standard Classification of Education (ISCED) standard classification, and was classified as ‘uncompleted basic’ (ISCED 0), ‘basic’ (ISCED 1 and 2), ‘secondary’ (ISCED 3 and 4) and ‘tertiary’ (ISCED 5 and 6), based on data on education systems and reforms available from the European Encyclopaedia on National Education Systems (http://eacea.ec.europa.eu/education/eurydice/eurypedia_en.php).
These analyses included data from nine cohorts in six European countries (Austria, France, Greece, Italy, Spain and Switzerland) that collected data on maximum attained educational level. Patients were included if they were diagnosed with HIV between 1 January 1996 and 31 December 2011, were aged at least 16 years, were enrolled within 6 months of HIV diagnosis, and had at least one measured CD4+ cell count within 6 months of their HIV diagnosis while cART-naive and the level of education was known [11]. Patients from COHERE's three seroconverter cohorts were excluded; by definition, they could not qualify as late presenters. Ethics approval was granted by the Ethic Committees of each of the participating cohorts according to country regulations. Signed informed consent was obtained from all patients.
Data were pooled in September 2011 within COHERE in EuroCoord (http://www.cohere.org and http://www.EuroCoord.net) and additional data on educational level data were received from the cohorts in 2012.
Statistical analyses
Delayed HIV presentation
We defined delayed HIV presentation based on definitions proposed by the European Late Presenter Consensus Working group: presentation with advanced HIV disease (AHD) as CD4+ cell count below 200 cells/μl or an AIDS-defining event in the 6 months following presentation; and presentation with late HIV disease (LHD) as CD4+ cell count below 350 cells/μl or an AIDS-defining event in the 6 months following diagnosis [22].
We used logistic regression models to explore the association between educational level with AHD and LHD, adjusting for the following potential confounders, chosen a priori: calendar period of HIV diagnosis (<2001 versus ≥2001, to reflect the introduction of optimal cART regimen, including boosted protease inhibitors and non-nucleoside reverse transcriptase inhibitors); transmission category (MSM, heterosexuals, IDUs, other/unknown); geographical origin (Europe, non-European, unknown); age at HIV diagnosis (<35 versus ≥35 years, median age of the dataset); sex; and cohort. Likelihood ratio tests were used to determine if sex and transmission category were effect modifiers for the association between educational level and delayed HIV diagnosis. We also refit the models treating age at HIV diagnosis as a continuous variable (linear and fractional polynomial [23]), and, since the younger patients might not have completed education yet, restricting to individuals with age above 25 years. Finally, we described differences by broad cohort geographical areas defined a priori as Austria, France and Switzerland; and Greece, Italy and Spain. These analyses were descriptive and not adjusted for potential confounders. We present detailed results for the AHD analyses and a summary of LHD analyses.
Because the analyses excluded patients with unknown educational level and complete case analyses may be biased if data on educational level are not missing completely at random (MCAR), we used the following sensitivity analyses. First, we used multiple imputation to impute educational level [24] assuming data are missing at random. After inspecting the characteristics of the individuals with available and with missing data on level of education, we decided to include in the imputation model CD4+ cell count at HIV diagnosis in addition to all variables used in the analysis models. Whereas age and CD4+ cell count were treated as continuous variables, all other variables were treated as categorical. Twenty imputed data sets were generated, analysed separately and then combined using Rubin's rule [25]. Second, we imputed missing data on educational level under the assumption of data missing not at random and we assigned all patients with unknown educational level to basic education, secondary education and tertiary education in separate analyses. These extreme case scenarios are unrealistic in practice, but provide an illustration of how sensitive the analyses may be to assumptions regarding missing data. Since we excluded patients without CD4+ cell count measured within 6 months of HIV, we also ran analyses in which we assumed these patients were all AHD and none AHD. Finally, we reanalysed the data including patients who were enrolled and had more than one CD4+ cell count within 3 months and 12 months of HIV diagnosed rather than 6 months of HIV diagnosis as in the main analysis.
CD4+ cell count at initiation of antiretroviral therapy
We explored patterns of delayed cART initiation among patients included in the analyses of delayed HIV diagnosis. We further restricted the analysis to individuals who initiated cART while cART-naive and who had their CD4+ cell count measured at least once, between 6 months before and 1 week after cART was initiated. Tests for trend and multiple median regression models estimated the association between level of education and CD4+ cell count at cART initiation overall and when restricted to individuals who did not present with AHD. All models were adjusted for sex, calendar year of cART initiation, transmission category, geographical origin, age at cART initiation, and cohort. Since there is no formal definition of late cART initiation, and national and international recommendations changed significantly across the study period, we decided that analyses of the distribution of CD4+ cell count at cART initiation with median regression would return more interpretable and robust results. As a sensitivity analysis, we defined late cART initiation as individuals with a CD4+ cell count below 350 cells/μl prior to initiation, and used logistic regression models adjusting for the same covariates above.
