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The revised JBI critical appraisal tool for the assessment of risk of bias for analytical cross-sectional studies

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dc.contributor.authorBarker, T.H.
dc.contributor.authorHasanoff, S.
dc.contributor.authorAromataris, E.
dc.contributor.authorStone, J.C.
dc.contributor.authorLeonardi-Bee, J.
dc.contributor.authorSears, K.
dc.contributor.authorKlugar, M.
dc.contributor.authorTufanaru, C.
dc.contributor.authorMoola, S.
dc.contributor.authorLiu, X.-L.
dc.contributor.authorMunn, Z.
dc.date.issued2025
dc.descriptionPublished online: June 10, 2025. OnlinePubl
dc.description.abstractCross-sectional studies are a useful observational study design that provides a snapshot of a population’s health status at a specific moment in time. Analytical cross-sectional studies are often included in systematic reviews investigating the etiology or risk of diseases, and descriptive cross-sectional studies are often used to determine the prevalence of a disease. As required of all studies that meet eligibility criteria for a systematic review, analytical cross-sectional studies should be subjected to appropriate critical appraisal of their methodological quality to determine the risk of bias. The JBI Effectiveness Methodology Group is currently undertaking a comprehensive revision of the entire suite of JBI critical appraisal tools to align with recent advances in risk of bias assessment. This paper presents the revised critical appraisal tool for risk of bias assessment of analytical cross-sectional studies. Applying tools such as the revised JBI tools within systematic reviews allows for end users to make informed decisions using the evidence. We discuss major changes from the previous iterations of this tool and justify these changes within the context of the broader advancements to risk-of-bias assessment science. We also offer practical guidance for the use of this revised tool, and provide examples for interpreting the results of risk-of-bias assessment for analytical cross-sectional studies to support reviewers including these studies in their systematic reviews.
dc.description.statementofresponsibilityTimothy H. Barker, Sabira Hasanoff, Edoardo Aromataris, Jennifer C. Stone, Jo Leonardi-Bee, Kim Sears, Miloslav Klugar, Catalin Tufanaru, Sandeep Moola, Xian-Liang Liu, Zachary Munn
dc.identifier.citationJBI evidence synthesis, 2025; 24(3):1-8
dc.identifier.doi10.11124/JBIES-24-00523
dc.identifier.issn2689-8381
dc.identifier.issn2689-8381
dc.identifier.orcidBarker, T.H. [0000-0002-6897-814X]
dc.identifier.orcidHasanoff, S. [0000-0001-7246-0485]
dc.identifier.orcidAromataris, E. [0000-0001-7238-5833]
dc.identifier.orcidStone, J.C. [0000-0002-3787-6175] [0000-0002-7848-1401]
dc.identifier.orcidKlugar, M. [0000-0002-2804-7295]
dc.identifier.orcidMoola, S. [0000-0002-1266-7246]
dc.identifier.orcidMunn, Z. [0000-0002-7091-5842]
dc.identifier.urihttps://hdl.handle.net/2440/147877
dc.language.isoen
dc.publisherLippincott, Williams & Wilkins
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1195676
dc.rights© 2025 JBI
dc.source.urihttps://doi.org/10.11124/jbies-24-00523
dc.subjectbias assessment; cross-sectional studies; methodology; risk of bias; systematic review
dc.titleThe revised JBI critical appraisal tool for the assessment of risk of bias for analytical cross-sectional studies
dc.typeJournal article
pubs.publication-statusPublished

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