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

Heather Bradley; Keri N Althoff; Kate Buchacz; John T Brooks; M John Gill; Michael A Horberg; Mari M Kitahata; Vincent Marconi; Kenneth H Mayer; Angel Mayor; +13 more... Richard Moore; Michael Mugavero; Sonia Napravnik; Gabriela Paz-Bailey; Joseph Prejean; Peter F Rebeiro; Christopher T Rentsch ORCID logo; R Luke Shouse; Michael J Silverberg; Patrick S Sullivan; Jennifer E Thorne; Baligh Yehia; Eli S Rosenberg; (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: 10.1016/j.annepidem.2018.11.005
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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.


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