4.4 Article

Achieving consistency in measures of HIV-1 viral suppression across countries: derivation of an adjustment based on international antiretroviral treatment cohort data

期刊

出版社

JOHN WILEY & SONS LTD
DOI: 10.1002/jia2.25776

关键词

antiretroviral therapy; HIV; viral load

资金

  1. US National Institute on Alcohol Abuse and Alcoholism [U01-AA026209]
  2. ANRS (France REcherche Nord&Sud Sida-hiv Hepatites)
  3. Institut National de la Sante et de la Recherche Medicale (INSERM)
  4. French Ministry of Health
  5. Italian Ministry of Health
  6. Spanish Ministry of Health
  7. Preben and Anne Simonsens Foundation
  8. Ministry of Science and Innovation
  9. Spanish Network for AIDS Research [Spanish Network of Excellence on HIV] [RD12/0017/0018, RD16CIII/0002/0006]
  10. Abbott
  11. Gilead
  12. Tibotec-Upjohn
  13. ViiV Healthcare
  14. MSD
  15. GlaxoSmithKline
  16. Pfizer
  17. Bristol-Myers Squibb
  18. Roche
  19. Boehringer Ingelheim
  20. U.S. National Institutes of Health's National Institute of Allergy and Infectious Diseases (NIAID)
  21. Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
  22. National Cancer Institute (NCI)
  23. National Institute of Mental Health (NIMH)
  24. National Institute on Drug Abuse (NIDA)
  25. National Heart, Lung, and Blood Institute (NHLBI)
  26. National Institute on Alcohol Abuse and Alcoholism (NIAAA)
  27. National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
  28. Fogarty International Center (FIC) as part of the International Epidemiology Databases to Evaluate AIDS (IeDEA) [U01AI069907]
  29. Australian Government Department of Health and Ageing
  30. NIH [U01AI069923, K01AI131895]
  31. NIAID
  32. NICHD
  33. NCI
  34. NIMH
  35. NIDA
  36. NHLBI
  37. NIAAA
  38. NIDDK
  39. FIC
  40. National Library of Medicine (NLM)
  41. NIAID of the National Institutes of Health [U01AI096299]
  42. US NIH East Africa IeDEA Consortium [U01AI069911]
  43. US NIH [K01AI131895, U01AI069918, F31AI124794, F31DA037788, G12MD007583, K01AI093197, K23EY013707, K24AI065298, K24AI118591, K24DA000432, KL2TR000421, M01RR000052, N01CP01004, N02CP055504, N02CP91027, P30AI027757]
  44. THE US NIH [P30AI027763, P30AI027767, P30AI036219, P30AI050410, P30AI094189, P30AI110527, P30MH62246, R01AA016893, R01CA165937, R01DA011602, R01DA012568, R01 AG053100, R24AI067039, U01AA013566, U01 AA020790, U01AI031834, U01AI034989, U01AI034993, U01AI034994, U01AI035004, U01AI035039, U01AI035040, U01AI035041, U01AI035042]
  45. 'US NIH' [U01AI042590, U01AI068634, U01AI068636, U01AI069432, U01AI069434, U01AI103390, U01AI103397, U01AI103401, U01AI103408, U01DA03629, U01DA036935, U01HD032632, U10EY008057, U10EY008052, U10EY008067, U24AA 020794, U54MD007587, UL1RR024131]
  46. Centers for Disease Control and Prevention, USA [CDC-200-2006-18797, CDC-200-2015-63931]
  47. Agency for Healthcare Research and Quality, USA [90047713]
  48. Health Resources and Services Administration, USA [90051652]
  49. Canadian Institutes of Health Research, Canada [CBR-86906, CBR-94036, HCP-97105, TGF-96118]
  50. Ontario Ministry of Health and Long Term Care
  51. Government of Alberta, Canada
  52. NIAID of the US NIH [U01AI069924]
  53. US NIH (NIAID) [U01AI069919]
  54. US NIH (NICHD) [U01AI069919]
  55. US NIH (NCI) [U01AI069919]
  56. US NIH (NHLBI) [U01AI069919]
  57. US NIH (NIDDK) [U01AI069919]
  58. US NIH (NIAAA) [U01AI069919]
  59. US NIH (FIC) [U01AI069919]
  60. US NIH (NIMH) [U01AI069919]
  61. Harmonist project [R24AI124872]
  62. NLM
  63. US NIH [Z01CP010214, Z01CP010176, U01AI037613, U01AI037984, U01AI038855, U01AI038858, UL1TR000004, UL1TR000083, UL1TR000454, UM1AI035043]

向作者/读者索取更多资源

This study aimed to propose an adjustment method to support consistent monitoring of viral suppression rates in ART patients. By considering different viral load distributions and model calibration, the reverse Weibull model was recommended for estimation.
Introduction The third of the Joint United Nations Programme on HIV/AIDS (UNAIDS) 90-90-90 targets is to achieve a 90% rate of viral suppression (HIV viral load <1000 HIV-1 RNA copies/ml) in patients on antiretroviral treatment (ART) by 2020. However, some countries use different thresholds when reporting viral suppression, and there is thus a need for an adjustment to standardize estimates to the <1000 threshold. We aim to propose such an adjustment, to support consistent monitoring of progress towards the third 90 target. Methods We considered three possible distributions for viral loads in ART patients: Weibull, Pareto and reverse Weibull (imposing an upper limit but no lower limit on the log scale). The models were fitted to data on viral load distributions in ART patients in the International epidemiology Databases to Evaluate AIDS (IeDEA) collaboration (representing seven global regions) and the ART Cohort Collaboration (representing Europe), using separate random effects models for adults and children. The models were validated using data from the World Health Organization (WHO) HIV drug resistance report and the Brazilian national ART programme. Results Models were calibrated using 921,157 adult and 37,431 paediatric viral load measurements, over 2010-2019. The Pareto and reverse Weibull models provided the best fits to the data, but for all models, the shape parameters for the viral load distributions differed significantly between regions. The Weibull model performed best in the validation against the WHO drug resistance survey data, while the Pareto model produced uncertainty ranges that were too narrow, relative to the validation data. Based on these analyses, we recommend using the reverse Weibull model. For example, if a country reports an 80% rate of viral suppression at <200 copies/ml, this model estimates the proportion virally suppressed at <1000 copies/ml is 88.3% (0.80(0.56)), with uncertainty range 85.5-90.6% (0.80(0.70)-0.80(0.44)). Conclusions Estimates of viral suppression can change substantially depending on the threshold used in defining viral suppression. It is, therefore, important that viral suppression rates are standardized to the same threshold for the purpose of assessing progress towards UNAIDS targets. We have proposed a simple adjustment that allows this, and this has been incorporated into UNAIDS modelling software.

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