Heather Bradley1, Keri N Althoff2, Kate Buchacz3, John T Brooks3, M John Gill4, Michael A Horberg5, Mari M Kitahata6, Vincent Marconi7, Kenneth H Mayer8, Angel Mayor9, Richard Moore10, Michael Mugavero11, Sonia Napravnik12, Gabriela Paz-Bailey3, Joseph Prejean3, Peter F Rebeiro13, Christopher T Rentsch14, R Luke Shouse3, Michael J Silverberg15, Patrick S Sullivan16, Jennifer E Thorne2, Baligh Yehia10, Eli S Rosenberg17. 1. Division of HIV/AIDS Prevention, Centers for Disease Control and Prevention, Atlanta, GA. Electronic address: hbradley@gsu.edu. 2. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD. 3. Division of HIV/AIDS Prevention, Centers for Disease Control and Prevention, Atlanta, GA. 4. University of Calgary, Alberta, Canada. 5. Kaiser Permanente Mid-Atlantic States, Mid-Atlantic Permanente Research Institute, Rockville, MD. 6. Department of Medicine, University of Washington School of Medicine, Seattle, WA. 7. Atlanta VA Medical Center, Atlanta, GA; Emory University School of Medicine, Atlanta, GA; Department of Global Health, Emory University Rollins School of Public Health, Atlanta, GA. 8. Fenway Health, Boston, MA. 9. Department of Internal Medicine, School of Medicine, Universidad Central del Caribe, Bayamon, PR. 10. Department of Medicine, Johns Hopkins University, Baltimore, MD. 11. Division of Infectious Diseases, University of Alabama Birmingham, Birmingham. 12. Division of Infectious Diseases, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC. 13. Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN. 14. Yale School of Medicine, New Haven, CT; VA Connecticut Healthcare System, West Haven, CT; London School of Hygiene & Tropical Medicine, Faculty of Epidemiology and Population Health, London, UK. 15. Kaiser Permanente Northern California, Oakland, CA. 16. Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA. 17. SUNY, Department of Epidemiology and Biostatistics, University at Albany School of Public Health, Rensselaer, NY.
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. Published by Elsevier Inc.
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. Published by Elsevier Inc.
Entities:
Keywords:
HIV clinical care; HIV viral suppression; Indirect standardization; Surveillance
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