Analyses were performed using Stata version 11.0 (Stata Corp., College Station, Texas, USA).
Results
Of the 37 438 patients diagnosed with HIV during 1996–2011, 22 024 were excluded (12 656 enrolled >6 months of HIV diagnosis; 2459 had no CD4+ cell count measurements while cART-naive within 6 months of HIV diagnosis; 6909 had unknown level of education). Of the remaining 15 414 patients, 9, 28, 47 and 15% had uncompleted basic, basic, secondary and tertiary education. Compared to the excluded individuals, these were more likely to be of European geographical origin, infected through sex between men and diagnosed with HIV after 2000. Median [interquartile range (IQR)] CD4+ cell count and age at HIV diagnosis were 304 (125–503) cells/μl and 35 [29–43] years; 76% were men, and 83% acquired HIV through heterosexual or MSM contact. Proportions with uncompleted basic, basic, secondary and tertiary education were 17, 39, 39 and 6% for women and 7, 25, 50 and 18% for men.
Delayed diagnosis
A total of 6129 (40%) patients presented with AHD, of which 5725 had CD4+ below 200 cells/μl, 404 had an AIDS-defining event, and 2522 had both. The proportion of AHD decreased with educational level: 52, 45, 37, and 31% for uncompleted basic, basic, secondary and tertiary education (P for trend <0.001). Although the proportion of patients presenting with AHD did not differ for men and women (39 and 41%, respectively), the proportion of AHD for uncompleted basic and tertiary education varied more for men (54–30%) than for women (49–41%) (P < 0.001 for interaction test). The gradient was more pronounced after 2001 (P < 0.001 test for interaction); the proportion of AHD with uncompleted basic and tertiary education was 55 and 43% before 2001 and 50 and 28% from 2001 onwards (Table 1). The gradient by educational level was more pronounced for MSM compared to other groups; the proportion of AHD ranged between 45 and 26% for patients with uncompleted basic and tertiary education (Table 1). Gradients by educational level varied by cohort, but were consistently observed when data were grouped by broad geographical regions, although more noticeable in Greece, Italy and Spain (Table 1).
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Table 1: Patient characteristics at HIV diagnosis and proportion who presented with advanced HIV disease by baseline characteristics, overall and stratified by completed level of education.
The gradient by educational level was maintained in multivariate analyses; compared to tertiary education, the adjusted odds ratios (aORs) for AHD were 1.72 [95% confidence interval (CI) 1.48–2.00], 1.39 (95% CI 1.24–1.56) and 1.20 (95% CI 1.08–1.34) in patients with uncompleted basic, basic and secondary completed education. In the adjusted logistic model, male sex, calendar period of diagnosis 1996–2000, non-European geographical origin, membership in the heterosexual and injecting drug use transmission groups, and age above 35 years at HIV diagnosis were significant predictors of presenting with AHD (data not shown).
Figure 1 shows the aOR for presentation with AHD and sex-stratified educational level. The gradient by level of education was more prominent in men (P value for interaction 0.082). For instance, the aOR for uncompleted basic education versus tertiary education was 1.70 (95% CI 1.41–2.04) in men and 1.31 (95% CI 0.96–1.82) in women. The trend by educational level was maintained restricting our analysis to MSM; aORs were 1.88 (95% CI 1.35–2.61), 1.67 (95% CI 1.40–1.99), and 1.27 (95% CI 1.10–1.46) compared to patients with tertiary education (Table 2) (P value 0.225 for interaction test).
Fig. 1: Multivariate analyses of presentation with advanced HIV disease by sex.Estimates from multivariable logistic models adjusted by transmission mode, geographical origin, age at HIV diagnosis, calendar period and cohort. Test for interaction: P = 0.082. COHERE in EuroCoord, 1996–2011.
Table 2: Multivariate analyses of presentation with advanced HIV disease by transmission category.
There were 9486 (62%) individuals with LHD at presentation. Among patients with uncompleted basic, basic, secondary and tertiary educational level, the proportions of LHD were 73, 65, 59 and 55%. Trends in proportion of LHD and aOR by level of education were similar to the AHD analysis (Appendix 1, https://links.lww.com/QAD/A557).
