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Author Notes:

Correspondence: hbradley@gsu.edu

The authors would like to thank members of the scientific and public health advisory group of the Coalition for Applied Modeling for Prevention project for their input on this study, and specifically those members who reviewed a previous version of this manuscript: Mary Ann Chiasson, David Dowdy, Gregory Felzien, and Jane Kelly.


Research Funding:

This work was supported by the National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention Epidemiologic and Economic Modeling Agreement (NEEMA) at the Centers for Disease Control and Prevention [grant: U38 PS004646], the National Institutes of Health [grants: R01 HD068395, R21 HD075662, R24 HD042828], and the Emory Center for AIDS Research [grant: P30 AI050409].

This work was also supported by National Institutes of Health grants U01AI069918, F31AI124794, F31DA037788, G12MD007583, K01AI093197, K01AI131895, K23EY013707, K24AI065298, K24AI118591, K24DA000432, KL2TR000421, M01RR000052, N01CP01004,N02CP055504, N02CP91027, P30AI027757, P30AI027763, P30AI027767, P30AI036219, P30AI050410, P30AI094189, P30AI110527, P30MH62246, R01AA016893, R01CA165937, R01DA011602, R01DA012568, R01 AG053100, R24AI067039, U01AA013566, U01AA020790, U01AI031834, U01AI034989, U01AI034993, U01AI034994, U01AI035004, U01AI035039, U01AI035040, U01AI035041, U01AI035042, U01AI037613, U01AI037984, U01AI038855, U01AI038858, U01AI042590, U01AI068634, U01AI068636, U01AI069432, U01AI069434, U01AI103390, U01AI103397, U01AI103401, U01AI103408, U01DA03629, U01DA036935, U01HD032632, U10EY008057, U10EY008052, U10EY008067, U24AA020794,U54MD007587, UL1RR024131, UL1TR000004, UL1TR000083, UL1TR000454, UM1AI035043, Z01CP010214 and Z01CP010176

Contracts CDC-200–2006-18797 and CDC-200–2015-63931 from the Centers for Disease Control and Prevention, USA; contract 90047713 from the Agency for Healthcare Research and Quality, USA; contract 90051652 from the Health Resources and Services Administration, USA; grants CBR-86906, CBR-94036, HCP-97105 and TGF-96118 from the Canadian Institutes of Health Research, Canada

Ontario Ministry of Health and Long Term Care; and the Government of Alberta, Canada. Additional support was provided by the National Cancer Institute, National Institute for Mental Health and National Institute on Drug Abuse.


  • Science & Technology
  • Life Sciences & Biomedicine
  • Public, Environmental & Occupational Health
  • HIV viral suppression
  • HIV clinical care
  • Surveillance
  • Indirect standardization
  • Antiretroviral therapy
  • Infection

Viral suppression among persons in HIV care in the United States during 2009-2013: sampling bias in Medical Monitoring Project surveillance estimates

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Journal Title:

Annals of Epidemiology


Volume 31


, Pages 3-7

Type of Work:

Article | Post-print: After Peer Review


Purpose: To assess sampling bias in national viral suppression (VS) estimates derived from the Medical Monitoring Project (MMP) resulting from use of an abbreviated (four-month) annual sampling period. We aimed to improve VS estimates using cohort data from the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) and a novel cohort-adjustment method. Methods: Using full calendar years of NA-ACCORD data, we assessed timing of HIV care attendance (inside vs. exclusively outside MMP's four-month sampling period), VS status at last test (<200 vs. ≥200 copies/mL), and associated demographics. These external estimates were used to standardize MMP to NA-ACCORD data with multivariable regression models of care attendance and VS, yielding adjusted 2009–2013 VS estimates with 95% confidence intervals. Results: Weighted percentages of VS among persons in HIV care were 67% in 2009 and 77% in 2013. These estimates are slightly lower than previously published MMP estimates (72% and 80% in 2009 and 2013, respectively). The number of persons receiving HIV care was previously underestimated by 20%, because patients receiving care exclusively outside the MMP sampling period did not contribute toward the weighted population estimate. Conclusions: Careful examination of national surveillance estimates using data triangulation and novel methodologies can improve the robustness of VS estimates.

Copyright information:

© 2019 Elsevier B.V.

This is an Open Access work distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/).
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