Browsing by Author "Balestre E"

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    Monitoring and switching of first-line antiretroviral therapy in adult treatment cohorts in sub-Saharan Africa: collaborative analysis.
    (2015-Jul) Haas AD; Keiser O; Balestre E; Brown S; Bissagnene E; Chimbetete C; Dabis F; Davies MA; Hoffmann CJ; Oyaro P; Parkes-Ratanshi R; Reynolds SJ; Sikazwe I; Wools-Kaloustian K; Zannou DM; Wandeler G; Egger M; Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.; Newlands Clinic, Harare, Zimbabwe.; Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland; Department of Infectious Diseases, Bern University Hospital and University of Bern, Bern, Switzerland.; Centre for Infectious Disease Epidemiology and Research, University of Cape Town, Cape Town, South Africa.; Kenya Medical Research Institute - RCTP FACES Program, Kisumu, Kenya.; Johns Hopkins University, Baltimore, MD, USA; Aurum Institute, Johannesburg, South Africa.; Rakai Health Sciences Program, Entebbe, Uganda; Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA; Johns Hopkins University School of Medicine, Baltimore, MD, USA.; Indiana University School of Medicine, Indianapolis, IN, USA.; Faculté des Sciences de la Santé de l'Université d'Abomey-Calavi, and Centre de Traitement Ambulatoire du Centre National Hospitalier Universitaire Hubert Koutoukou Maga, Cotonou, Benin.; Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland; Centre for Infectious Disease Epidemiology and Research, University of Cape Town, Cape Town, South Africa. Electronic address: egger@ispm.unibe.ch.; Infectious Diseases Institute, Mulago Hospital Complex, Kampala, Uganda.; Centre for Infectious Disease Research in Zambia, Lusaka, Zambia.; Service de Maladies Infectieuses et Tropicales, Centre Hospitalier Universitaire de Treichville, Abidjan, Côte d'Ivoire.; Centre de Recherche INSERM U897, Epidemiologie-Biostatistique, Institut de Santé Publique, Epidémiologie et Développement, Université de Bordeaux, Bordeaux, France.; CIDRZ; Centre for Infectious Disease Research in Zambia (CIDRZ)
    BACKGROUND: HIV-1 viral load testing is recommended to monitor antiretroviral therapy (ART) but is not universally available. The aim of our study was to assess monitoring of first-line ART and switching to second-line ART in sub-Saharan Africa. METHODS: We did a collaborative analysis of cohort studies from 16 countries in east Africa, southern Africa, and west Africa that participate in the international epidemiological database to evaluate AIDS (IeDEA). We included adults infected with HIV-1 who started combination ART between January, 2004, and January, 2013. We defined switching of ART as a change from a non-nucleoside reverse-transcriptase inhibitor (NNRTI)-based regimen to one including a protease inhibitor, with adjustment of one or more nucleoside reverse-transcriptase inhibitors (NRTIs). Virological and immunological failures were defined according to WHO criteria. We calculated cumulative probabilities of switching and hazard ratios with 95% CIs comparing routine viral load monitoring, targeted viral load monitoring, CD4 monitoring, and clinical monitoring, adjusting for programme and individual characteristics. FINDINGS: Of 297,825 eligible patients, 10,352 (3%) switched to second-line ART during 782 ,412 person-years of follow-up. Compared with CD4 monitoring, hazard ratios for switching were 3·15 (95% CI 2·92-3·40) for routine viral load monitoring, 1·21 (1·13-1·30) for targeted viral load monitoring, and 0·49 (0·43-0·56) for clinical monitoring. Of 6450 patients with confirmed virological failure, 58·0% (95% CI 56·5-59·6) switched by 2 years, and of 15,892 patients with confirmed immunological failure, 19·3% (18·5-20·0) switched by 2 years. Of 10,352 patients who switched, evidence of treatment failure based on one CD4 count or viral load measurement ranged from 86 (32%) of 268 patients with clinical monitoring to 3754 (84%) of 4452 with targeted viral load monitoring. Median CD4 counts at switching were 215 cells per μL (IQR 117-335) with routine viral load monitoring, but were lower with other types of monitoring (range 114-133 cells per μL). INTERPRETATION: Overall, few patients switched to second-line ART and switching happened late in the absence of routine viral load monitoring. Switching was more common and happened earlier after initiation of ART with targeted or routine viral load testing. FUNDING: National Institute of Allergy and Infectious Diseases, Swiss National Science Foundation.
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    Universal definition of loss to follow-up in HIV treatment programs: a statistical analysis of 111 facilities in Africa, Asia, and Latin America.
    (2011-Oct) Chi BH; Yiannoutsos CT; Westfall AO; Newman JE; Zhou J; Cesar C; Brinkhof MW; Mwango A; Balestre E; Carriquiry G; Sirisanthana T; Mukumbi H; Martin JN; Grimsrud A; Bacon M; Thiebaut R; University of Alabama at Birmingham, Birmingham, Alabama, United States of America. bchi@cidrz.org; CIDRZ; Centre for Infectious Disease Research in Zambia (CIDRZ)
    BACKGROUND: Although patient attrition is recognized as a threat to the long-term success of antiretroviral therapy programs worldwide, there is no universal definition for classifying patients as lost to follow-up (LTFU). We analyzed data from health facilities across Africa, Asia, and Latin America to empirically determine a standard LTFU definition. METHODS AND FINDINGS: At a set "status classification" date, patients were categorized as either "active" or "LTFU" according to different intervals from time of last clinic encounter. For each threshold, we looked forward 365 d to assess the performance and accuracy of this initial classification. The best-performing definition for LTFU had the lowest proportion of patients misclassified as active or LTFU. Observational data from 111 health facilities-representing 180,718 patients from 19 countries-were included in this study. In the primary analysis, for which data from all facilities were pooled, an interval of 180 d (95% confidence interval [CI]: 173-181 d) since last patient encounter resulted in the fewest misclassifications (7.7%, 95% CI: 7.6%-7.8%). A secondary analysis that gave equal weight to cohorts and to regions generated a similar result (175 d); however, an alternate approach that used inverse weighting for cohorts based on variance and equal weighting for regions produced a slightly lower summary measure (150 d). When examined at the facility level, the best-performing definition varied from 58 to 383 d (mean=150 d), but when a standard definition of 180 d was applied to each facility, only slight increases in misclassification (mean=1.2%, 95% CI: 1.0%-1.5%) were observed. Using this definition, the proportion of patients classified as LTFU by facility ranged from 3.1% to 45.1% (mean=19.9%, 95% CI: 19.1%-21.7%). CONCLUSIONS: Based on this evaluation, we recommend the adoption of ≥180 d since the last clinic visit as a standard LTFU definition. Such standardization is an important step to understanding the reasons that underlie patient attrition and establishing more reliable and comparable program evaluation worldwide. Please see later in the article for the Editors' Summary.