Quality assessment of prevalence studies: a systematic review
Open accessAbstract
Objectives The objective of the study is to identify items and domains applicable for the quality assessment of prevalence studies. Study design and setting We searched databases and the gray literature to identify tools or guides about the quality assessment of prevalence studies. After study selection, we abstracted questions applicable for prevalence studies and classified into at least one of the following domains: 'population and setting', 'condition measurement', 'statistics', and 'other'. PROSPERO registration:CRD42018088437. Results We included 30 tools: eight (26.7%) specifically designed to appraise prevalence studies and 22 (73.3%) adaptable for this purpose. We identified 12 unique items in the domain "population and setting", 16 in the domain "condition measurement", and 14 in the domain "statistics". Of those, 25 (59.5%) were identified in the eight specific tools. Regarding the domain "other", we identified 77 unique items, mainly related to manuscript writing and reporting (n = 48, 62.3%); of those, 24 (31.2%) were identified in the eight specific tools and 53 (68.8%) in the additional 22 nonspecific tools. Conclusion We provide a comprehensive set of items classified by domains that can guide the appraisal of prevalence studies, conduction of primary prevalence studies, and update or development of tools to evaluate prevalence studies.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Study quality was assessed with the Joanna Briggs Institute prevalence critical appraisal tool, with two reviewers independently conducting assessment and discrepancies resolved with a third reviewer.
“Two reviewers independently conducted the quality assessment using the JBI tool, with any discrepancies resolved through discussion with a third reviewer.”
From Adherence to PCSK9 Inhibitors in Clinical Practice (2026) · cites this paper as [20]
Cites
RoB 2: a revised tool for assessing risk of bias in randomised trials
Sterne, J. A. C., Savović, J., Page, M. J., Elbers, R. G., Blencowe, N. S., Boutron, I., Cates, C. J., Cheng, H.-Y., Corbett, M. S., Eldridge, S. M., et al. (2019) · BMJ · doi:10.1136/bmj.l4898 · Cited by 33,583 · see citing works
ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions
Sterne, J. A., Hernán, M. A., Reeves, B. C., Savović, J., Berkman, N. D., Viswanathan, M., Henry, D., Altman, D. G., Ansari, M. T., Boutron, I., et al. (2016) · BMJ · doi:10.1136/bmj.i4919 · Cited by 20,329 · see citing works
Downs, S. H., & Black, N. (1998) · Journal of Epidemiology and Community Health · doi:10.1136/jech.52.6.377 · Cited by 8,252 · see citing works
Development of a critical appraisal tool to assess the quality of cross-sectional studies (AXIS)
Downes, M. J., Brennan, M. L., Williams, H. C., & Dean, R. S. (2016) · BMJ Open · doi:10.1136/bmjopen-2016-011458 · Cited by 2,376 · see citing works
Standard Quality Assessment Criteria for Evaluating Primary Research Papers from a Variety of Fields
Kmet, L. M., Cook, L. S., & Lee, R. C. (2004) · University of Alberta Library · doi:10.7939/r37m04f16 · Cited by 1,682 · see citing works
Munn, Z., Moola, S., Riitano, D., & Lisy, K. (2014) · International Journal of Health Policy and Management · doi:10.15171/ijhpm.2014.71 · Cited by 1,479 · see citing works
Self-citationWong, W. C., Cheung, C. S., & Hart, G. J. (2008) · Emerging Themes in Epidemiology · doi:10.1186/1742-7622-5-23 · Cited by 257 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
The prevalence of loneliness across 113 countries: systematic review and meta-analysis
Surkalim, D. L., Luo, M., Eres, R., Gebel, K., van Buskirk, J., Bauman, A., & Ding, D. (2022) · BMJ · doi:10.1136/bmj-2021-067068 · Cited by 821 · see citing works
Lewis, H., Ware, H., Whelan, M., Subissi, L., Li, Z., Ma, X., Nardone, A., Valenciano, M., Cheng, B., Noel, K., et al. (2022) · doi:10.1101/2022.02.14.22270934 · Cited by 177 · see citing works
Harrop, E. N., Mensinger, J. L., Moore, M., & Lindhorst, T. (2021) · International Journal of Eating Disorders · doi:10.1002/eat.23519 · Cited by 148 · see citing works
Smythe, K. L., Petersen, I., & Schartau, P. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.18969 · Cited by 147 · see citing works
Kieselbach, B., Kimber, M., MacMillan, H. L., & Perneger, T. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-051140 · Cited by 36 · see citing works
Adherence to PCSK9 Inhibitors in Clinical Practice
Blais, J. E., Zhong, W., Li, C. Y. V., Kongkaew, C., & Chan, A. H. Y. (2026) · JACC: Advances · doi:10.1016/j.jacadv.2026.102733 · Cited by 7 · see citing works
- Authors
- Migliavaca, C. B., Stein, C., Colpani, V., Munn, Z., & Falavigna, M.
- Affiliations
- Universidade Federal do Rio Grande do Sul (BR)
- Hospital Moinhos de Vento (BR)
- The University of Adelaide (AU)
- Corresponding author
- Migliavaca, Celina Borges
- Venue
- Journal of Clinical Epidemiology
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 127
- Pages
- 59-68
- ISSN
- 0895-4356 (ISSN-L)
- OpenAlex
- W3042819477
- Cited by
- 253
- References
- 7
- Citations by year
- 2026: 47 · 2025: 49 · 2024: 51 · 2023: 46 · 2022: 38 · 2021: 19 · 2020: 2
- FWCI
- 5.44
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Clinical Epidemiology (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Education › Health Education and Validation
- Keywords
- prevalence studies, quality assessment, quality assessment tools, reporting quality, manuscript preparation, reporting guidelines
- MeSH terms
- Cross-Sectional Studies, Bias
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
