Cohort studies are a robust analytical observational study design that explore the difference in outcomes between two cohorts, differentiated by their exposure status. Despite being observational in nature, they are often included in systematic reviews of effectiveness, particularly when randomized controlled trials are limited or not feasible. Like all studies included in a systematic review, cohort studies must undergo a critical appraisal process to assess the extent to which a study has considered potential bias in its design, conduct, or analysis. Critical appraisal tools facilitate this evaluation. This paper introduces the revised critical appraisal tool for cohort studies, completed by the JBI Effectiveness Methodology Group, who are currently revising the suite of JBI critical appraisal tools for quantitative study designs. The revised tool responds to updates in methodological guidance from the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) Working Group and reporting guidance from PRISMA 2020, providing a robust framework for evaluating risk of bias in a cohort study. Transparent and rigorous assessment using this tool will assist reviewers in understanding the validity and relevance of the results and conclusions drawn from a systematic review that includes cohort studies. This may contribute to better evidence-based decision-making in health care. This paper discusses the key changes made to the tool, outlines justifications for these changes, and provides practical guidance on how this tool should be interpreted and applied by systematic reviewers.

Center for Evidence Based Education and Arts Therapies A JBI Affiliated Group Faculty of Education Palacký University Olomouc Olomouc Czech Republic

Centre for Health Informatics Australian Institute of Health Innovation Macquarie University Sydney NSW Australia

Czech National Centre for Evidence Based Healthcare and Knowledge Translation Faculty of Medicine Institute of Biostatistics and Analyses Masaryk University Brno Czech Republic

Health Economics and Value Assessment Sanofi Healthcare India Pvt Ltd India

Health Evidence Synthesis Recommendations and Impact School of Public Health The University of Adelaide Adelaide SA Australia

JBI Faculty of Health and Medical Sciences The University of Adelaide Adelaide SA Australia

Queen's Collaboration for Health Care Quality A JBI Centre of Excellence Queen's University Kingston Ontario Canada

School of Nursing and Health Studies Hong Kong Metropolitan University Homantin Kowloon Hong Kong SAR China

The Nottingham Centre for Evidence Based Healthcare A JBI Centre of Excellence School of Medicine University of Nottingham Nottingham UK

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Munn Z, Barker TH, Moola S, Tufanaru C, Stern C, McArthur A, et al. Methodological quality of case series studies: an introduction to the JBI critical appraisal tool. JBI Evid Synth 2020;18(10):2127–2133.

Aromataris E, Munn Z, editors. JBI Manual for Evidence Synthesis [internet]. JBI; 2020 [cited 2024 Mar 3]. https://synthesismanual.jbi.global .

Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JPA, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ 2009;339:b2700.

Munn Z, Stern C, Aromataris E, Lockwood C, Jordan Z. What kind of systematic review should I conduct? A proposed typology and guidance for systematic reviewers in the medical and health sciences. BMC Med Res Methodol 2018;18(1):5.

Higgins JP, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al. Cochrane handbook for systematic reviews of interventions. John Wiley and Sons; 2019.

Glasofer A, Townsend AB. Determining the level of evidence: experimental research appraisal. Nurs Crit Care 2019;14(6):22–25.

Moola S, Munn Z, Sears K, Sfetcu R, Currie M, Lisy K, et al. Conducting systematic reviews of association (etiology): The Joanna Briggs Institute’s approach. JBI Evid Implement 2015;13(3):163–9.

Euser AM, Zoccali C, Jager KJ, Dekker FW. Cohort studies: prospective versus retrospective. Nephron Clin Pract 2009;113(3):c214–7.

Reeves B, Higgins J, Shea B, Tugwell P, Wells G. Chapter 24: including non-randomized studies on intervention effects. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA, editors. Cochrane Handbook for Systematic Reviews of Interventions [internet]. Cochrane; 2021 [cited 2024 Mar 3]. Available from: https://training.cochrane.org/handbook/current/chapter-24 .

Setia MS. Methodology series module 1: cohort studies. Indian J Dermatol 2016;61(1):21–5.