Sensitivity analyses
Individuals with unknown level of education had similar demographic characteristics and similar proportions of AHD and LHD, but were more likely to have an unknown transmission mode and geographical origin (Appendix 2, https://links.lww.com/QAD/A557). Results were consistent in all sensitivity analyses (Appendices 3 and 4, https://links.lww.com/QAD/A557).
Delayed initiation of antiretroviral therapy
Of the 15414 individuals included in the previous analysis, 72% (11 035) initiated cART. These patients were mostly men (78%), MSM, had initiated cART after a median (IQR) of 2 (1–7) months after HIV diagnosis with a median (IQR) CD4+ cell count of 218 (90–331) cells/μl and age 36 (31–42) years. Overall, results from the median regression analyses indicate a strong association between CD4+ cell count distribution at cART initiation and educational level (P < 0.001 heterogeneity test). Notably, patients with uncompleted basic, basic and secondary education had estimated median CD4+ cell counts of 49, 27 and 19 cells/μl lower than patients with tertiary education at the time of cART initiation (Table 3). When the analyses were limited to the 5906 individuals who did not present with AHD, we found a trend of lower CD4+ cell count with lower educational level, but it was not statistically significant (Table 4). The logistic regression analysis for late cART initiation, defined as CD4+ cell count below 350 cells/μl, showed a statistically significant association between CD4+ cell count distribution at cART initiation and educational level.
Table 3: Distribution of CD4+ at combination antiretroviral therapy initiation and multivariable analyses.
Table 4: Distribution of CD4+ cell count at combination antiretroviral therapy initiation and multivariable analyses restricting to individuals who did not present with advanced HIV diagnosis.
Discussion
We found that in Europe in the cART era, individuals with lower educational level were substantially more likely to present with AHD and LHD, even after taking into account individual characteristics that are traditionally associated with delayed HIV diagnosis. This gradient was more marked for men than for women. Among patients who initiated cART, lower level of education was independently associated with lower median CD4+ cell count at cART initiation.
The mechanisms underlying the observed associations are likely to implicate a number of material and psychosocial pathways through which education influences attitudes toward HIV testing and cART initiation. As a surrogate of socioeconomic status, individuals with higher education have better material resources such as employment and higher-paying occupations [26], which imply easier access to healthcare facilities. People with higher education are more likely to practice health-promoting behaviours, including timely healthcare check-ups and screenings [27] and, therefore, might be more likely to test for HIV when they perceive themselves at increased risk. Higher education increases people's health literacy and cognitive skills, enabling them to make better informed health-related choices [4,27], including the importance of appropriate HIV testing and timely initiation of cART with better access to websites and community resources. Finally, education is linked with social and psychological factors, including sense of control, social standing and social support [26], and individuals with higher education may face fewer barriers to access HIV care and be more resilient to stigma [28].
Interestingly, the association between lower education and lower CD4+ cell count at cART initiation was substantially reduced though did not disappear, when the analyses were restricted to individuals with timely HIV diagnosis. Therefore, the observed association between delayed cART initiation and lower educational level could be largely, but not solely, attributed to patterns of delayed HIV diagnosis by educational level. This highlights the existence of additional socioeconomic barriers that deter access to cART after HIV diagnosis.
The observed differences concord with previous evidence from Spain and Italy indicating a higher frequency of delayed HIV diagnosis and cART initiation among individuals of lower educational level [19,20], and build on previous work conducted within COHERE showing very high levels of delayed HIV diagnoses in Europe [11]. We show in this study that not only presentation with AHD and LHD is common across all educational level groups, but that it exhibits an increasing trend with decreasing educational levels. The gradient of the association between level of education and delayed diagnosis is more remarkable for AHD than for LHD. Thus, socioeconomic inequalities are particularly visible in patients with very low CD4+ cell count levels, usually associated with high risk of AIDS and mortality [29]. These results are compatible with inequity in access to HIV testing and are worrisome considering that the study was conducted in six European countries with universal public health systems.
