The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Development of a quality assessment tool for systematic reviews of observational studies (QATSO) of HIV prevalence in men having sex with men and associated risk behaviours
Open accessAbstract
Background: Systematic reviews based on the critical appraisal of observational and analytic studies on HIV prevalence and risk factors for HIV transmission among men having sex with men are very useful for health care decisions and planning. Such appraisal is particularly difficult, however, as the quality assessment tools available for use with observational and analytic studies are poorly established. Methods: We reviewed the existing quality assessment tools for systematic reviews of observational studies and developed a concise quality assessment checklist to help standardise decisions regarding the quality of studies, with careful consideration of issues such as external and internal validity. Results: A pilot version of the checklist was developed based on epidemiological principles, reviews of study designs, and existing checklists for the assessment of observational studies. The Quality Assessment Tool for Systematic Reviews of Observational Studies (QATSO) Score consists of five items: External validity (1 item), reporting (2 items), bias (1 item) and confounding factors (1 item). Expert opinions were sought and it was tested on manuscripts that fulfil the inclusion criteria of a systematic review. Like all assessment scales, QATSO may oversimplify and generalise information yet it is inclusive, simple and practical to use, and allows comparability between papers. Conclusion: A specific tool that allows researchers to appraise and guide study quality of observational studies is developed and can be modified for similar studies in the future.
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“The need for complete reporting of all relevant methodological aspects as a prerequisite to assessing the risk of bias of studies was highlighted in 10 articles, [16, 33, 35, 47, 50, 56, 86, 87, 90, 91] and was the focus of recommendations in a publication from the Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group [12].”
From Methods to systematically review and meta-analyse observational studies: a systematic scoping review of recommendations (2018) · cites this paper as [86]
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von Elm, E., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., Vandenbroucke, J. P., & Initiative, F. T. S. (2007) · PLoS Medicine · doi:10.1371/journal.pmed.0040296 · Cited by 23,020
Meta-analysis of Observational Studies in Epidemiology<SUBTITLE>A Proposal for Reporting</SUBTITLE>
Stroup, D. F. (2000) · JAMA · doi:10.1001/jama.283.15.2008 · Cited by 20,623
von Elm, E., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., Vandenbroucke, J. P., & Initiative, F. T. S. (2007) · Annals of Internal Medicine · doi:10.7326/0003-4819-147-8-200710160-00010 · Cited by 17,796
(2014) · Medical Entomology and Zoology · Cited by 17,633
Downs, S. H., & Black, N. (1998) · Journal of Epidemiology and Community Health · doi:10.1136/jech.52.6.377 · Cited by 8,252
Moher, D., Cook, D. J., Eastwood, S., Olkin, I., Rennie, D., & Stroup, D. F. (1999) · The Lancet · doi:10.1016/s0140-6736(99)04149-5 · Cited by 4,826
Evaluating non-randomised intervention studies
Deeks, J., Dinnes, J., D'Amico, R., Sowden, A., Sakarovitch, C., Song, F., Petticrew, M., & Altman, D. (2003) · Health Technology Assessment · doi:10.3310/hta7270 · Cited by 2,907
Systematic Reviews in Health Care: Meta‐analysis in Context
Pittler, M. (2010) · Focus on Alternative and Complementary Therapies · doi:10.1111/j.2042-7166.2002.tb05479.x · Cited by 1,639
Sanderson, S., Tatt, I. D., & Higgins, J. P. (2007) · International Journal of Epidemiology · doi:10.1093/ije/dym018 · Cited by 1,531
Systems to rate the strength of scientific evidence.
West, S. L., King, V., Carey, T. S., Lohr, K. N., Mckoy, N. A., Sutton, S. F., & Lux, L. J. (2002) · PubMed Central · Cited by 873
Research works printed but not linked: 3
- Altman D, Egger M, Pocock S, Vandenbrouke JP, von Elm E: Strengthening the reporting of observational epidemiological studies. STROBE Statement: Checklist of Essential Items Version 3. 2005 [http://www.strobe-statement.org/Checklist.html]
- Deeks J, Glanville J, Sheldon T: Undertaking Systematic Reviews of Effectiveness: CRD Guidelines for Those Carrying Out or Commissioning Reviews. York, England: NHS Centre for Reviews and Dissemination, University of York; 1996, CRD Report 4
- The Cochrane Collaborative Review Group on HIV Infection and AIDS: Editorial Policy: Inclusion and Appraisal of Experimental and Non-experimental (Observational) Studies. [http://www.igh.org/Cochrane]
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Shea, B. J., Reeves, B. C., Wells, G., Thuku, M., Hamel, C., Moran, J., Moher, D., Tugwell, P., Welch, V., Kristjansson, E., et al. (2017) · BMJ · doi:10.1136/bmj.j4008 · Cited by 10,636
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
Pace, R., Pluye, P., Bartlett, G., Macaulay, A. C., Salsberg, J., Jagosh, J., & Seller, R. (2012) · International Journal of Nursing Studies · doi:10.1016/j.ijnurstu.2011.07.002 · Cited by 1,331
Baumeister, D., Akhtar, R., Ciufolini, S., Pariante, C. M., & Mondelli, V (2016) · Molecular Psychiatry · doi:10.1038/mp.2015.67 · Cited by 1,084
Popova, S., Lange, S., Probst, C., Gmel, G., & Rehm, J. (2017) · The Lancet Global Health · doi:10.1016/s2214-109x(17)30021-9 · Cited by 937
Metadata
- Authors
- Wong, W. C., Cheung, C. S., & Hart, G. J.
- Affiliations
- The University of Melbourne (AU)
- Chinese University of Hong Kong (HK)
- Mortimer Market Centre (GB)
- Corresponding author
- Wong, William CW
- Venue
- Emerging Themes in Epidemiology
- Published
- 2008-11-17
- Volume
- 5
- Issue
- 1
- Pages
- 23-23
- ISSN
- 1742-7622 (ISSN-L)
- PMC
- PMC2603000
- OpenAlex
- W2161763362
- Cited by
- 257
- References
- 15
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 12 · 2023: 15 · 2022: 13 · 2021: 18 · 2020: 22 · 2019: 15 · 2018: 16 · 2017: 28 · 2016: 22 · 2015: 24 · 2014: 17 · 2013: 19 · 2012: 19
- FWCI
- 0.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Emerging Themes in Epidemiology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- Open Scholarship Institutional Repository (Washington University in St. Louis) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- The HKU Scholars Hub (University of Hong Kong) (submittedVersion)
- Funders
- Chinese University of Hong Kong
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Health Sciences › Medicine › Infectious Diseases › HIV/AIDS Research and Interventions
- Keywords
- quality assessment tools, systematic reviews, observational studies, HIV prevalence, men who have sex with men, HIV risk behavior, generalizability, internal validity, reporting quality, bias, confounding, critical appraisal
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