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Negoescu DM, Zhang Z, Bucher HC, Bendavid E. Differentiated Human Immunodeficiency Virus RNA Monitoring in Resource-Limited Settings: An Economic Analysis. Clin Infect Dis 2018; 64:1724-1730. [PMID: 28329208 PMCID: PMC5447887 DOI: 10.1093/cid/cix177] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2016] [Accepted: 02/25/2017] [Indexed: 12/17/2022] Open
Abstract
Background. Viral load (VL) monitoring for patients receiving antiretroviral therapy (ART) is recommended worldwide. However, the costs of frequent monitoring are a barrier to implementation in resource-limited settings. The extent to which personalized monitoring frequencies may be cost-effective is unknown. Methods. We created a simulation model parameterized using person-level longitudinal data to assess the benefits of flexible monitoring frequencies. Our data-driven model tracked human immunodeficiency virus (HIV)–infected individuals for 10 years following ART initiation. We optimized the interval between viral load tests as a function of patients’ age, gender, education, duration since ART initiation, adherence behavior, and the cost-effectiveness threshold. We compared the cost-effectiveness of the personalized monitoring strategies to fixed monitoring intervals every 1, 3, 6, 12, and 24 months. Results. Shorter fixed VL monitoring intervals yielded increasing benefits (6.034 to 6.221 discounted quality-adjusted life-years [QALYs] per patient with monitoring every 24 to 1 month over 10 years, respectively, standard error = 0.005 QALY), at increasing average costs: US$3445 (annual monitoring) to US$5393 (monthly monitoring) per patient, respectively (standard error = US$3.7). The adaptive policy optimized for low-income contexts achieved 6.142 average QALYs at a cost of US$3524, similar to the fixed 12-month policy (6.135 QALYs, US$3518). The adaptive policy optimized for middle-income resource settings yields 0.008 fewer QALYs per person, but saves US$204 compared to monitoring every 3 months. Conclusions. The benefits from implementing adaptive vs fixed VL monitoring policies increase with the availability of resources. In low- and middle-income countries, adaptive policies achieve similar outcomes to simpler, fixed-interval policies.
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Affiliation(s)
- Diana M Negoescu
- College of Science and Engineering, Industrial and System Engineering, University of Minnesota, Minneapolis
| | - Zhenhuan Zhang
- College of Science and Engineering, Industrial and System Engineering, University of Minnesota, Minneapolis
| | - Heiner C Bucher
- Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel and University of Basel, Switzerland
| | - Eran Bendavid
- Department of Medicine, and.,Center for Health Policy/Center for Primary Care and Outcomes Research, Stanford University, California
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Caniglia EC, Cain LE, Sabin CA, Robins JM, Logan R, Abgrall S, Mugavero MJ, Hernández-Díaz S, Meyer L, Seng R, Drozd DR, Seage GR, Bonnet F, Dabis F, Moore RD, Reiss P, van Sighem A, Mathews WC, Del Amo J, Moreno S, Deeks SG, Muga R, Boswell SL, Ferrer E, Eron JJ, Napravnik S, Jose S, Phillips A, Justice AC, Tate JP, Gill J, Pacheco A, Veloso VG, Bucher HC, Egger M, Furrer H, Porter K, Touloumi G, Crane H, Miro JM, Sterne JA, Costagliola D, Saag M, Hernán MA. Comparison of dynamic monitoring strategies based on CD4 cell counts in virally suppressed, HIV-positive individuals on combination antiretroviral therapy in high-income countries: a prospective, observational study. Lancet HIV 2017; 4:e251-e259. [PMID: 28411091 PMCID: PMC5492888 DOI: 10.1016/s2352-3018(17)30043-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2016] [Revised: 01/14/2017] [Accepted: 01/19/2017] [Indexed: 12/24/2022]
Abstract
BACKGROUND Clinical guidelines vary with respect to the optimal monitoring frequency of HIV-positive individuals. We compared dynamic monitoring strategies based on time-varying CD4 cell counts in virologically suppressed HIV-positive individuals. METHODS In this observational study, we used data from prospective studies of HIV-positive individuals in Europe (France, Greece, the Netherlands, Spain, Switzerland, and the UK) and North and South America (Brazil, Canada, and the USA) in The HIV-CAUSAL Collaboration and The Centers for AIDS Research Network of Integrated Clinical Systems. We compared three monitoring strategies that differ in the threshold used to measure CD4 cell count and HIV RNA viral load every 3-6 months (when below the threshold) or every 9-12 months (when above the threshold). The strategies were defined by the threshold CD4 counts of 200 cells per μL, 350 cells per μL, and 500 cells per μL. Using inverse probability weighting to adjust for baseline and time-varying confounders, we estimated hazard ratios (HRs) of death and of AIDS-defining illness or death, risk ratios of virological failure, and mean differences in CD4 cell count. FINDINGS 47 635 individuals initiated an antiretroviral therapy regimen between Jan 1, 2000, and Jan 9, 2015, and met the eligibility criteria for inclusion in our study. During follow-up, CD4 cell count was measured on average every 4·0 months and viral load every 3·8 months. 464 individuals died (107 in threshold 200 strategy, 157 in threshold 350, and 200 in threshold 500) and 1091 had AIDS-defining illnesses or died (267 in threshold 200 strategy, 365 in threshold 350, and 459 in threshold 500). Compared with threshold 500, the mortality HR was 1·05 (95% CI 0·86-1·29) for threshold 200 and 1·02 (0·91·1·14) for threshold 350. Corresponding estimates for death or AIDS-defining illness were 1·08 (0·95-1·22) for threshold 200 and 1·03 (0·96-1·12) for threshold 350. Compared with threshold 500, the 24 month risk ratios of virological failure (viral load more than 200 copies per mL) were 2·01 (1·17-3·43) for threshold 200 and 1·24 (0·89-1·73) for threshold 350, and 24 month mean CD4 cell count differences were 0·4 (-25·5 to 26·3) cells per μL for threshold 200 and -3·5 (-16·0 to 8·9) cells per μL for threshold 350. INTERPRETATION Decreasing monitoring to annually when CD4 count is higher than 200 cells per μL compared with higher than 500 cells per μL does not worsen the short-term clinical and immunological outcomes of virally suppressed HIV-positive individuals. However, more frequent virological monitoring might be necessary to reduce the risk of virological failure. Further follow-up studies are needed to establish the long-term safety of these strategies. FUNDING National Institutes of Health.