The study shows that the association of delayed HIV diagnosis with educational level differed between men and women in absolute and relative terms. The milder gradient in AHD by educational level observed in women might be explained by universal HIV testing offered to all pregnant women in European countries for prevention of mother-to-child HIV transmission [30]. These findings, however, need to be put in the context of the literature that underlies that economic position may be a poorer predictor for a number of health outcomes in women [31]. The proportions of AHD for women from all four educational levels are lower than those of the heterosexual men but, compared to MSM, women with secondary and tertiary education exhibit higher levels of AHD. MSM, one of the key target groups for HIV screening in most European countries [6], still had a steep decline in AHD by educational level. These results indicate that universal HIV screening approaches such as those aiming at all pregnant women, as an alternative to targeted HIV testing policies based on HIV risk perception such as those aimed at MSM, could have the additional benefit to decrease socioeconomic inequities in accessing HIV testing. Whereas the former approach would offer a voluntary HIV test to all pregnant women, for MSM to be offered an HIV test, disclosure of unprotected sex with other men and/or gay identity becomes a prerequisite which is likely to be influenced by socioeconomic position [28,32].
For a long time, the interest to collect patient's socioeconomic information in HIV cohorts has been rather limited, though some groups have strongly promoted it [33]. The advantages of this study which harmonized socioeconomic variables across several cohorts in European countries are its large sample size, allowing exploring interactions by sex and transmission group, the inclusion of patients under routine care and its standardised definition of the exposure variable across European countries. The pan-European perspective of the study suggests that the observed gradient of increased risk of late HIV diagnosis and late cART initiation with lower educational level is present across European countries with different HIV healthcare and education systems. The study also has some limitations. Not all cohorts in COHERE collect data on educational level, so this study was based on data from only six European countries. Results thus might not be generalizable to settings with different social systems such as those from northern and eastern Europe. Some bias may have been introduced through misclassification since an individual's educational level might not always reflect their socioeconomic status and whether this error is different for men and women. In smaller-sized cohort studies, combined indicators such as living on welfare, unstable housing and unemployment were found to be associated with non-adherence, whereas no association could be found with each component alone [34]. Protopopescu et al. have recently described how an indicator combining low educational level and unemployment was found associated with higher rates of mortality in the APROCO cohort while none of each separate variable had a significant effect [35]. However, educational level was the only variable collected by the six participating cohorts and it is important to note that education is one of the most used measures of socioeconomic status in healthcare research because of its influence on future occupational opportunities and earning potential [14]. Foreigners unfamiliar with the educational system of the host country may have misreported their educational level. However, these forms of misclassification are likely to be non-differential, resulting in an underestimate of the relationship between socioeconomic status and delayed HIV diagnosis and cART initiation. Despite this potential bias, we found significant trends and a clear gradient by educational level, and we believe this strengthens, rather than weakens, our conclusions about the existence of socioeconomic inequalities. Finally, a number of individuals were excluded from the analyses because their educational level was unknown or no CD4+ cell count measurement was available at the time of HIV diagnosis. Our estimates might be biased if the included and excluded patients had a different distribution of educational level and CD4+ cell count at HIV diagnosis. Our conclusions were, however, robust to a set of sensitivity analyses exploring different scenarios of missing data mechanisms.
In conclusion, this study shows that inequalities by educational level, a proxy of a socioeconomic status, in HIV testing and initiation of cART are present in European countries with universal healthcare systems, and thus, individuals with lower educational level will not equally benefit from the effectiveness of cART. Policies and interventions that target socioeconomic determinants leading to delays in HIV diagnosis and cART initiation are needed. Whether the observed inequalities are all avoidable, and thus amendable, is a discussion to be urgently advanced within the equity policy framework for Europe Health 2020 [36] and more deeply taken into account in clinical and epidemiological research.