Moola S, Munn Z, Tufanaru C, Aromataris E, Sears K, Sfetcu R, et al. Chapter 7: Systematic reviews of etiology and risk. In: Aromataris E, Munn Z, editors. JBI Manual for Evidence Synthesis [internet]. JBI; 2020 [cited 2024 Mar 3]. Available from: https://synthesismanual.jbi.global .

Garibaldi BT, Fiksel J, Muschelli J, Robinson ML, Rouhizadeh M, Perin J, et al. Patient trajectories among persons hospitalized for COVID-19: a cohort study. Ann Intern Med 2021;174(1):33–41.

Muntner P, Whittle J, Lynch AI, Colantonio LD, Simpson LM, Einhorn PT, et al. Visit-to-visit variability of blood pressure and coronary heart disease, stroke, heart failure, and mortality: a cohort study. Ann Intern Med 2015;163(5):329–38.

Terry PD, Miller AB, Rohan TE. A prospective cohort study of cigarette smoking and the risk of endometrial cancer. Br J Cancer 2002;86(9):1430–5.

Song JW, Chung KC. Observational studies: cohort and case-control studies. Plast Reconstr Surg 2010;126(6):2234–42.

Merrill RM. Introduction to epidemiology, 7th ed. Jones & Bartlett Learning; 2017.

Korda RJ, Clements MS, Armstrong BK, Law HD, Guiver T, Anderson PR, et al. Risk of cancer associated with residential exposure to asbestos insulation: a whole-population cohort study. Lancet Pub Health 2017;2(11):e522–8.

Che L, Yu J, Bai X, Wang Y, Zhang Y, Xu L, et al. Association between post-COVID-19 status and perioperative morbidity and mortality: protocol for an ambispective cohort study. BMJ Open 2023;13(9):e074337.

Averis A, Pearson A. Filling the gaps: identifying nursing research priorities through the analysis of completed systematic reviews. JBI Rep 2003;1(3):49–126.

Stone JC, Glass K, Clark J, Munn Z, Tugwell P, Doi SAR. A unified framework for bias assessment in clinical research. Int J Evid Based Healthc 2019;17(2):106–20.

Sterne JAC, Hernán MA, Reeves BC, Savović J, Berkman ND, Viswanathan M, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ 2016;355:i4919.

Barker TH, Stone JC, Sears K, Klugar M, Leonardi-Bee J, Tufanaru C, et al. Revising the JBI quantitative critical appraisal tools to improve their applicability: an overview of methods and the development process. JBI Evid Synth 2023;21(3):478–93.

Sterne JAC, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ 2019;366:l4898.

Stone JC, Gurunathan U, Aromataris E, Glass K, Tugwell P, Munn Z, et al. Bias assessment in outcomes research: the role of relative versus absolute approaches. Value Health 2021;24(8):1145–9.

Page MJ, Moher D, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ 2021;372:n160.

Atkins D, Best D, Briss PA, Eccles M, Falck-Ytter Y, Flottorp S, et al. Grading quality of evidence and strength of recommendations. BMJ 2004;328(7454):1490.

Barker TH, Stone JC, Sears K, Klugar M, Tufanaru C, Leonardi-Bee J, et al. The revised JBI critical appraisal tool for the assessment of risk of bias for randomized controlled trials. JBI Evid Synth 2023;21(3):494–506.

Porritt K, Gomersall J, Lockwood C. JBI’s systematic reviews: study selection and critical appraisal. Am J Nurs 2014;114(6).

Barker TH, Habibi N, Aromataris E, Stone JC, Leonardi-Bee J, Sears K, et al. The revised JBI critical appraisal tool for the assessment of risk of bias for quasi-experimental studies. JBI Evid Synth 2024;22(3):378–88.

Nohr EA, Liew Z. How to investigate and adjust for selection bias in cohort studies. Acta Obst Gynecol Scand 2018;97(4):407–16.

Howe CJ, Cole SR, Lau B, Napravnik S, Eron JJ Jr. Selection bias due to loss to follow up in cohort studies. Epidemiology 2016;27(1):91–7.

Piedvache A, van Buuren S, Barros H, Ribeiro AI, Draper E, Zeitlin J, et al. Strategies for assessing the impact of loss to follow-up on estimates of neurodevelopmental impairment in a very preterm cohort at 2 years of age. BMC Med Res Methodol 2021;21(1):118.