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Affiliation(s)
- Ellen C Caniglia
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA.
| | - Lauren E Cain
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
| | | | - James M Robins
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA; Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Roger Logan
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Sophie Abgrall
- Sorbonne Universités, UPMC Univ Paris 06, INSERM, Institut Pierre Louis d'épidémiologie et de Santé Publique (IPLESP UMRS 1136), Paris, France; Assistance Publique-Hopitaux de Paris (AP-HP), Hopital Antoine Béclère, Service de Médecine Interne, Clamart, France
| | - Michael J Mugavero
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA; UAB Center for AIDS Research, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Sonia Hernández-Díaz
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Laurence Meyer
- Université Paris Sud, INSERM CESP U1018, Paris, France; AP-HP, Hopital de Bicêtre, Service de Santé Publique, le Kremlin Bicêtre, France
| | - Remonie Seng
- Université Paris Sud, INSERM CESP U1018, Paris, France; AP-HP, Hopital de Bicêtre, Service de Santé Publique, le Kremlin Bicêtre, France
| | - Daniel R Drozd
- School of Medicine, Division of Allergy and Infectious Diseases, University of Washington, Seattle, WA, USA
| | - George R Seage
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Fabrice Bonnet
- Institut de Santé Publique, d'Epidémiologie et de Développement, Université de Bordeaux, Bordeaux, France; Department of Internal Medicine, Centre Hospitalier Universitaire de Bordeaux, Bordeaux, France
| | - Francois Dabis
- INSERM U897, Centre INSERM Epidémiologie et Biostatistique, Université de Bordeaux, Bordeaux, France; Department of Internal Medicine, Centre Hospitalier Universitaire de Bordeaux, Bordeaux, France
| | - Richard D Moore
- School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Peter Reiss
- Stichting HIV Monitoring, Amsterdam, Netherlands; Academic Medical Center, Department of Global Health and Division of Infectious Diseases, University of Amsterdam, Amsterdam, Netherlands; Amsterdam Institute for Global Health and Development, Amsterdam, Netherlands
| | | | | | - Julia Del Amo
- National Centre of Epidemiology, Instituto de Salud Carlos III, Madrid, Spain; Consorcio de Investigación Biomédica de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Santiago Moreno
- Ramón y Cajal Hospital, IRYCIS, Madrid, Spain; University of Alcalá de Henares, Madrid, Spain
| | - Steven G Deeks
- Positive Health Program, San Francisco General Hospital, San Francisco, CA, USA
| | - Roberto Muga
- Servei de Medicina Interna, Hospital Universitari Germans Trias i Pujol, Universitat Autònoma de Barcelona, Barcelona, Spain
| | | | - Elena Ferrer
- Hospital Universitari de Bellvitge-Bellvitge Institute for Biomedical Research, Hospitalet de Llobregat, Barcelona, Spain
| | - Joseph J Eron
- Division of Infectious Diseases, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Sonia Napravnik
- Division of Infectious Diseases, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | | | - Amy C Justice
- Yale School of Medicine, New Haven, CT, USA; VA Connecticut Healthcare System, West Haven, CT, USA
| | - Janet P Tate
- Yale School of Medicine, New Haven, CT, USA; VA Connecticut Healthcare System, West Haven, CT, USA
| | - John Gill
- Southern Alberta HIV Clinic, University of Calgary, Calgary, AB, Canada
| | - Antonio Pacheco
- Programa de Computação Científica, FIOCRUZ, Rio de Janeiro, Brazil
| | | | - Heiner C Bucher
- Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel, Basel, Switzerland
| | - Matthias Egger
- Centre for Infectious Disease Epidemiology and Research, School of Public Health and Family Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa; University of Bern, Institute for Social and Preventive Medicine, Bern, Switzerland
| | - Hansjakob Furrer
- Department of Infectious Diseases, Bern University Hospital and University of Bern, Bern, Switzerland
| | | | - Giota Touloumi
- Department of Hygiene, Epidemiology and Medical Statistics, Athens University Medical School, Athens, Greece
| | - Heidi Crane
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Jose M Miro
- Infectious Diseases, Hospital Clinic-IDIBAPS, Barcelona, Spain
| | - Jonathan A Sterne
- School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Dominique Costagliola
- Sorbonne Universités, UPMC Univ Paris 06, INSERM, Institut Pierre Louis d'épidémiologie et de Santé Publique (IPLESP UMRS 1136), Paris, France
| | - Michael Saag
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Miguel A Hernán
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA; Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Boston, MA, USA
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