Harmonizing HIV-1 RNA laboratory measurements in a longitudinal cohort collaboration.
| Title: | Harmonizing HIV-1 RNA laboratory measurements in a longitudinal cohort collaboration. |
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| Authors: | Lee JS; Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, United States.; Humes E; Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, United States.; Haw NJL; Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, United States.; Hogan B; Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, United States.; Zheng C; Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, United States.; Coburn S; Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, United States.; Lesko CR; Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, United States.; Lang R; Department of Medicine, University of Calgary, Calgary, Alberta, Canada.; Gill MJ; Department of Medicine, University of Calgary, Calgary, Alberta, Canada.; Horberg M; Kaiser Permanente Mid-Atlantic Medical Group and Research Institute, Washington DC, United States.; Silverberg MJ; Division of Research, Kaiser Permanente Northern California, Pleasanton, California, United States.; Palella FJ; Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States.; Delaney JA; Department of Medicine, University of Washington, Seattle, WA, United States.; Rebeiro PF; Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States.; Sterling T; Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States.; Rodriguez-Barradas M; Infectious Diseases Section, Michael E. De Bakey VAMC and Baylor College of Medicine, Houston, Texas, United States.; Marconi VC; Division of Infectious Diseases, Department of Medicine, Emory University School of Medicine and Global Health, Rollins School of Public Health, Atlanta, USA.; Yendewa GA; Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States.; Klein MB; Department of Medicine, McGill University, Montreal, Quebec, Canada.; Moore R; Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, United States.; Althoff KN; Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, United States.; Research And Design Na-Accord OBOTNAACCO |
| Source: | American journal of epidemiology [Am J Epidemiol] 2026 Mar 20. Date of Electronic Publication: 2026 Mar 20. |
| Publication Model: | Ahead of Print |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Oxford University Press Country of Publication: United States NLM ID: 7910653 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1476-6256 (Electronic) Linking ISSN: 00029262 NLM ISO Abbreviation: Am J Epidemiol Subsets: MEDLINE |
| Imprint Name(s): | Publication: Cary, NC : Oxford University Press; Original Publication: Baltimore, School of Hygiene and Public Health of Johns Hopkins Univ. |
| Abstract: | Plasma HIV-1 RNA viral loads (VL) are measured via laboratory assays with changing lower limits of quantification over time. We described an approach to produce an analytic-ready dataset of VLs over time and across longitudinal cohorts of adults. A three-step approach was used: 1) initial data cleaning; 2) data checking with visualization; and 3) final data cleaning. Assumptions, data-driven decisions, and information from cohort-specific data managers produce an analytic-ready dataset of VLs with minimal missing data for date of blood draw, HIV-1 RNA result (copies/mL), below the lower limit of quantification (BLLQ) indicator, and the lower limit of quantification (LLQ). Among 3 663 786 VLs from 186 990 participants (median number of VLs per participant = 12, interquartile range 4-27) measured from 1988 to 2021, 61% of VL records were harmonized via the three-step approach. The proportion of VLs below the lower limit of quantification increased from 39% to 60% after application of this approach. Changes to LLQ, VL result, and BLLQ indicator variables were made to 45%, 36%, and 22% of VLs, respectively. Stated assumptions, visualized data distributions, and a documented approach to preparing an analytic-ready dataset of pooled individual-level longitudinal data revealed data idiosyncrasies, informed assumptions, and improved the data for research inference.; (© The Author(s) 2026. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved.) |
| Grant Information: | U01 AI069918 United States AI NIAID NIH HHS |
| Contributed Indexing: | Keywords: HIV; biomarkers; electronic health records; longitudinal data; lower limit of quantification |
| Entry Date(s): | Date Created: 20260320 Latest Revision: 20260417 |
| Update Code: | 20260417 |
| PubMed Central ID: | PMC13084633 |
| DOI: | 10.1093/aje/kwag056 |
| PMID: | 41858287 |
| Database: | MEDLINE |
Journal Article