Bradley, Heather; Althoff, Keri N; Buchacz, Kate; Brooks, John T; Gill, M John; Horberg, Michael A; Kitahata, Mari M; Marconi, Vincent; Mayer, Kenneth H; Mayor, Angel; +13 more... Moore, Richard; Mugavero, Michael; Napravnik, Sonia; Paz-Bailey, Gabriela; Prejean, Joseph; Rebeiro, Peter F; Rentsch, Christopher T; Shouse, R Luke; Silverberg, Michael J; Sullivan, Patrick S; Thorne, Jennifer E; Yehia, Baligh; Rosenberg, Eli S; (2018) Viral suppression among persons in HIV care in the United States during 2009-2013: sampling bias in Medical Monitoring Project surveillance estimates. Annals of epidemiology, 31. pp. 3-7. ISSN 1047-2797 DOI: https://doi.org/10.1016/j.annepidem.2018.11.005
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Abstract
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.
Item Type | Article |
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Faculty and Department | Faculty of Epidemiology and Population Health > Dept of Non-Communicable Disease Epidemiology |
Research Centre | EHR Research Group |
PubMed ID | 30529086 |
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Filename: Bradley_MMP_NAACCORD_published.pdf
Licence: Creative Commons: Attribution-Noncommercial-No Derivative Works 3.0
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