Socio-economic inequalities and HIV Writing Group
Sara Lodi – Centro Nacional de Epidemiologia, Instituto de Salud Carlos III, Madrid, Spain; Rosemary Dray-Spira – INSERM, UMR_S 1136, Pierre Louis Institute of Epidemiology and Public Health, Team Research in Social Epidemiology, F-75013, Paris, France and Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1136, Pierre Louis Institute of Epidemiology and Public Health, Team Research in Social Epidemiology, F-75013, Paris, France; Giota Touloumi – University of Athens Medical School Athens, Greece; Dominique Braun – University Hospital of Zurich, Switzerland; Ramon Teira – Hospital Sierrallana, Torrelavega, Spain; Antonella D’Arminio Monforte – Department of Health Sciences, Institute of Infectious Diseases, Milan, Italy; Anne Gallois – Agència de Salut Pública de Catalunya-ICO, Barcelona, Spain; Robert Zangerle – Innsbruck Medical University, Innsbruck, Austria; Bruno Spire – Université Paris Diderot, Sorbonne Paris Cité, UMR 738, Paris, France, INSERM, UMR 738, Paris, France; Francois Dabis – Université of Bordeaux, ISPED, Centre Inserm, U897–Epidémiologie–Biostatistiques, Bordeaux, France and Inserm U897–Epidémiologie–Biostatistiques, Bordeaux, France; Cornelia Stähelin – Division of Infectious Diseases and Hospital Epidemiology, University Hospital Berne, Switzerland; Monique Termote – Université de Bordeaux, ISPED, Centre INSERM U897-Epidémiologie Statistique, Bordeaux, France; INSERM, ISPED, Centre INSERM U897-Epidémiologie Statistique, Bordeaux, France; Ole Kirk – CHIP, Dept. of Infectious Diseases and Rheumatology, Rigshospitalet, University of Copenhagen, Denmark; Genevieve Chêne – Université de Bordeaux, ISPED, Centre INSERM U897-Epidémiologie Statistique, Bordeaux, France; INSERM, ISPED, Centre INSERM U897-Epidémiologie Statistique, Bordeaux, France; Matthias Egger – Institute of Social and Preventive Medicine, University of Bern, Switzerland; and Julia del Amo – Centro Nacional de Epidemiologia, Instituto de Salud Carlos III, Madrid, Spain and CIBERESP, Instituto de Salud Carlos III, Madrid, Spain.
Acknowledgements
Principal contributions made by the authors: Data collection: Giota Touloumi, Ramon Teira, Antonella D’Arminio Monforte, Anne Gallois, Robert Zangerle, Bruno Spire, Francois Dabis, Monique Termote, Ole Kirk, Genevieve Chêne, Matthias Egger, Julia del Amo; study design and statistical analyses: Sara Lodi, Julia del Amo; interpretation of results: all authors; read and approved the manuscript: all authors; drafted the manuscript: Sara Lodi, Julia del Amo. Sara Lodi is the guarantor.
Funding source: The COHERE study group has received unrestricted funding from: Agence Nationale de Recherches sur le SIDA et les Hépatites Virales (ANRS), France; HIV Monitoring Foundation, The Netherlands; and the Augustinus Foundation, Denmark. COHERE receives funding from the European Union Seventh Framework Programme (FP7/2007–2013) under EuroCoord grant agreement n° 260694. Sara Lodi has received project specific funding from European Union Seventh Framework Programme (FP7/2007-2013) under Marie Curie grant agreement n° n 274817. A list of the funders of the participating cohorts can be found at http://www.COHERE.org.
The authors gratefully acknowledge Kali Tal for word editing and commenting on the manuscript.
The EuroCoord acknowledgment appendix may be found at http://www.cphiv.dk/portals/0/files/EuroCoord_appendix_December2012.pdf.
The Collaboration of Observational HIV Epidemiological Research Europe (COHERE) group
Socio-economic inequalities and HIV Writing Group: Sara Lodi, Rosemary Dray-Spira, Giota Touloumi, Dominique Braun, Ramon Teira, Antonella D’Arminio Monforte, Anne Gallois, Robert Zangerle, Bruno Spire, Francois Dabis, Cornelia Stähelin, Monique Termote, Ole Kirk, Genevieve Chêne, Matthias Egger, Julia del Amo.
Socio-economic inequalities and HIV Working Group: Sara Lodi, Rosemary Dray-Spira, Giota Touloumi, Ramon Teira, Antonella D’Arminio Monforte, Anne Gallois, Robert Zangerle, Catherine Leport, Bruno Spire, Francois Dabis, Genevieve Chêne, Matthias Egger, Julia del Amo.
