Considerations for viral co-infection studies in human populations.

Autor:	Chin T; Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA., Foxman EF; Department of Laboratory Medicine, Yale University School of Medicine, New Haven, Connecticut, USA.; Department of Immunobiology, Yale University School of Medicine, New Haven, Connecticut, USA., Watkins TA; Department of Laboratory Medicine, Yale University School of Medicine, New Haven, Connecticut, USA.; Department of Immunobiology, Yale University School of Medicine, New Haven, Connecticut, USA., Lipsitch M; Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Jazyk:	angličtina
Zdroj:	MBio [mBio] 2024 Jul 17; Vol. 15 (7), pp. e0065824. Date of Electronic Publication: 2024 Jun 07.
DOI:	10.1128/mbio.00658-24
Abstrakt:	When respiratory viruses co-circulate in a population, individuals may be infected with multiple pathogens and experience possible virus-virus interactions, where concurrent or recent prior infection with one virus affects the infection process of another virus. While experimental studies have provided convincing evidence for within-host mechanisms of virus-virus interactions, evaluating evidence for viral interference or potentiation using population-level data has proven more difficult. Recent studies have quantified the prevalence of co-detections using populations drawn from clinical settings. Here, we focus on selection bias issues associated with this study design. We provide a quantitative account of the conditions under which selection bias arises in these studies, review previous attempts to address this bias, and propose unbiased study designs with sample size estimates needed to ascertain viral interference. We show that selection bias is expected in cross-sectional co-detection prevalence studies conducted in clinical settings, except under a strict set of assumptions regarding the relative probabilities of being included in a study limited to individuals with clinical disease under different viral states. Population-wide studies that collect samples from participants irrespective of their clinical status would meanwhile require large sample sizes to be sufficiently powered to detect viral interference, suggesting that a study's timing, inclusion criteria, and the expected magnitude of interference are instrumental in determining feasibility. Competing Interests: The authors declare no conflict of interest.
Databáze:	MEDLINE
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