Steering Committee - Contributing Cohorts: Robert Zangerle (AHIVCOS),Giota Touloumi (AMACS), Josiane Warszawski (ANRS CO1 EPF/ANRS CO11 OBSERVATOIRE EPF), Laurence Meyer (ANRS CO2 SEROCO), François Dabis (ANRS CO3 AQUITAINE, and ANRS CO13 HEPAVIH), Murielle Mary Krause (ANRS CO4 FHDH), Jade Ghosn (ANRS CO6 PRIMO), Catherine Leport (ANRS CO8 COPILOTE), Peter Reiss (ATHENA), Ferdinand Wit (ATHENA), Maria Prins (CASCADE), Heiner Bucher (CASCADE), Caroline Sabin (CHIC), Diana Gibb (CHIPS), Gerd Fätkenheuer (Cologne-Bonn), Julia Del Amo (CoRIS), Niels Obel (Danish HIV Cohort), Claire Thorne (ECS), Amanda Mocroft (EuroSIDA), Ole Kirk (EuroSIDA), Christoph Stephan (Frankfurt), Santiago Pérez-Hoyos (GEMES-Haemo), Osamah Hamouda (German ClinSurv), Barbara Bartmeyer (German ClinSurv), Antoni Noguera-Julian (CORISPE-cat), Andrea Antinori (ICC), Antonella d’Arminio Monforte (ICONA), Norbert Brockmeyer (KOMPNET), José Ramos (Madrid PMTCT Cohort), Pablo Rojo Conejo (CORISPES-Madrid),Toni Soriano (NENEXP), Manuel Battegay (SHCS), Andri Rauch (SHCS), Cristina Mussini (Modena Cohort), Pat Tookey (NSHPC), Jordi Casabona (PISCIS), Jose M. Miró (PISCIS), Antonella Castagna (San Raffaele), Deborah Konopnick (St. Pierre Cohort), Tessa Goetghebuer (St Pierre Paediatric Cohort), Carlo Torti (Italian Master Cohort), Ramon Teira (VACH), Myriam Garrido (VACH). European AIDS Treatment Group: David Haerry.
Executive Committee: Stéphane de Wit (Chair, St Pierre University Hospital), Jordi Casabona (PISCIS), Dominique Costagliola (FHDH), Antonella d’Arminio-Monforte (ICONA), Manuel Battegay (SHCS), Julia del Amo (CoRIS), Amanda Mocroft (EuroSida), Jesper Grarup (Head, Copenhagen Regional Coordinating Centre), Genevieve Chêne (Head, Bordeaux Regional Coordinating Centre). Paediatric Cohort Representatives: Ali Judd, Pablo Rojo Conejo.
Regional Coordinating Centres: Bordeaux RCC: Diana Barger, Céline Colin, Christine Schwimmer, Monique Termote, Linda Wittkop; Copenhagen RCC: Maria Campbell, Nina Friis-Møller, Jesper Kjaer, Dorthe Raben, Rikke Salbøl Brandt.
Project Leads and Statisticians: Manuel Battegay, Julia Bohlius, Vincent Bouteloup, Heiner Bucher, Alessandro Cozzi-Lepri, François Dabis, Antonella d’Arminio Monforte, Julia del Amo, Maria Dorrucci, Matthias Egger, Frederik Engsig, Hansjakob Furrer, Ali Judd, Ole Kirk, Olivier Lambotte, Sara Lodi, Sophie Matheron, Laurence Meyer, Jose Miro, Amanda Mocroft, Susana Monge, Fumiyo Nakagawa, Niels Obel, Roger Paredes, Andrew Phillips, Massimo Puoti, Caroline Sabin, Colette Smit, Jonathan Sterne, Rodolphe Thiebaut, Claire Thorne, Carlo Torti, Giota Touloumi, Marc van der Valk, Linda Wittkop, Natasha Wyss.
Conflicts of interest
Support for travel to meetings for the study or other purposes: Giota Touloumi, Genevieve Chene (INSERM).
Grants/grants pending: Giota Touloumi (EuroCoord, NIH, Hellenic Center for Disease Control and Prevention, UNICEF), Sara Lodi (FP7 Marie Curie), Julia del Amo (FP7 EU), Genevieve Chêne (French Agency for AIDS and hepatitis research, French National Institute of Health and medical Research (INSERM) Gilead, TIbotec-Janssen, Roche, MSD, Janssen, Boehringer Ingelheim,
Bristol-Myers Squibb, GlaxoSmithKline, ViiV Healthcare, Mylan, Abbie), Ole Kirk (Eurosida).
Board membership: Ole Kirk (Viiv, Merck, GILEAD).
Consultancy: Bruno Spire (MSD, GILEAD), Ramon Teira (Gilead Sciences, Abbvie, Bristol-Myers), Genevieve Chêne (Roche), Ole Kirk (GILEAD).
Payment for lectures including service on speakers bureaus: Bruno Spire, Ramon Teira (Gilead Sciences, Janssen Cilag, Bristol-Myers), Ole Kirk (Abbott, GILEAD, Tibotec, Viiv).
Travel/accommodations/meeting expenses/unrelated to activities listed: Bruno Spire (MSD), Cornelia Stahelin (Gilead Sciences, Merck, Sharp & Dhome), Ramon Teira (Gilead Sciences, Bristol-Myers, ViiV Healthcare), Genevieve Chêne.
Payment for development of educational presentations: Ole Kirk (Abbott, BMS, GILEAD, Merck, Viiv).
No other conflict of interest to declare.
